pyarrow API Reference

Python Library Documentation: class Array in module pyarrow.lib

class Array(_PandasConvertible)
 |  Array()
 |
 |  The base class for all Arrow arrays.
 |
 |  Method resolution order:
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class ArrowCancelled in module pyarrow.lib

class ArrowCancelled(ArrowException)
 |  ArrowCancelled(message, signum=None)
 |
 |  Method resolution order:
 |      ArrowCancelled
 |      ArrowException
 |      builtins.Exception
 |      builtins.BaseException
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, message, signum=None)
 |      ArrowCancelled.__init__(self, message, signum=None)
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ArrowException:
 |
 |  __weakref__
 |      list of weak references to the object
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from builtins.Exception:
 |
 |  __new__(*args, **kwargs) class method of builtins.Exception
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.BaseException:
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__(...)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  add_note(...)
 |      Exception.add_note(note) --
 |      add a note to the exception
 |
 |  with_traceback(...)
 |      Exception.with_traceback(tb) --
 |      set self.__traceback__ to tb and return self.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.BaseException:
 |
 |  __cause__
 |      exception cause
 |
 |  __context__
 |      exception context
 |
 |  __dict__
 |
 |  __suppress_context__
 |
 |  __traceback__
 |
 |  args

Python Library Documentation: class ArrowCapacityError in module pyarrow.lib

class ArrowCapacityError(ArrowException)
 |  Method resolution order:
 |      ArrowCapacityError
 |      ArrowException
 |      builtins.Exception
 |      builtins.BaseException
 |      builtins.object
 |
 |  Data descriptors inherited from ArrowException:
 |
 |  __weakref__
 |      list of weak references to the object
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.Exception:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from builtins.Exception:
 |
 |  __new__(*args, **kwargs) class method of builtins.Exception
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.BaseException:
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__(...)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  add_note(...)
 |      Exception.add_note(note) --
 |      add a note to the exception
 |
 |  with_traceback(...)
 |      Exception.with_traceback(tb) --
 |      set self.__traceback__ to tb and return self.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.BaseException:
 |
 |  __cause__
 |      exception cause
 |
 |  __context__
 |      exception context
 |
 |  __dict__
 |
 |  __suppress_context__
 |
 |  __traceback__
 |
 |  args

Python Library Documentation: class ArrowException in module pyarrow.lib

class ArrowException(builtins.Exception)
 |  Method resolution order:
 |      ArrowException
 |      builtins.Exception
 |      builtins.BaseException
 |      builtins.object
 |
 |  Data descriptors defined here:
 |
 |  __weakref__
 |      list of weak references to the object
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.Exception:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from builtins.Exception:
 |
 |  __new__(*args, **kwargs) class method of builtins.Exception
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.BaseException:
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__(...)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  add_note(...)
 |      Exception.add_note(note) --
 |      add a note to the exception
 |
 |  with_traceback(...)
 |      Exception.with_traceback(tb) --
 |      set self.__traceback__ to tb and return self.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.BaseException:
 |
 |  __cause__
 |      exception cause
 |
 |  __context__
 |      exception context
 |
 |  __dict__
 |
 |  __suppress_context__
 |
 |  __traceback__
 |
 |  args

Python Library Documentation: class OSError in module builtins

class OSError(Exception)
 |  Base class for I/O related errors.
 |
 |  Method resolution order:
 |      OSError
 |      Exception
 |      BaseException
 |      object
 |
 |  Built-in subclasses:
 |      BlockingIOError
 |      ChildProcessError
 |      ConnectionError
 |      FileExistsError
 |      ... and 7 other subclasses
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  characters_written
 |
 |  errno
 |      POSIX exception code
 |
 |  filename
 |      exception filename
 |
 |  filename2
 |      second exception filename
 |
 |  strerror
 |      exception strerror
 |
 |  winerror
 |      Win32 exception code
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseException:
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__(...)
 |
 |  add_note(...)
 |      Exception.add_note(note) --
 |      add a note to the exception
 |
 |  with_traceback(...)
 |      Exception.with_traceback(tb) --
 |      set self.__traceback__ to tb and return self.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from BaseException:
 |
 |  __cause__
 |      exception cause
 |
 |  __context__
 |      exception context
 |
 |  __dict__
 |
 |  __suppress_context__
 |
 |  __traceback__
 |
 |  args

Python Library Documentation: class ArrowIndexError in module pyarrow.lib

class ArrowIndexError(builtins.IndexError, ArrowException)
 |  Method resolution order:
 |      ArrowIndexError
 |      builtins.IndexError
 |      builtins.LookupError
 |      ArrowException
 |      builtins.Exception
 |      builtins.BaseException
 |      builtins.object
 |
 |  Data descriptors defined here:
 |
 |  __weakref__
 |      list of weak references to the object
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.IndexError:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from builtins.IndexError:
 |
 |  __new__(*args, **kwargs) class method of builtins.IndexError
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.BaseException:
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__(...)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  add_note(...)
 |      Exception.add_note(note) --
 |      add a note to the exception
 |
 |  with_traceback(...)
 |      Exception.with_traceback(tb) --
 |      set self.__traceback__ to tb and return self.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.BaseException:
 |
 |  __cause__
 |      exception cause
 |
 |  __context__
 |      exception context
 |
 |  __dict__
 |
 |  __suppress_context__
 |
 |  __traceback__
 |
 |  args

Python Library Documentation: class ArrowInvalid in module pyarrow.lib

class ArrowInvalid(builtins.ValueError, ArrowException)
 |  Method resolution order:
 |      ArrowInvalid
 |      builtins.ValueError
 |      ArrowException
 |      builtins.Exception
 |      builtins.BaseException
 |      builtins.object
 |
 |  Data descriptors defined here:
 |
 |  __weakref__
 |      list of weak references to the object
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.ValueError:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from builtins.ValueError:
 |
 |  __new__(*args, **kwargs) class method of builtins.ValueError
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.BaseException:
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__(...)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  add_note(...)
 |      Exception.add_note(note) --
 |      add a note to the exception
 |
 |  with_traceback(...)
 |      Exception.with_traceback(tb) --
 |      set self.__traceback__ to tb and return self.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.BaseException:
 |
 |  __cause__
 |      exception cause
 |
 |  __context__
 |      exception context
 |
 |  __dict__
 |
 |  __suppress_context__
 |
 |  __traceback__
 |
 |  args

Python Library Documentation: class ArrowKeyError in module pyarrow.lib

class ArrowKeyError(builtins.KeyError, ArrowException)
 |  Method resolution order:
 |      ArrowKeyError
 |      builtins.KeyError
 |      builtins.LookupError
 |      ArrowException
 |      builtins.Exception
 |      builtins.BaseException
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __str__(self)
 |      ArrowKeyError.__str__(self)
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  __weakref__
 |      list of weak references to the object
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.KeyError:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from builtins.LookupError:
 |
 |  __new__(*args, **kwargs) class method of builtins.LookupError
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.BaseException:
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__(...)
 |
 |  add_note(...)
 |      Exception.add_note(note) --
 |      add a note to the exception
 |
 |  with_traceback(...)
 |      Exception.with_traceback(tb) --
 |      set self.__traceback__ to tb and return self.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.BaseException:
 |
 |  __cause__
 |      exception cause
 |
 |  __context__
 |      exception context
 |
 |  __dict__
 |
 |  __suppress_context__
 |
 |  __traceback__
 |
 |  args

Python Library Documentation: class ArrowMemoryError in module pyarrow.lib

class ArrowMemoryError(builtins.MemoryError, ArrowException)
 |  Method resolution order:
 |      ArrowMemoryError
 |      builtins.MemoryError
 |      ArrowException
 |      builtins.Exception
 |      builtins.BaseException
 |      builtins.object
 |
 |  Data descriptors defined here:
 |
 |  __weakref__
 |      list of weak references to the object
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.MemoryError:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from builtins.MemoryError:
 |
 |  __new__(*args, **kwargs) class method of builtins.MemoryError
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.BaseException:
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__(...)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  add_note(...)
 |      Exception.add_note(note) --
 |      add a note to the exception
 |
 |  with_traceback(...)
 |      Exception.with_traceback(tb) --
 |      set self.__traceback__ to tb and return self.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.BaseException:
 |
 |  __cause__
 |      exception cause
 |
 |  __context__
 |      exception context
 |
 |  __dict__
 |
 |  __suppress_context__
 |
 |  __traceback__
 |
 |  args

Python Library Documentation: class ArrowNotImplementedError in module pyarrow.lib

class ArrowNotImplementedError(builtins.NotImplementedError, ArrowException)
 |  Method resolution order:
 |      ArrowNotImplementedError
 |      builtins.NotImplementedError
 |      builtins.RuntimeError
 |      ArrowException
 |      builtins.Exception
 |      builtins.BaseException
 |      builtins.object
 |
 |  Data descriptors defined here:
 |
 |  __weakref__
 |      list of weak references to the object
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.NotImplementedError:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from builtins.NotImplementedError:
 |
 |  __new__(*args, **kwargs) class method of builtins.NotImplementedError
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.BaseException:
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__(...)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  add_note(...)
 |      Exception.add_note(note) --
 |      add a note to the exception
 |
 |  with_traceback(...)
 |      Exception.with_traceback(tb) --
 |      set self.__traceback__ to tb and return self.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.BaseException:
 |
 |  __cause__
 |      exception cause
 |
 |  __context__
 |      exception context
 |
 |  __dict__
 |
 |  __suppress_context__
 |
 |  __traceback__
 |
 |  args

Python Library Documentation: class ArrowSerializationError in module pyarrow.lib

class ArrowSerializationError(ArrowException)
 |  Method resolution order:
 |      ArrowSerializationError
 |      ArrowException
 |      builtins.Exception
 |      builtins.BaseException
 |      builtins.object
 |
 |  Data descriptors inherited from ArrowException:
 |
 |  __weakref__
 |      list of weak references to the object
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.Exception:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from builtins.Exception:
 |
 |  __new__(*args, **kwargs) class method of builtins.Exception
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.BaseException:
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__(...)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  add_note(...)
 |      Exception.add_note(note) --
 |      add a note to the exception
 |
 |  with_traceback(...)
 |      Exception.with_traceback(tb) --
 |      set self.__traceback__ to tb and return self.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.BaseException:
 |
 |  __cause__
 |      exception cause
 |
 |  __context__
 |      exception context
 |
 |  __dict__
 |
 |  __suppress_context__
 |
 |  __traceback__
 |
 |  args

Python Library Documentation: class ArrowTypeError in module pyarrow.lib

class ArrowTypeError(builtins.TypeError, ArrowException)
 |  Method resolution order:
 |      ArrowTypeError
 |      builtins.TypeError
 |      ArrowException
 |      builtins.Exception
 |      builtins.BaseException
 |      builtins.object
 |
 |  Data descriptors defined here:
 |
 |  __weakref__
 |      list of weak references to the object
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.TypeError:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from builtins.TypeError:
 |
 |  __new__(*args, **kwargs) class method of builtins.TypeError
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.BaseException:
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__(...)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  add_note(...)
 |      Exception.add_note(note) --
 |      add a note to the exception
 |
 |  with_traceback(...)
 |      Exception.with_traceback(tb) --
 |      set self.__traceback__ to tb and return self.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.BaseException:
 |
 |  __cause__
 |      exception cause
 |
 |  __context__
 |      exception context
 |
 |  __dict__
 |
 |  __suppress_context__
 |
 |  __traceback__
 |
 |  args

Python Library Documentation: class BaseExtensionType in module pyarrow.lib

class BaseExtensionType(DataType)
 |  Concrete base class for extension types.
 |
 |  Method resolution order:
 |      BaseExtensionType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_ext_class__(self)
 |      BaseExtensionType.__arrow_ext_class__(self)
 |
 |      The associated array extension class
 |
 |  __arrow_ext_scalar_class__(self)
 |      BaseExtensionType.__arrow_ext_scalar_class__(self)
 |
 |      The associated scalar class
 |
 |  wrap_array(self, storage)
 |      BaseExtensionType.wrap_array(self, storage)
 |
 |      Wrap the given storage array as an extension array.
 |
 |      Parameters
 |      ----------
 |      storage : Array or ChunkedArray
 |
 |      Returns
 |      -------
 |      array : Array or ChunkedArray
 |          Extension array wrapping the storage array
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  bit_width
 |      The bit width of the extension type.
 |
 |  byte_width
 |      The byte width of the extension type.
 |
 |  extension_name
 |      The extension type name.
 |
 |  storage_type
 |      The underlying storage type.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      DataType.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class BinaryArray in module pyarrow.lib

class BinaryArray(Array)
 |  Concrete class for Arrow arrays of variable-sized binary data type.
 |
 |  Method resolution order:
 |      BinaryArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  total_values_length
 |      The number of bytes from beginning to end of the data buffer addressed
 |      by the offsets of this BinaryArray.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class BinaryScalar in module pyarrow.lib

class BinaryScalar(Scalar)
 |  Concrete class for binary-like scalars.
 |
 |  Method resolution order:
 |      BinaryScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __buffer__(self, flags, /)
 |      Return a buffer object that exposes the underlying memory of the object.
 |
 |  __bytes__(self)
 |      BinaryScalar.__bytes__(self)
 |
 |  as_buffer(self)
 |      BinaryScalar.as_buffer(self)
 |
 |      Return a view over this value as a Buffer object.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      BinaryScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python bytes.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class BinaryViewArray in module pyarrow.lib

class BinaryViewArray(Array)
 |  Concrete class for Arrow arrays of variable-sized binary view data type.
 |
 |  Method resolution order:
 |      BinaryViewArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class BinaryViewScalar in module pyarrow.lib

class BinaryViewScalar(BinaryScalar)
 |  Method resolution order:
 |      BinaryViewScalar
 |      BinaryScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BinaryScalar:
 |
 |  __buffer__(self, flags, /)
 |      Return a buffer object that exposes the underlying memory of the object.
 |
 |  __bytes__(self)
 |      BinaryScalar.__bytes__(self)
 |
 |  as_buffer(self)
 |      BinaryScalar.as_buffer(self)
 |
 |      Return a view over this value as a Buffer object.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      BinaryScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python bytes.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Bool8Array in module pyarrow.lib

class Bool8Array(ExtensionArray)
 |  Concrete class for bool8 extension arrays.
 |
 |  Examples
 |  --------
 |  Define the extension type for an bool8 array
 |
 |  >>> import pyarrow as pa
 |  >>> bool8_type = pa.bool8()
 |
 |  Create an extension array
 |
 |  >>> arr = [-1, 0, 1, 2, None]
 |  >>> storage = pa.array(arr, pa.int8())
 |  >>> pa.ExtensionArray.from_storage(bool8_type, storage)
 |  <pyarrow.lib.Bool8Array object at ...>
 |  [
 |    -1,
 |    0,
 |    1,
 |    2,
 |    null
 |  ]
 |
 |  Method resolution order:
 |      Bool8Array
 |      ExtensionArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Bool8Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy bool view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for arrays without any nulls.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_numpy(obj)
 |      Bool8Array.from_numpy(obj)
 |
 |      Convert numpy array to a bool8 extension array without making a copy.
 |      The input array must be 1-dimensional, with either ``bool_`` or ``int8`` dtype.
 |
 |      Parameters
 |      ----------
 |      obj : numpy.ndarray
 |
 |      Returns
 |      -------
 |      bool8_array : Bool8Array
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> arr = np.array([True, False, True], dtype=np.bool_)
 |      >>> pa.Bool8Array.from_numpy(arr)
 |      <pyarrow.lib.Bool8Array object at ...>
 |      [
 |        1,
 |        0,
 |        1
 |      ]
 |
 |  from_storage(storage)
 |      Bool8Array.from_storage(Int8Array storage)
 |
 |      Construct Bool8Array from Int8Array storage.
 |
 |      Parameters
 |      ----------
 |      storage : Int8Array
 |          The underlying storage for the result array.
 |
 |      Returns
 |      -------
 |      bool8_array : Bool8Array
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ExtensionArray:
 |
 |  storage
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Bool8Scalar in module pyarrow.lib

class Bool8Scalar(ExtensionScalar)
 |  Concrete class for bool8 extension scalar.
 |
 |  Method resolution order:
 |      Bool8Scalar
 |      ExtensionScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Bool8Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this scalar as a Python object.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from ExtensionScalar:
 |
 |  from_storage(typ, value)
 |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
 |
 |      Construct ExtensionScalar from type and storage value.
 |
 |      Parameters
 |      ----------
 |      typ : DataType
 |          The extension type for the result scalar.
 |      value : object
 |          The storage value for the result scalar.
 |
 |      Returns
 |      -------
 |      ext_scalar : ExtensionScalar
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ExtensionScalar:
 |
 |  value
 |      Return storage value as a scalar.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Bool8Type in module pyarrow.lib

class Bool8Type(BaseExtensionType)
 |  Concrete class for bool8 extension type.
 |
 |  Bool8 is an alternate representation for boolean
 |  arrays using 8 bits instead of 1 bit per value. The underlying
 |  storage type is int8.
 |
 |  Examples
 |  --------
 |  Create an instance of bool8 extension type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.bool8()
 |  Bool8Type(extension<arrow.bool8>)
 |
 |  Method resolution order:
 |      Bool8Type
 |      BaseExtensionType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_ext_class__(self)
 |      Bool8Type.__arrow_ext_class__(self)
 |
 |  __arrow_ext_scalar_class__(self)
 |      Bool8Type.__arrow_ext_scalar_class__(self)
 |
 |  __reduce__(self)
 |      Bool8Type.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseExtensionType:
 |
 |  wrap_array(self, storage)
 |      BaseExtensionType.wrap_array(self, storage)
 |
 |      Wrap the given storage array as an extension array.
 |
 |      Parameters
 |      ----------
 |      storage : Array or ChunkedArray
 |
 |      Returns
 |      -------
 |      array : Array or ChunkedArray
 |          Extension array wrapping the storage array
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from BaseExtensionType:
 |
 |  bit_width
 |      The bit width of the extension type.
 |
 |  byte_width
 |      The byte width of the extension type.
 |
 |  extension_name
 |      The extension type name.
 |
 |  storage_type
 |      The underlying storage type.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class BooleanArray in module pyarrow.lib

class BooleanArray(Array)
 |  Concrete class for Arrow arrays of boolean data type.
 |
 |  Method resolution order:
 |      BooleanArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  false_count
 |
 |  true_count
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class BooleanScalar in module pyarrow.lib

class BooleanScalar(Scalar)
 |  Concrete class for boolean scalars.
 |
 |  Method resolution order:
 |      BooleanScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __bool__(self, /)
 |      True if self else False
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      BooleanScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python bool.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Buffer in module pyarrow.lib

class Buffer(_Weakrefable)
 |  Buffer()
 |
 |  The base class for all Arrow buffers.
 |
 |  A buffer represents a contiguous memory area.  Many buffers will own
 |  their memory, though not all of them do.
 |
 |  Method resolution order:
 |      Buffer
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __buffer__(self, flags, /)
 |      Return a buffer object that exposes the underlying memory of the object.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce_ex__(self, protocol)
 |      Buffer.__reduce_ex__(self, protocol)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  equals(self, other)
 |      Buffer.equals(self, Buffer other)
 |
 |      Determine if two buffers contain exactly the same data.
 |
 |      Parameters
 |      ----------
 |      other : Buffer
 |
 |      Returns
 |      -------
 |      are_equal : bool
 |          True if buffer contents and size are equal
 |
 |  hex(self)
 |      Buffer.hex(self)
 |
 |      Compute hexadecimal representation of the buffer.
 |
 |      Returns
 |      -------
 |      : bytes
 |
 |  slice(self, offset=0, length=None)
 |      Buffer.slice(self, offset=0, length=None)
 |
 |      Slice this buffer.  Memory is not copied.
 |
 |      You can also use the Python slice notation ``buffer[start:stop]``.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of buffer to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Buffer starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Buffer
 |          A logical view over this buffer.
 |
 |  to_pybytes(self)
 |      Buffer.to_pybytes(self)
 |
 |      Return this buffer as a Python bytes object. Memory is copied.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  address
 |      The buffer's address, as an integer.
 |
 |      The returned address may point to CPU or device memory.
 |      Use `is_cpu()` to disambiguate.
 |
 |  device
 |      The device where the buffer resides.
 |
 |      Returns
 |      -------
 |      Device
 |
 |  device_type
 |      The device type where the buffer resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the buffer is CPU-accessible.
 |
 |  is_mutable
 |      Whether the buffer is mutable.
 |
 |  memory_manager
 |      The memory manager associated with the buffer.
 |
 |      Returns
 |      -------
 |      MemoryManager
 |
 |  parent
 |
 |  size
 |      The buffer size in bytes.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class BufferOutputStream in module pyarrow.lib

class BufferOutputStream(NativeFile)
 |  An output stream that writes to a resizable buffer.
 |
 |  The buffer is produced as a result when ``getvalue()`` is called.
 |
 |  Examples
 |  --------
 |  Create an output stream, write data to it and finalize it with
 |  ``getvalue()``:
 |
 |  >>> import pyarrow as pa
 |  >>> f = pa.BufferOutputStream()
 |  >>> f.write(b'pyarrow.Buffer')
 |  14
 |  >>> f.closed
 |  False
 |  >>> f.getvalue()
 |  <pyarrow.Buffer address=... size=14 is_cpu=True is_mutable=True>
 |  >>> f.closed
 |  True
 |
 |  Method resolution order:
 |      BufferOutputStream
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__ = __reduce_cython__(...)
 |      BufferOutputStream.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      BufferOutputStream.__setstate_cython__(self, __pyx_state)
 |
 |  getvalue(self)
 |      BufferOutputStream.getvalue(self)
 |
 |      Finalize output stream and return result as pyarrow.Buffer.
 |
 |      Returns
 |      -------
 |      value : Buffer
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  fileno(self)
 |      NativeFile.fileno(self)
 |
 |      NOT IMPLEMENTED
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class BufferReader in module pyarrow.lib

class BufferReader(NativeFile)
 |  BufferReader(obj)
 |
 |  Zero-copy reader from objects convertible to Arrow buffer.
 |
 |  Parameters
 |  ----------
 |  obj : Python bytes or pyarrow.Buffer
 |
 |  Examples
 |  --------
 |  Create an Arrow input stream and inspect it:
 |
 |  >>> import pyarrow as pa
 |  >>> data = b'reader data'
 |  >>> buf = memoryview(data)
 |  >>> with pa.input_stream(buf) as stream:
 |  ...     stream.size()
 |  ...     stream.read(6)
 |  ...     stream.seek(7)
 |  ...     stream.read(15)
 |  ...
 |  11
 |  b'reader'
 |  7
 |  b'data'
 |
 |  Method resolution order:
 |      BufferReader
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      BufferReader.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      BufferReader.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  fileno(self)
 |      NativeFile.fileno(self)
 |
 |      NOT IMPLEMENTED
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class BufferedInputStream in module pyarrow.lib

class BufferedInputStream(NativeFile)
 |  BufferedInputStream(NativeFile stream, int buffer_size, MemoryPool memory_pool=None)
 |
 |  An input stream that performs buffered reads from
 |  an unbuffered input stream, which can mitigate the overhead
 |  of many small reads in some cases.
 |
 |  Parameters
 |  ----------
 |  stream : NativeFile
 |      The input stream to wrap with the buffer
 |  buffer_size : int
 |      Size of the temporary read buffer.
 |  memory_pool : MemoryPool
 |      The memory pool used to allocate the buffer.
 |
 |  Method resolution order:
 |      BufferedInputStream
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      BufferedInputStream.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      BufferedInputStream.__setstate_cython__(self, __pyx_state)
 |
 |  detach(self)
 |      BufferedInputStream.detach(self)
 |
 |      Release the raw InputStream.
 |      Further operations on this stream are invalid.
 |
 |      Returns
 |      -------
 |      raw : NativeFile
 |          The underlying raw input stream
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  fileno(self)
 |      NativeFile.fileno(self)
 |
 |      NOT IMPLEMENTED
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class BufferedOutputStream in module pyarrow.lib

class BufferedOutputStream(NativeFile)
 |  BufferedOutputStream(NativeFile stream, int buffer_size, MemoryPool memory_pool=None)
 |
 |  An output stream that performs buffered reads from
 |  an unbuffered output stream, which can mitigate the overhead
 |  of many small writes in some cases.
 |
 |  Parameters
 |  ----------
 |  stream : NativeFile
 |      The writable output stream to wrap with the buffer
 |  buffer_size : int
 |      Size of the buffer that should be added.
 |  memory_pool : MemoryPool
 |      The memory pool used to allocate the buffer.
 |
 |  Method resolution order:
 |      BufferedOutputStream
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      BufferedOutputStream.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      BufferedOutputStream.__setstate_cython__(self, __pyx_state)
 |
 |  detach(self)
 |      BufferedOutputStream.detach(self)
 |
 |      Flush any buffered writes and release the raw OutputStream.
 |      Further operations on this stream are invalid.
 |
 |      Returns
 |      -------
 |      raw : NativeFile
 |          The underlying raw output stream.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  fileno(self)
 |      NativeFile.fileno(self)
 |
 |      NOT IMPLEMENTED
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class BuildInfo in module importlib._bootstrap

class BuildInfo(builtins.tuple)
 |  BuildInfo(build_type, cpp_build_info)
 |
 |  BuildInfo(build_type, cpp_build_info)
 |
 |  Method resolution order:
 |      BuildInfo
 |      builtins.tuple
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getnewargs__(self) from collections.BuildInfo
 |      Return self as a plain tuple.  Used by copy and pickle.
 |
 |  __repr__(self) from collections.BuildInfo
 |      Return a nicely formatted representation string
 |
 |  _asdict(self) from collections.BuildInfo
 |      Return a new dict which maps field names to their values.
 |
 |  _replace(self, /, **kwds) from collections.BuildInfo
 |      Return a new BuildInfo object replacing specified fields with new values
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  _make(iterable) from collections.BuildInfo
 |      Make a new BuildInfo object from a sequence or iterable
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(_cls, build_type, cpp_build_info) from namedtuple_BuildInfo.BuildInfo
 |      Create new instance of BuildInfo(build_type, cpp_build_info)
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  build_type
 |      Alias for field number 0
 |
 |  cpp_build_info
 |      Alias for field number 1
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __match_args__ = ('build_type', 'cpp_build_info')
 |
 |  _field_defaults = {}
 |
 |  _fields = ('build_type', 'cpp_build_info')
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.tuple:
 |
 |  __add__(self, value, /)
 |      Return self+value.
 |
 |  __contains__(self, key, /)
 |      Return bool(key in self).
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(self, value, /)
 |      Return self*value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  count(self, value, /)
 |      Return number of occurrences of value.
 |
 |  index(self, value, start=0, stop=9223372036854775807, /)
 |      Return first index of value.
 |
 |      Raises ValueError if the value is not present.
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from builtins.tuple:
 |
 |  __class_getitem__(...)
 |      See PEP 585

Python Library Documentation: class CacheOptions in module pyarrow.lib

class CacheOptions(_Weakrefable)
 |  CacheOptions(hole_size_limit=None, *, range_size_limit=None, lazy=None, prefetch_limit=None)
 |
 |  Cache options for a pre-buffered fragment scan.
 |
 |  Parameters
 |  ----------
 |  hole_size_limit : int, default 8KiB
 |      The maximum distance in bytes between two consecutive ranges; beyond
 |      this value, ranges are not combined.
 |  range_size_limit : int, default 32MiB
 |      The maximum size in bytes of a combined range; if combining two
 |      consecutive ranges would produce a range of a size greater than this,
 |      they are not combined
 |  lazy : bool, default True
 |      lazy = false: request all byte ranges when PreBuffer or WillNeed is called.
 |      lazy = True, prefetch_limit = 0: request merged byte ranges only after the reader
 |      needs them.
 |      lazy = True, prefetch_limit = k: prefetch up to k merged byte ranges ahead of the
 |      range that is currently being read.
 |  prefetch_limit : int, default 0
 |      The maximum number of ranges to be prefetched. This is only used for
 |      lazy cache to asynchronously read some ranges after reading the target
 |      range.
 |
 |  Method resolution order:
 |      CacheOptions
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      CacheOptions.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_network_metrics(time_to_first_byte_millis, transfer_bandwidth_mib_per_sec, ideal_bandwidth_utilization_frac=0.9, max_ideal_request_size_mib=64)
 |      CacheOptions.from_network_metrics(time_to_first_byte_millis, transfer_bandwidth_mib_per_sec, ideal_bandwidth_utilization_frac=0.9, max_ideal_request_size_mib=64)
 |
 |      Create suitable CacheOptions based on provided network metrics.
 |
 |      Typically this will be used with object storage solutions like Amazon S3,
 |      Google Cloud Storage and Azure Blob Storage.
 |
 |      Parameters
 |      ----------
 |      time_to_first_byte_millis : int
 |          Seek-time or Time-To-First-Byte (TTFB) in milliseconds, also called call
 |          setup latency of a new read request. The value is a positive integer.
 |      transfer_bandwidth_mib_per_sec : int
 |          Data transfer Bandwidth (BW) in MiB/sec (per connection). The value is a positive
 |          integer.
 |      ideal_bandwidth_utilization_frac : int, default 0.9
 |          Transfer bandwidth utilization fraction (per connection) to maximize the net
 |          data load. The value is a positive float less than 1.
 |      max_ideal_request_size_mib : int, default 64
 |          The maximum single data request size (in MiB) to maximize the net data load.
 |
 |      Returns
 |      -------
 |      CacheOptions
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  hole_size_limit
 |
 |  lazy
 |
 |  prefetch_limit
 |
 |  range_size_limit
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class ChunkedArray in module pyarrow.lib

class ChunkedArray(_PandasConvertible)
 |  ChunkedArray()
 |
 |  An array-like composed from a (possibly empty) collection of pyarrow.Arrays
 |
 |  Warnings
 |  --------
 |  Do not call this class's constructor directly.
 |
 |  Examples
 |  --------
 |  To construct a ChunkedArray object use :func:`pyarrow.chunked_array`:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.chunked_array([], type=pa.int8())
 |  <pyarrow.lib.ChunkedArray object at ...>
 |  [
 |  ...
 |  ]
 |
 |  >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |  <pyarrow.lib.ChunkedArray object at ...>
 |  [
 |    [
 |      2,
 |      2,
 |      4
 |    ],
 |    [
 |      4,
 |      5,
 |      100
 |    ]
 |  ]
 |  >>> isinstance(pa.chunked_array([[2, 2, 4], [4, 5, 100]]), pa.ChunkedArray)
 |  True
 |
 |  Method resolution order:
 |      ChunkedArray
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __array__(self, dtype=None, copy=None)
 |      ChunkedArray.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_stream__(self, requested_schema=None)
 |      ChunkedArray.__arrow_c_stream__(self, requested_schema=None)
 |
 |      Export to a C ArrowArrayStream PyCapsule.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule, default None
 |          The schema to which the stream should be casted, passed as a
 |          PyCapsule containing a C ArrowSchema representation of the
 |          requested schema.
 |
 |      Returns
 |      -------
 |      PyCapsule
 |          A capsule containing a C ArrowArrayStream struct.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      ChunkedArray.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or ChunkedArray (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      ChunkedArray.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __sizeof__(self)
 |      ChunkedArray.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  cast(self, target_type=None, safe=None, options=None)
 |      ChunkedArray.cast(self, target_type=None, safe=None, options=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |
 |      Returns
 |      -------
 |      cast : Array or ChunkedArray
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.type
 |      DataType(int64)
 |
 |      Change the data type of an array:
 |
 |      >>> n_legs_seconds = n_legs.cast(pa.duration('s'))
 |      >>> n_legs_seconds.type
 |      DurationType(duration[s])
 |
 |  chunk(self, i)
 |      ChunkedArray.chunk(self, i)
 |
 |      Select a chunk by its index.
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Array
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
 |      >>> n_legs.chunk(1)
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        4,
 |        5,
 |        100
 |      ]
 |
 |  combine_chunks(self, memory_pool=None)
 |      ChunkedArray.combine_chunks(self, MemoryPool memory_pool=None)
 |
 |      Flatten this ChunkedArray into a single non-chunked array.
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          For memory allocations, if required, otherwise use default pool
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2,
 |          4
 |        ],
 |        [
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |      >>> n_legs.combine_chunks()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        2,
 |        2,
 |        4,
 |        4,
 |        5,
 |        100
 |      ]
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      ChunkedArray.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : ChunkedArray
 |          A dictionary-encoded version of this array.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> animals = pa.chunked_array((
 |      ...             ["Flamingo", "Parrot", "Dog"],
 |      ...             ["Horse", "Brittle stars", "Centipede"]
 |      ...             ))
 |      >>> animals.dictionary_encode()
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |      ...
 |        -- dictionary:
 |          [
 |            "Flamingo",
 |            "Parrot",
 |            "Dog",
 |            "Horse",
 |            "Brittle stars",
 |            "Centipede"
 |          ]
 |        -- indices:
 |          [
 |            0,
 |            1,
 |            2
 |          ],
 |      ...
 |        -- dictionary:
 |          [
 |            "Flamingo",
 |            "Parrot",
 |            "Dog",
 |            "Horse",
 |            "Brittle stars",
 |            "Centipede"
 |          ]
 |        -- indices:
 |          [
 |            3,
 |            4,
 |            5
 |          ]
 |      ]
 |
 |  drop_null(self)
 |      ChunkedArray.drop_null(self)
 |
 |      Remove missing values from a chunked array.
 |      See :func:`pyarrow.compute.drop_null` for full description.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
 |      >>> n_legs
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2,
 |          null
 |        ],
 |        [
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |      >>> n_legs.drop_null()
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2
 |        ],
 |        [
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |
 |  equals(self, other)
 |      ChunkedArray.equals(self, ChunkedArray other)
 |
 |      Return whether the contents of two chunked arrays are equal.
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.ChunkedArray
 |          Chunked array to compare against.
 |
 |      Returns
 |      -------
 |      are_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> animals = pa.chunked_array((
 |      ...             ["Flamingo", "Parrot", "Dog"],
 |      ...             ["Horse", "Brittle stars", "Centipede"]
 |      ...             ))
 |      >>> n_legs.equals(n_legs)
 |      True
 |      >>> n_legs.equals(animals)
 |      False
 |
 |  fill_null(self, fill_value)
 |      ChunkedArray.fill_null(self, fill_value)
 |
 |      Replace each null element in values with fill_value.
 |
 |      See :func:`pyarrow.compute.fill_null` for full usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array or ChunkedArray
 |          A new array with nulls replaced by the given value.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> fill_value = pa.scalar(5, type=pa.int8())
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
 |      >>> n_legs.fill_null(fill_value)
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2,
 |          4,
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |
 |  filter(self, mask, null_selection_behavior='drop')
 |      ChunkedArray.filter(self, mask, null_selection_behavior='drop')
 |
 |      Select values from the chunked array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the chunked array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array or ChunkedArray
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2,
 |          4
 |        ],
 |        [
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |      >>> mask = pa.array([True, False, None, True, False, True])
 |      >>> n_legs.filter(mask)
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2
 |        ],
 |        [
 |          4,
 |          100
 |        ]
 |      ]
 |      >>> n_legs.filter(mask, null_selection_behavior="emit_null")
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          null
 |        ],
 |        [
 |          4,
 |          100
 |        ]
 |      ]
 |
 |  flatten(self, memory_pool=None)
 |      ChunkedArray.flatten(self, MemoryPool memory_pool=None)
 |
 |      Flatten this ChunkedArray.  If it has a struct type, the column is
 |      flattened into one array per struct field.
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          For memory allocations, if required, otherwise use default pool
 |
 |      Returns
 |      -------
 |      result : list of ChunkedArray
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> c_arr = pa.chunked_array(n_legs.value_counts())
 |      >>> c_arr
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        -- is_valid: all not null
 |        -- child 0 type: int64
 |          [
 |            2,
 |            4,
 |            5,
 |            100
 |          ]
 |        -- child 1 type: int64
 |          [
 |            2,
 |            2,
 |            1,
 |            1
 |          ]
 |      ]
 |      >>> c_arr.flatten()
 |      [<pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          4,
 |          5,
 |          100
 |        ]
 |      ], <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2,
 |          1,
 |          1
 |        ]
 |      ]]
 |      >>> c_arr.type
 |      StructType(struct<values: int64, counts: int64>)
 |      >>> n_legs.type
 |      DataType(int64)
 |
 |  format(self, **kwargs)
 |      ChunkedArray.format(self, **kwargs)
 |
 |      DEPRECATED, use pyarrow.ChunkedArray.to_string
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict
 |
 |      Returns
 |      -------
 |      str
 |
 |  get_total_buffer_size(self)
 |      ChunkedArray.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the chunked array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
 |      >>> n_legs.get_total_buffer_size()
 |      49
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      ChunkedArray.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2,
 |          4
 |        ],
 |        [
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |      >>> n_legs.index(4)
 |      <pyarrow.Int64Scalar: 2>
 |      >>> n_legs.index(4, start=3)
 |      <pyarrow.Int64Scalar: 3>
 |
 |  is_nan(self)
 |      ChunkedArray.is_nan(self)
 |
 |      Return boolean array indicating the NaN values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> arr = pa.chunked_array([[2, np.nan, 4], [4, None, 100]])
 |      >>> arr.is_nan()
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          false,
 |          true,
 |          false,
 |          false,
 |          null,
 |          false
 |        ]
 |      ]
 |
 |  is_null(self, *, nan_is_null=False)
 |      ChunkedArray.is_null(self, *, nan_is_null=False)
 |
 |      Return boolean array indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array or ChunkedArray
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
 |      >>> n_legs.is_null()
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          false,
 |          false,
 |          false,
 |          false,
 |          true,
 |          false
 |        ]
 |      ]
 |
 |  is_valid(self)
 |      ChunkedArray.is_valid(self)
 |
 |      Return boolean array indicating the non-null values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
 |      >>> n_legs.is_valid()
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          true,
 |          true,
 |          true
 |        ],
 |        [
 |          true,
 |          false,
 |          true
 |        ]
 |      ]
 |
 |  iterchunks(self)
 |      ChunkedArray.iterchunks(self)
 |
 |      Convert to an iterator of ChunkArrays.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
 |      >>> for i in n_legs.iterchunks():
 |      ...     print(i.null_count)
 |      ...
 |      0
 |      1
 |
 |  length(self)
 |      ChunkedArray.length(self)
 |
 |      Return length of a ChunkedArray.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.length()
 |      6
 |
 |  slice(self, offset=0, length=None)
 |      ChunkedArray.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this ChunkedArray
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice
 |      length : int, default None
 |          Length of slice (default is until end of batch starting from
 |          offset)
 |
 |      Returns
 |      -------
 |      sliced : ChunkedArray
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2,
 |          4
 |        ],
 |        [
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |      >>> n_legs.slice(2,2)
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          4
 |        ],
 |        [
 |          4
 |        ]
 |      ]
 |
 |  sort(self, order='ascending', **kwargs)
 |      ChunkedArray.sort(self, order='ascending', **kwargs)
 |
 |      Sort the ChunkedArray
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : ChunkedArray
 |
 |  take(self, indices)
 |      ChunkedArray.take(self, indices)
 |
 |      Select values from the chunked array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array or ChunkedArray
 |          An array with the same datatype, containing the taken values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2,
 |          4
 |        ],
 |        [
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |      >>> n_legs.take([1,4,5])
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          5,
 |          100
 |        ]
 |      ]
 |
 |  to_numpy(self, zero_copy_only=False)
 |      ChunkedArray.to_numpy(self, zero_copy_only=False)
 |
 |      Return a NumPy copy of this array (experimental).
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default False
 |          Introduced for signature consistence with pyarrow.Array.to_numpy.
 |          This must be False here since NumPy arrays' buffer must be contiguous.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_numpy()
 |      array([  2,   2,   4,   4,   5, 100])
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      ChunkedArray.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
 |      >>> n_legs.to_pylist()
 |      [2, 2, 4, 4, None, 100]
 |
 |  to_string(self, *, indent=0, window=5, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      ChunkedArray.to_string(self, *, int indent=0, int window=5, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the ChunkedArray
 |
 |      Parameters
 |      ----------
 |      indent : int
 |          How much to indent right the content of the array,
 |          by default ``0``.
 |      window : int
 |          How many items to preview within each chunk at the begin and end
 |          of the chunk when the chunk is bigger than the window.
 |          The other elements will be ellipsed.
 |      container_window : int
 |          How many chunks to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other elements will be ellipsed.
 |          This setting also applies to list columns.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_string(skip_new_lines=True)
 |      '[[2,2,4],[4,5,100]]'
 |
 |  unify_dictionaries(self, memory_pool=None)
 |      ChunkedArray.unify_dictionaries(self, MemoryPool memory_pool=None)
 |
 |      Unify dictionaries across all chunks.
 |
 |      This method returns an equivalent chunked array, but where all
 |      chunks share the same dictionary values.  Dictionary indices are
 |      transposed accordingly.
 |
 |      If there are no dictionaries in the chunked array, it is returned
 |      unchanged.
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          For memory allocations, if required, otherwise use default pool
 |
 |      Returns
 |      -------
 |      result : ChunkedArray
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode()
 |      >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode()
 |      >>> c_arr = pa.chunked_array([arr_1, arr_2])
 |      >>> c_arr
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |      ...
 |        -- dictionary:
 |          [
 |            "Flamingo",
 |            "Parrot",
 |            "Dog"
 |          ]
 |        -- indices:
 |          [
 |            0,
 |            1,
 |            2
 |          ],
 |      ...
 |        -- dictionary:
 |          [
 |            "Horse",
 |            "Brittle stars",
 |            "Centipede"
 |          ]
 |        -- indices:
 |          [
 |            0,
 |            1,
 |            2
 |          ]
 |      ]
 |      >>> c_arr.unify_dictionaries()
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |      ...
 |        -- dictionary:
 |          [
 |            "Flamingo",
 |            "Parrot",
 |            "Dog",
 |            "Horse",
 |            "Brittle stars",
 |            "Centipede"
 |          ]
 |        -- indices:
 |          [
 |            0,
 |            1,
 |            2
 |          ],
 |      ...
 |        -- dictionary:
 |          [
 |            "Flamingo",
 |            "Parrot",
 |            "Dog",
 |            "Horse",
 |            "Brittle stars",
 |            "Centipede"
 |          ]
 |        -- indices:
 |          [
 |            3,
 |            4,
 |            5
 |          ]
 |      ]
 |
 |  unique(self)
 |      ChunkedArray.unique(self)
 |
 |      Compute distinct elements in array
 |
 |      Returns
 |      -------
 |      pyarrow.Array
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2,
 |          4
 |        ],
 |        [
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |      >>> n_legs.unique()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        2,
 |        4,
 |        5,
 |        100
 |      ]
 |
 |  validate(self, *, full=False)
 |      ChunkedArray.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      ChunkedArray.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      An array of  <input type "Values", int64_t "Counts"> structs
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2,
 |          4
 |        ],
 |        [
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |      >>> n_legs.value_counts()
 |      <pyarrow.lib.StructArray object at ...>
 |      -- is_valid: all not null
 |      -- child 0 type: int64
 |        [
 |          2,
 |          4,
 |          5,
 |          100
 |        ]
 |      -- child 1 type: int64
 |        [
 |          2,
 |          2,
 |          1,
 |          1
 |        ]
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  chunks
 |      Convert to a list of single-chunked arrays.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
 |      >>> n_legs
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          2,
 |          null
 |        ],
 |        [
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |      >>> n_legs.chunks
 |      [<pyarrow.lib.Int64Array object at ...>
 |      [
 |        2,
 |        2,
 |        null
 |      ], <pyarrow.lib.Int64Array object at ...>
 |      [
 |        4,
 |        5,
 |        100
 |      ]]
 |
 |  data
 |
 |  is_cpu
 |      Whether all chunks in the ChunkedArray are CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the chunked array.
 |
 |      In other words, the sum of bytes from all buffer ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will only be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
 |      >>> n_legs.nbytes
 |      49
 |
 |  null_count
 |      Number of null entries
 |
 |      Returns
 |      -------
 |      int
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
 |      >>> n_legs.null_count
 |      1
 |
 |  num_chunks
 |      Number of underlying chunks.
 |
 |      Returns
 |      -------
 |      int
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
 |      >>> n_legs.num_chunks
 |      2
 |
 |  type
 |      Return data type of a ChunkedArray.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.type
 |      DataType(int64)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Codec in module pyarrow.lib

class Codec(_Weakrefable)
 |  Codec(str compression, compression_level=None)
 |
 |  Compression codec.
 |
 |  Parameters
 |  ----------
 |  compression : str
 |      Type of compression codec to initialize, valid values are: 'gzip',
 |      'bz2', 'brotli', 'lz4' (or 'lz4_frame'), 'lz4_raw', 'zstd' and
 |      'snappy'.
 |  compression_level : int, None
 |      Optional parameter specifying how aggressively to compress.  The
 |      possible ranges and effect of this parameter depend on the specific
 |      codec chosen.  Higher values compress more but typically use more
 |      resources (CPU/RAM).  Some codecs support negative values.
 |
 |      gzip
 |          The compression_level maps to the memlevel parameter of
 |          deflateInit2.  Higher levels use more RAM but are faster
 |          and should have higher compression ratios.
 |
 |      bz2
 |          The compression level maps to the blockSize100k parameter of
 |          the BZ2_bzCompressInit function.  Higher levels use more RAM
 |          but are faster and should have higher compression ratios.
 |
 |      brotli
 |          The compression level maps to the BROTLI_PARAM_QUALITY
 |          parameter.  Higher values are slower and should have higher
 |          compression ratios.
 |
 |      lz4/lz4_frame/lz4_raw
 |          The compression level parameter is not supported and must
 |          be None
 |
 |      zstd
 |          The compression level maps to the compressionLevel parameter
 |          of ZSTD_initCStream.  Negative values are supported.  Higher
 |          values are slower and should have higher compression ratios.
 |
 |      snappy
 |          The compression level parameter is not supported and must
 |          be None
 |
 |
 |  Raises
 |  ------
 |  ValueError
 |      If invalid compression value is passed.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |  >>> pa.Codec.is_available('gzip')
 |  True
 |  >>> codec = pa.Codec('gzip')
 |  >>> codec.name
 |  'gzip'
 |  >>> codec.compression_level
 |  9
 |
 |  Method resolution order:
 |      Codec
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      Codec.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      Codec.__setstate_cython__(self, __pyx_state)
 |
 |  compress(self, buf, asbytes=False, memory_pool=None)
 |      Codec.compress(self, buf, asbytes=False, memory_pool=None)
 |
 |      Compress data from buffer-like object.
 |
 |      Parameters
 |      ----------
 |      buf : pyarrow.Buffer, bytes, or other object supporting buffer protocol
 |      asbytes : bool, default False
 |          Return result as Python bytes object, otherwise Buffer
 |      memory_pool : MemoryPool, default None
 |          Memory pool to use for buffer allocations, if any
 |
 |      Returns
 |      -------
 |      compressed : pyarrow.Buffer or bytes (if asbytes=True)
 |
 |  decompress(self, buf, decompressed_size=None, asbytes=False, memory_pool=None)
 |      Codec.decompress(self, buf, decompressed_size=None, asbytes=False, memory_pool=None)
 |
 |      Decompress data from buffer-like object.
 |
 |      Parameters
 |      ----------
 |      buf : pyarrow.Buffer, bytes, or memoryview-compatible object
 |      decompressed_size : int, default None
 |          Size of the decompressed result
 |      asbytes : boolean, default False
 |          Return result as Python bytes object, otherwise Buffer
 |      memory_pool : MemoryPool, default None
 |          Memory pool to use for buffer allocations, if any.
 |
 |      Returns
 |      -------
 |      uncompressed : pyarrow.Buffer or bytes (if asbytes=True)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  default_compression_level(compression)
 |      Codec.default_compression_level(str compression)
 |
 |      Returns the compression level that Arrow will use for the codec if
 |      None is specified.
 |
 |      Parameters
 |      ----------
 |      compression : str
 |          Type of compression codec,
 |          refer to Codec docstring for a list of supported ones.
 |
 |  detect(path)
 |      Codec.detect(path)
 |
 |      Detect and instantiate compression codec based on file extension.
 |
 |      Parameters
 |      ----------
 |      path : str, path-like
 |          File-path to detect compression from.
 |
 |      Raises
 |      ------
 |      TypeError
 |          If the passed value is not path-like.
 |      ValueError
 |          If the compression can't be detected from the path.
 |
 |      Returns
 |      -------
 |      Codec
 |
 |  is_available(compression)
 |      Codec.is_available(str compression)
 |
 |      Returns whether the compression support has been built and enabled.
 |
 |      Parameters
 |      ----------
 |      compression : str
 |           Type of compression codec,
 |           refer to Codec docstring for a list of supported ones.
 |
 |      Returns
 |      -------
 |      bool
 |
 |  maximum_compression_level(compression)
 |      Codec.maximum_compression_level(str compression)
 |
 |      Returns the largest valid value for the compression level
 |
 |      Parameters
 |      ----------
 |      compression : str
 |          Type of compression codec,
 |          refer to Codec docstring for a list of supported ones.
 |
 |  minimum_compression_level(compression)
 |      Codec.minimum_compression_level(str compression)
 |
 |      Returns the smallest valid value for the compression level
 |
 |      Parameters
 |      ----------
 |      compression : str
 |          Type of compression codec,
 |          refer to Codec docstring for a list of supported ones.
 |
 |  supports_compression_level(compression)
 |      Codec.supports_compression_level(str compression)
 |
 |      Returns true if the compression level parameter is supported
 |      for the given codec.
 |
 |      Parameters
 |      ----------
 |      compression : str
 |          Type of compression codec,
 |          refer to Codec docstring for a list of supported ones.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  compression_level
 |      Returns the compression level parameter of the codec
 |
 |  name
 |      Returns the name of the codec
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class CompressedInputStream in module pyarrow.lib

class CompressedInputStream(NativeFile)
 |  CompressedInputStream(stream, str compression)
 |
 |  An input stream wrapper which decompresses data on the fly.
 |
 |  Parameters
 |  ----------
 |  stream : string, path, pyarrow.NativeFile, or file-like object
 |      Input stream object to wrap with the compression.
 |  compression : str
 |      The compression type ("bz2", "brotli", "gzip", "lz4" or "zstd").
 |
 |  Examples
 |  --------
 |  Create an output stream which compresses the data:
 |
 |  >>> import pyarrow as pa
 |  >>> data = b"Compressed stream"
 |  >>> raw = pa.BufferOutputStream()
 |  >>> with pa.CompressedOutputStream(raw, "gzip") as compressed:
 |  ...     compressed.write(data)
 |  ...
 |  17
 |
 |  Create an input stream with decompression referencing the
 |  buffer with compressed data:
 |
 |  >>> cdata = raw.getvalue()
 |  >>> with pa.input_stream(cdata, compression="gzip") as compressed:
 |  ...     compressed.read()
 |  ...
 |  b'Compressed stream'
 |
 |  which actually translates to the use of ``BufferReader``and
 |  ``CompressedInputStream``:
 |
 |  >>> raw = pa.BufferReader(cdata)
 |  >>> with pa.CompressedInputStream(raw, "gzip") as compressed:
 |  ...     compressed.read()
 |  ...
 |  b'Compressed stream'
 |
 |  Method resolution order:
 |      CompressedInputStream
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      CompressedInputStream.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      CompressedInputStream.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  fileno(self)
 |      NativeFile.fileno(self)
 |
 |      NOT IMPLEMENTED
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class CompressedOutputStream in module pyarrow.lib

class CompressedOutputStream(NativeFile)
 |  CompressedOutputStream(stream, str compression)
 |
 |  An output stream wrapper which compresses data on the fly.
 |
 |  Parameters
 |  ----------
 |  stream : string, path, pyarrow.NativeFile, or file-like object
 |      Input stream object to wrap with the compression.
 |  compression : str
 |      The compression type ("bz2", "brotli", "gzip", "lz4" or "zstd").
 |
 |  Examples
 |  --------
 |  Create an output stream which compresses the data:
 |
 |  >>> import pyarrow as pa
 |  >>> data = b"Compressed stream"
 |  >>> raw = pa.BufferOutputStream()
 |  >>> with pa.CompressedOutputStream(raw, "gzip") as compressed:
 |  ...     compressed.write(data)
 |  ...
 |  17
 |
 |  Method resolution order:
 |      CompressedOutputStream
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      CompressedOutputStream.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      CompressedOutputStream.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  fileno(self)
 |      NativeFile.fileno(self)
 |
 |      NOT IMPLEMENTED
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class CppBuildInfo in module importlib._bootstrap

class CppBuildInfo(builtins.tuple)
 |  CppBuildInfo(version, version_info, so_version, full_so_version, compiler_id, compiler_version, compiler_flags, git_id, git_description, package_kind, build_type)
 |
 |  CppBuildInfo(version, version_info, so_version, full_so_version, compiler_id, compiler_version, compiler_flags, git_id, git_description, package_kind, build_type)
 |
 |  Method resolution order:
 |      CppBuildInfo
 |      builtins.tuple
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getnewargs__(self) from collections.CppBuildInfo
 |      Return self as a plain tuple.  Used by copy and pickle.
 |
 |  __repr__(self) from collections.CppBuildInfo
 |      Return a nicely formatted representation string
 |
 |  _asdict(self) from collections.CppBuildInfo
 |      Return a new dict which maps field names to their values.
 |
 |  _replace(self, /, **kwds) from collections.CppBuildInfo
 |      Return a new CppBuildInfo object replacing specified fields with new values
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  _make(iterable) from collections.CppBuildInfo
 |      Make a new CppBuildInfo object from a sequence or iterable
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(_cls, version, version_info, so_version, full_so_version, compiler_id, compiler_version, compiler_flags, git_id, git_description, package_kind, build_type) from namedtuple_CppBuildInfo.CppBuildInfo
 |      Create new instance of CppBuildInfo(version, version_info, so_version, full_so_version, compiler_id, compiler_version, compiler_flags, git_id, git_description, package_kind, build_type)
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  version
 |      Alias for field number 0
 |
 |  version_info
 |      Alias for field number 1
 |
 |  so_version
 |      Alias for field number 2
 |
 |  full_so_version
 |      Alias for field number 3
 |
 |  compiler_id
 |      Alias for field number 4
 |
 |  compiler_version
 |      Alias for field number 5
 |
 |  compiler_flags
 |      Alias for field number 6
 |
 |  git_id
 |      Alias for field number 7
 |
 |  git_description
 |      Alias for field number 8
 |
 |  package_kind
 |      Alias for field number 9
 |
 |  build_type
 |      Alias for field number 10
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __match_args__ = ('version', 'version_info', 'so_version', 'full_so_ve...
 |
 |  _field_defaults = {}
 |
 |  _fields = ('version', 'version_info', 'so_version', 'full_so_version',...
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.tuple:
 |
 |  __add__(self, value, /)
 |      Return self+value.
 |
 |  __contains__(self, key, /)
 |      Return bool(key in self).
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(self, value, /)
 |      Return self*value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  count(self, value, /)
 |      Return number of occurrences of value.
 |
 |  index(self, value, start=0, stop=9223372036854775807, /)
 |      Return first index of value.
 |
 |      Raises ValueError if the value is not present.
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from builtins.tuple:
 |
 |  __class_getitem__(...)
 |      See PEP 585

Python Library Documentation: class DataType in module pyarrow.lib

class DataType(_Weakrefable)
 |  DataType()
 |
 |  Base class of all Arrow data types.
 |
 |  Each data type is an *instance* of this class.
 |
 |  Examples
 |  --------
 |  Instance of int64 type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.int64()
 |  DataType(int64)
 |
 |  Method resolution order:
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      DataType.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class Date32Array in module pyarrow.lib

class Date32Array(NumericArray)
 |  Concrete class for Arrow arrays of date32 data type.
 |
 |  Method resolution order:
 |      Date32Array
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Date32Scalar in module pyarrow.lib

class Date32Scalar(Scalar)
 |  Concrete class for date32 scalars.
 |
 |  Method resolution order:
 |      Date32Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Date32Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python datetime.datetime instance.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Date64Array in module pyarrow.lib

class Date64Array(NumericArray)
 |  Concrete class for Arrow arrays of date64 data type.
 |
 |  Method resolution order:
 |      Date64Array
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Date64Scalar in module pyarrow.lib

class Date64Scalar(Scalar)
 |  Concrete class for date64 scalars.
 |
 |  Method resolution order:
 |      Date64Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Date64Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python datetime.datetime instance.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Decimal128Array in module pyarrow.lib

class Decimal128Array(FixedSizeBinaryArray)
 |  Concrete class for Arrow arrays of decimal128 data type.
 |
 |  Method resolution order:
 |      Decimal128Array
 |      FixedSizeBinaryArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Decimal128Scalar in module pyarrow.lib

class Decimal128Scalar(Scalar)
 |  Concrete class for decimal128 scalars.
 |
 |  Method resolution order:
 |      Decimal128Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Decimal128Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python Decimal.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Decimal128Type in module pyarrow.lib

class Decimal128Type(FixedSizeBinaryType)
 |  Concrete class for decimal128 data types.
 |
 |  Examples
 |  --------
 |  Create an instance of decimal128 type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.decimal128(5, 2)
 |  Decimal128Type(decimal128(5, 2))
 |
 |  Method resolution order:
 |      Decimal128Type
 |      FixedSizeBinaryType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      Decimal128Type.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  precision
 |      The decimal precision, in number of decimal digits (an integer).
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.decimal128(5, 2)
 |      >>> t.precision
 |      5
 |
 |  scale
 |      The decimal scale (an integer).
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.decimal128(5, 2)
 |      >>> t.scale
 |      2
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class Decimal256Array in module pyarrow.lib

class Decimal256Array(FixedSizeBinaryArray)
 |  Concrete class for Arrow arrays of decimal256 data type.
 |
 |  Method resolution order:
 |      Decimal256Array
 |      FixedSizeBinaryArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Decimal256Scalar in module pyarrow.lib

class Decimal256Scalar(Scalar)
 |  Concrete class for decimal256 scalars.
 |
 |  Method resolution order:
 |      Decimal256Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Decimal256Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python Decimal.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Decimal256Type in module pyarrow.lib

class Decimal256Type(FixedSizeBinaryType)
 |  Concrete class for decimal256 data types.
 |
 |  Examples
 |  --------
 |  Create an instance of decimal256 type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.decimal256(76, 38)
 |  Decimal256Type(decimal256(76, 38))
 |
 |  Method resolution order:
 |      Decimal256Type
 |      FixedSizeBinaryType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      Decimal256Type.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  precision
 |      The decimal precision, in number of decimal digits (an integer).
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.decimal256(76, 38)
 |      >>> t.precision
 |      76
 |
 |  scale
 |      The decimal scale (an integer).
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.decimal256(76, 38)
 |      >>> t.scale
 |      38
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class Decimal32Array in module pyarrow.lib

class Decimal32Array(FixedSizeBinaryArray)
 |  Method resolution order:
 |      Decimal32Array
 |      FixedSizeBinaryArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Decimal32Scalar in module pyarrow.lib

class Decimal32Scalar(Scalar)
 |  Concrete class for decimal32 scalars.
 |
 |  Method resolution order:
 |      Decimal32Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Decimal32Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python Decimal.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Decimal32Type in module pyarrow.lib

class Decimal32Type(FixedSizeBinaryType)
 |  Concrete class for decimal32 data types.
 |
 |  Examples
 |  --------
 |  Create an instance of decimal32 type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.decimal32(5, 2)
 |  Decimal32Type(decimal32(5, 2))
 |
 |  Method resolution order:
 |      Decimal32Type
 |      FixedSizeBinaryType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      Decimal32Type.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  precision
 |      The decimal precision, in number of decimal digits (an integer).
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.decimal32(5, 2)
 |      >>> t.precision
 |      5
 |
 |  scale
 |      The decimal scale (an integer).
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.decimal32(5, 2)
 |      >>> t.scale
 |      2
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class Decimal64Array in module pyarrow.lib

class Decimal64Array(FixedSizeBinaryArray)
 |  Concrete class for Arrow arrays of decimal64 data type.
 |
 |  Method resolution order:
 |      Decimal64Array
 |      FixedSizeBinaryArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Decimal64Scalar in module pyarrow.lib

class Decimal64Scalar(Scalar)
 |  Concrete class for decimal64 scalars.
 |
 |  Method resolution order:
 |      Decimal64Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Decimal64Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python Decimal.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Decimal64Type in module pyarrow.lib

class Decimal64Type(FixedSizeBinaryType)
 |  Concrete class for decimal64 data types.
 |
 |  Examples
 |  --------
 |  Create an instance of decimal64 type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.decimal64(5, 2)
 |  Decimal64Type(decimal64(5, 2))
 |
 |  Method resolution order:
 |      Decimal64Type
 |      FixedSizeBinaryType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      Decimal64Type.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  precision
 |      The decimal precision, in number of decimal digits (an integer).
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.decimal64(5, 2)
 |      >>> t.precision
 |      5
 |
 |  scale
 |      The decimal scale (an integer).
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.decimal64(5, 2)
 |      >>> t.scale
 |      2
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class DenseUnionType in module pyarrow.lib

class DenseUnionType(UnionType)
 |  Concrete class for dense union types.
 |
 |  Examples
 |  --------
 |  Create an instance of a dense UnionType using ``pa.union``:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())],
 |  ...          mode=pa.lib.UnionMode_DENSE),
 |  (DenseUnionType(dense_union<a: fixed_size_binary[10]=0, b: string=1>),)
 |
 |  Create an instance of a dense UnionType using ``pa.dense_union``:
 |
 |  >>> pa.dense_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |  DenseUnionType(dense_union<a: fixed_size_binary[10]=0, b: string=1>)
 |
 |  Method resolution order:
 |      DenseUnionType
 |      UnionType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from UnionType:
 |
 |  __getitem__(...)
 |      UnionType.__getitem__(self, i)
 |
 |      Return a child field by its index.
 |
 |      Alias of ``field``.
 |
 |  __iter__(...)
 |      UnionType.__iter__(self)
 |
 |      Iterate over union members, in order.
 |
 |  __len__(...)
 |      UnionType.__len__(self)
 |
 |      Like num_fields().
 |
 |  __reduce__(self)
 |      UnionType.__reduce__(self)
 |
 |  field(self, i)
 |      UnionType.field(self, i) -> Field
 |
 |      Return a child field by its numeric index.
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |      >>> union[0]
 |      pyarrow.Field<a: fixed_size_binary[10]>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from UnionType:
 |
 |  mode
 |      The mode of the union ("dense" or "sparse").
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |      >>> union.mode
 |      'sparse'
 |
 |  type_codes
 |      The type code to indicate each data type in this union.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |      >>> union.type_codes
 |      [0, 1]
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class Device in module pyarrow.lib

class Device(_Weakrefable)
 |  Device()
 |
 |  Abstract interface for hardware devices
 |
 |  This object represents a device with access to some memory spaces.
 |  When handling a Buffer or raw memory address, it allows deciding in which
 |  context the raw memory address should be interpreted
 |  (e.g. CPU-accessible memory, or embedded memory on some particular GPU).
 |
 |  Method resolution order:
 |      Device
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      Device.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      Device.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  device_id
 |      A device ID to identify this device if there are multiple of this type.
 |
 |      If there is no "device_id" equivalent (such as for the main CPU device on
 |      non-numa systems) returns -1.
 |
 |  device_type
 |      Return the DeviceAllocationType of this device.
 |
 |  is_cpu
 |      Whether this device is the main CPU device.
 |
 |      This shorthand method is very useful when deciding whether a memory address
 |      is CPU-accessible.
 |
 |  type_name
 |      A shorthand for this device's type.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class DeviceAllocationType in module pyarrow.lib

class DeviceAllocationType(enum.IntFlag)
 |  DeviceAllocationType(*values)
 |
 |  Method resolution order:
 |      DeviceAllocationType
 |      enum.IntFlag
 |      builtins.int
 |      enum.ReprEnum
 |      enum.Flag
 |      enum.Enum
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __and__(self, other) from enum.Flag
 |
 |  __format__(self, format_spec, /) from builtins.int
 |      Convert to a string according to format_spec.
 |
 |  __invert__(self) from enum.Flag
 |
 |  __new__(cls, value) from enum.Enum
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  __or__(self, other) from enum.Flag
 |      Return self|value.
 |
 |  __rand__ = __and__(self, other)
 |
 |  __ror__ = __or__(self, other)
 |
 |  __rxor__ = __xor__(self, other)
 |
 |  __xor__(self, other) from enum.Flag
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  CPU = <DeviceAllocationType.CPU: 1>
 |
 |  CUDA = <DeviceAllocationType.CUDA: 2>
 |
 |  CUDA_HOST = <DeviceAllocationType.CUDA_HOST: 3>
 |
 |  CUDA_MANAGED = <DeviceAllocationType.CUDA_MANAGED: 13>
 |
 |  EXT_DEV = <DeviceAllocationType.EXT_DEV: 12>
 |
 |  HEXAGON = <DeviceAllocationType.HEXAGON: 16>
 |
 |  METAL = <DeviceAllocationType.METAL: 8>
 |
 |  ONEAPI = <DeviceAllocationType.ONEAPI: 14>
 |
 |  OPENCL = <DeviceAllocationType.OPENCL: 4>
 |
 |  ROCM = <DeviceAllocationType.ROCM: 10>
 |
 |  ROCM_HOST = <DeviceAllocationType.ROCM_HOST: 11>
 |
 |  VPI = <DeviceAllocationType.VPI: 9>
 |
 |  VULKAN = <DeviceAllocationType.VULKAN: 7>
 |
 |  WEBGPU = <DeviceAllocationType.WEBGPU: 15>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from enum.IntFlag:
 |
 |  __repr__(self) from enum.Flag
 |      Return repr(self).
 |
 |  __str__ = __repr__(self, /) from builtins.int
 |      Return repr(self).
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.int:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(self, value, /)
 |      Return self+value.
 |
 |  __bool__(self, /)
 |      True if self else False
 |
 |  __ceil__(...)
 |      Ceiling of an Integral returns itself.
 |
 |  __divmod__(self, value, /)
 |      Return divmod(self, value).
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __float__(self, /)
 |      float(self)
 |
 |  __floor__(...)
 |      Flooring an Integral returns itself.
 |
 |  __floordiv__(self, value, /)
 |      Return self//value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __getnewargs__(self, /)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __index__(self, /)
 |      Return self converted to an integer, if self is suitable for use as an index into a list.
 |
 |  __int__(self, /)
 |      int(self)
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(self, value, /)
 |      Return self<<value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mod__(self, value, /)
 |      Return self%value.
 |
 |  __mul__(self, value, /)
 |      Return self*value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __pos__(self, /)
 |      +self
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rdivmod__(self, value, /)
 |      Return divmod(value, self).
 |
 |  __rfloordiv__(self, value, /)
 |      Return value//self.
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmod__(self, value, /)
 |      Return value%self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __round__(...)
 |      Rounding an Integral returns itself.
 |
 |      Rounding with an ndigits argument also returns an integer.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(self, value, /)
 |      Return self>>value.
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __sizeof__(self, /)
 |      Returns size in memory, in bytes.
 |
 |  __sub__(self, value, /)
 |      Return self-value.
 |
 |  __truediv__(self, value, /)
 |      Return self/value.
 |
 |  __trunc__(...)
 |      Truncating an Integral returns itself.
 |
 |  as_integer_ratio(self, /)
 |      Return a pair of integers, whose ratio is equal to the original int.
 |
 |      The ratio is in lowest terms and has a positive denominator.
 |
 |      >>> (10).as_integer_ratio()
 |      (10, 1)
 |      >>> (-10).as_integer_ratio()
 |      (-10, 1)
 |      >>> (0).as_integer_ratio()
 |      (0, 1)
 |
 |  bit_count(self, /)
 |      Number of ones in the binary representation of the absolute value of self.
 |
 |      Also known as the population count.
 |
 |      >>> bin(13)
 |      '0b1101'
 |      >>> (13).bit_count()
 |      3
 |
 |  bit_length(self, /)
 |      Number of bits necessary to represent self in binary.
 |
 |      >>> bin(37)
 |      '0b100101'
 |      >>> (37).bit_length()
 |      6
 |
 |  conjugate(...)
 |      Returns self, the complex conjugate of any int.
 |
 |  is_integer(self, /)
 |      Returns True. Exists for duck type compatibility with float.is_integer.
 |
 |  to_bytes(self, /, length=1, byteorder='big', *, signed=False)
 |      Return an array of bytes representing an integer.
 |
 |      length
 |        Length of bytes object to use.  An OverflowError is raised if the
 |        integer is not representable with the given number of bytes.  Default
 |        is length 1.
 |      byteorder
 |        The byte order used to represent the integer.  If byteorder is 'big',
 |        the most significant byte is at the beginning of the byte array.  If
 |        byteorder is 'little', the most significant byte is at the end of the
 |        byte array.  To request the native byte order of the host system, use
 |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
 |      signed
 |        Determines whether two's complement is used to represent the integer.
 |        If signed is False and a negative integer is given, an OverflowError
 |        is raised.
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from builtins.int:
 |
 |  from_bytes(bytes, byteorder='big', *, signed=False)
 |      Return the integer represented by the given array of bytes.
 |
 |      bytes
 |        Holds the array of bytes to convert.  The argument must either
 |        support the buffer protocol or be an iterable object producing bytes.
 |        Bytes and bytearray are examples of built-in objects that support the
 |        buffer protocol.
 |      byteorder
 |        The byte order used to represent the integer.  If byteorder is 'big',
 |        the most significant byte is at the beginning of the byte array.  If
 |        byteorder is 'little', the most significant byte is at the end of the
 |        byte array.  To request the native byte order of the host system, use
 |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
 |      signed
 |        Indicates whether two's complement is used to represent the integer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.int:
 |
 |  denominator
 |      the denominator of a rational number in lowest terms
 |
 |  imag
 |      the imaginary part of a complex number
 |
 |  numerator
 |      the numerator of a rational number in lowest terms
 |
 |  real
 |      the real part of a complex number
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from enum.Flag:
 |
 |  __contains__(self, other)
 |      Returns True if self has at least the same flags set as other.
 |
 |  __iter__(self)
 |      Returns flags in definition order.
 |
 |  __len__(self)
 |      Return the number of members (no aliases)
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from enum.Enum:
 |
 |  __dir__(self)
 |      Returns public methods and other interesting attributes.
 |
 |  __init__(self, *args, **kwds)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce_ex__(self, proto)
 |      Helper for pickle.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from enum.Enum:
 |
 |  name
 |      The name of the Enum member.
 |
 |  value
 |      The value of the Enum member.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from enum.EnumType:
 |
 |  __getitem__(name)
 |      Return the member matching `name`.
 |
 |  ----------------------------------------------------------------------
 |  Readonly properties inherited from enum.EnumType:
 |
 |  __members__
 |      Returns a mapping of member name->value.
 |
 |      This mapping lists all enum members, including aliases. Note that this
 |      is a read-only view of the internal mapping.

Python Library Documentation: class DictionaryArray in module pyarrow.lib

class DictionaryArray(Array)
 |  Concrete class for dictionary-encoded Arrow arrays.
 |
 |  Method resolution order:
 |      DictionaryArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  dictionary_decode(self)
 |      DictionaryArray.dictionary_decode(self)
 |
 |      Decodes the DictionaryArray to an Array.
 |
 |  dictionary_encode(self)
 |      DictionaryArray.dictionary_encode(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_arrays(indices, dictionary, mask=None, ordered=False, from_pandas=False, safe=True, memory_pool=None)
 |      DictionaryArray.from_arrays(indices, dictionary, mask=None, bool ordered=False, bool from_pandas=False, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Construct a DictionaryArray from indices and values.
 |
 |      Parameters
 |      ----------
 |      indices : pyarrow.Array, numpy.ndarray or pandas.Series, int type
 |          Non-negative integers referencing the dictionary values by zero
 |          based index.
 |      dictionary : pyarrow.Array, ndarray or pandas.Series
 |          The array of values referenced by the indices.
 |      mask : ndarray or pandas.Series, bool type
 |          True values indicate that indices are actually null.
 |      ordered : bool, default False
 |          Set to True if the category values are ordered.
 |      from_pandas : bool, default False
 |          If True, the indices should be treated as though they originated in
 |          a pandas.Categorical (null encoded as -1).
 |      safe : bool, default True
 |          If True, check that the dictionary indices are in range.
 |      memory_pool : MemoryPool, default None
 |          For memory allocations, if required, otherwise uses default pool.
 |
 |      Returns
 |      -------
 |      dict_array : DictionaryArray
 |
 |  from_buffers(type, length, buffers, dictionary, null_count=-1, offset=0)
 |      DictionaryArray.from_buffers(DataType type, int64_t length, buffers, Array dictionary, int64_t null_count=-1, int64_t offset=0)
 |
 |      Construct a DictionaryArray from buffers.
 |
 |      Parameters
 |      ----------
 |      type : pyarrow.DataType
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing the indices array.
 |      dictionary : pyarrow.Array, ndarray or pandas.Series
 |          The array of values referenced by the indices.
 |      null_count : int, default -1
 |          The number of null entries in the indices array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |
 |      Returns
 |      -------
 |      dict_array : DictionaryArray
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  dictionary
 |
 |  indices
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class DictionaryMemo in module pyarrow.lib

class DictionaryMemo(_Weakrefable)
 |  Tracking container for dictionary-encoded fields.
 |
 |  Method resolution order:
 |      DictionaryMemo
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__ = __reduce_cython__(...)
 |      DictionaryMemo.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      DictionaryMemo.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.

Python Library Documentation: class DictionaryScalar in module pyarrow.lib

class DictionaryScalar(Scalar)
 |  Concrete class for dictionary-encoded scalars.
 |
 |  Method resolution order:
 |      DictionaryScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      DictionaryScalar.__reduce__(self)
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      DictionaryScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this encoded value as a Python object.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  dictionary
 |
 |  index
 |      Return this value's underlying index as a scalar.
 |
 |  value
 |      Return the encoded value as a scalar.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class DictionaryType in module pyarrow.lib

class DictionaryType(DataType)
 |  Concrete class for dictionary data types.
 |
 |  Examples
 |  --------
 |  Create an instance of dictionary type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.dictionary(pa.int64(), pa.utf8())
 |  DictionaryType(dictionary<values=string, indices=int64, ordered=0>)
 |
 |  Method resolution order:
 |      DictionaryType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      DictionaryType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  index_type
 |      The data type of dictionary indices (a signed integer type).
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.dictionary(pa.int16(), pa.utf8()).index_type
 |      DataType(int16)
 |
 |  ordered
 |      Whether the dictionary is ordered, i.e. whether the ordering of values
 |      in the dictionary is important.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.dictionary(pa.int64(), pa.utf8()).ordered
 |      False
 |
 |  value_type
 |      The dictionary value type.
 |
 |      The dictionary values are found in an instance of DictionaryArray.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.dictionary(pa.int16(), pa.utf8()).value_type
 |      DataType(string)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class DoubleArray in module pyarrow.lib

class DoubleArray(FloatingPointArray)
 |  Concrete class for Arrow arrays of float64 data type.
 |
 |  Method resolution order:
 |      DoubleArray
 |      FloatingPointArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class DoubleScalar in module pyarrow.lib

class DoubleScalar(Scalar)
 |  Concrete class for double scalars.
 |
 |  Method resolution order:
 |      DoubleScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __float__(self, /)
 |      float(self)
 |
 |  __int__(self, /)
 |      int(self)
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      DoubleScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python float.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class DurationArray in module pyarrow.lib

class DurationArray(NumericArray)
 |  Concrete class for Arrow arrays of duration data type.
 |
 |  Method resolution order:
 |      DurationArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class DurationScalar in module pyarrow.lib

class DurationScalar(Scalar)
 |  Concrete class for duration scalars.
 |
 |  Method resolution order:
 |      DurationScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      DurationScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Pandas Timedelta instance (if units are
 |      nanoseconds and pandas is available), otherwise as a Python
 |      datetime.timedelta instance.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class DurationType in module pyarrow.lib

class DurationType(DataType)
 |  Concrete class for duration data types.
 |
 |  Examples
 |  --------
 |  Create an instance of duration type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.duration('s')
 |  DurationType(duration[s])
 |
 |  Method resolution order:
 |      DurationType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  unit
 |      The duration unit ('s', 'ms', 'us' or 'ns').
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.duration('s')
 |      >>> t.unit
 |      's'
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      DataType.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class ExtensionArray in module pyarrow.lib

class ExtensionArray(Array)
 |  Concrete class for Arrow extension arrays.
 |
 |  Method resolution order:
 |      ExtensionArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_storage(typ, storage)
 |      ExtensionArray.from_storage(BaseExtensionType typ, Array storage)
 |
 |      Construct ExtensionArray from type and storage array.
 |
 |      Parameters
 |      ----------
 |      typ : DataType
 |          The extension type for the result array.
 |      storage : Array
 |          The underlying storage for the result array.
 |
 |      Returns
 |      -------
 |      ext_array : ExtensionArray
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  storage
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class ExtensionScalar in module pyarrow.lib

class ExtensionScalar(Scalar)
 |  Concrete class for Extension scalars.
 |
 |  Method resolution order:
 |      ExtensionScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      ExtensionScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this scalar as a Python object.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_storage(typ, value)
 |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
 |
 |      Construct ExtensionScalar from type and storage value.
 |
 |      Parameters
 |      ----------
 |      typ : DataType
 |          The extension type for the result scalar.
 |      value : object
 |          The storage value for the result scalar.
 |
 |      Returns
 |      -------
 |      ext_scalar : ExtensionScalar
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value
 |      Return storage value as a scalar.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class ExtensionType in module pyarrow.lib

class ExtensionType(BaseExtensionType)
 |  ExtensionType(DataType storage_type, extension_name)
 |
 |  Concrete base class for Python-defined extension types.
 |
 |  Parameters
 |  ----------
 |  storage_type : DataType
 |      The underlying storage type for the extension type.
 |  extension_name : str
 |      A unique name distinguishing this extension type. The name will be
 |      used when deserializing IPC data.
 |
 |  Examples
 |  --------
 |  Define a RationalType extension type subclassing ExtensionType:
 |
 |  >>> import pyarrow as pa
 |  >>> class RationalType(pa.ExtensionType):
 |  ...     def __init__(self, data_type: pa.DataType):
 |  ...         if not pa.types.is_integer(data_type):
 |  ...             raise TypeError(f"data_type must be an integer type not {data_type}")
 |  ...         super().__init__(
 |  ...             pa.struct(
 |  ...                 [
 |  ...                     ("numer", data_type),
 |  ...                     ("denom", data_type),
 |  ...                 ],
 |  ...             ),
 |  ...             # N.B. This name does _not_ reference `data_type` so deserialization
 |  ...             # will work for _any_ integer `data_type` after registration
 |  ...             "my_package.rational",
 |  ...         )
 |  ...     def __arrow_ext_serialize__(self) -> bytes:
 |  ...         # No parameters are necessary
 |  ...         return b""
 |  ...     @classmethod
 |  ...     def __arrow_ext_deserialize__(cls, storage_type, serialized):
 |  ...         # return an instance of this subclass
 |  ...         return RationalType(storage_type[0].type)
 |
 |  Register the extension type:
 |
 |  >>> pa.register_extension_type(RationalType(pa.int64()))
 |
 |  Create an instance of RationalType extension type:
 |
 |  >>> rational_type = RationalType(pa.int32())
 |
 |  Inspect the extension type:
 |
 |  >>> rational_type.extension_name
 |  'my_package.rational'
 |  >>> rational_type.storage_type
 |  StructType(struct<numer: int32, denom: int32>)
 |
 |  Wrap an array as an extension array:
 |
 |  >>> storage_array = pa.array(
 |  ...     [
 |  ...         {"numer": 10, "denom": 17},
 |  ...         {"numer": 20, "denom": 13},
 |  ...     ],
 |  ...     type=rational_type.storage_type
 |  ... )
 |  >>> rational_array = rational_type.wrap_array(storage_array)
 |  >>> rational_array
 |  <pyarrow.lib.ExtensionArray object at ...>
 |  -- is_valid: all not null
 |  -- child 0 type: int32
 |    [
 |      10,
 |      20
 |    ]
 |  -- child 1 type: int32
 |    [
 |      17,
 |      13
 |    ]
 |
 |  Or do the same with creating an ExtensionArray:
 |
 |  >>> rational_array = pa.ExtensionArray.from_storage(rational_type, storage_array)
 |  >>> rational_array
 |  <pyarrow.lib.ExtensionArray object at ...>
 |  -- is_valid: all not null
 |  -- child 0 type: int32
 |    [
 |      10,
 |      20
 |    ]
 |  -- child 1 type: int32
 |    [
 |      17,
 |      13
 |    ]
 |
 |  Unregister the extension type:
 |
 |  >>> pa.unregister_extension_type("my_package.rational")
 |
 |  Note that even though we registered the concrete type
 |  ``RationalType(pa.int64())``, PyArrow will be able to deserialize
 |  ``RationalType(integer_type)`` for any ``integer_type``, as the deserializer
 |  will reference the name ``my_package.rational`` and the ``@classmethod``
 |  ``__arrow_ext_deserialize__``.
 |
 |  Method resolution order:
 |      ExtensionType
 |      BaseExtensionType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_ext_class__(self)
 |      ExtensionType.__arrow_ext_class__(self)
 |
 |      Return an extension array class to be used for building or
 |      deserializing arrays with this extension type.
 |
 |      This method should return a subclass of the ExtensionArray class. By
 |      default, if not specialized in the extension implementation, an
 |      extension type array will be a built-in ExtensionArray instance.
 |
 |  __arrow_ext_scalar_class__(self)
 |      ExtensionType.__arrow_ext_scalar_class__(self)
 |
 |      Return an extension scalar class for building scalars with this
 |      extension type.
 |
 |      This method should return subclass of the ExtensionScalar class. By
 |      default, if not specialized in the extension implementation, an
 |      extension type scalar will be a built-in ExtensionScalar instance.
 |
 |  __arrow_ext_serialize__(self)
 |      ExtensionType.__arrow_ext_serialize__(self)
 |
 |      Serialized representation of metadata to reconstruct the type object.
 |
 |      This method should return a bytes object, and those serialized bytes
 |      are stored in the custom metadata of the Field holding an extension
 |      type in an IPC message.
 |      The bytes are passed to ``__arrow_ext_deserialize`` and should hold
 |      sufficient information to reconstruct the data type instance.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(...)
 |      Initialize an extension type instance.
 |
 |      This should be called at the end of the subclass'
 |      ``__init__`` method.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      ExtensionType.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  __arrow_ext_deserialize__(storage_type, serialized)
 |      ExtensionType.__arrow_ext_deserialize__(cls, storage_type, serialized)
 |
 |      Return an extension type instance from the storage type and serialized
 |      metadata.
 |
 |      This method should return an instance of the ExtensionType subclass
 |      that matches the passed storage type and serialized metadata (the
 |      return value of ``__arrow_ext_serialize__``).
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseExtensionType:
 |
 |  wrap_array(self, storage)
 |      BaseExtensionType.wrap_array(self, storage)
 |
 |      Wrap the given storage array as an extension array.
 |
 |      Parameters
 |      ----------
 |      storage : Array or ChunkedArray
 |
 |      Returns
 |      -------
 |      array : Array or ChunkedArray
 |          Extension array wrapping the storage array
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from BaseExtensionType:
 |
 |  bit_width
 |      The bit width of the extension type.
 |
 |  byte_width
 |      The byte width of the extension type.
 |
 |  extension_name
 |      The extension type name.
 |
 |  storage_type
 |      The underlying storage type.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class Field in module pyarrow.lib

class Field(_Weakrefable)
 |  Field()
 |
 |  A named field, with a data type, nullability, and optional metadata.
 |
 |  Notes
 |  -----
 |  Do not use this class's constructor directly; use pyarrow.field
 |
 |  Examples
 |  --------
 |  Create an instance of pyarrow.Field:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.field('key', pa.int32())
 |  pyarrow.Field<key: int32>
 |  >>> pa.field('key', pa.int32(), nullable=False)
 |  pyarrow.Field<key: int32 not null>
 |  >>> field = pa.field('key', pa.int32(),
 |  ...                  metadata={"key": "Something important"})
 |  >>> field
 |  pyarrow.Field<key: int32>
 |  >>> field.metadata
 |  {b'key': b'Something important'}
 |
 |  Use the field to create a struct type:
 |
 |  >>> pa.struct([field])
 |  StructType(struct<key: int32>)
 |
 |  Method resolution order:
 |      Field
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_c_schema__(self)
 |      Field.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      Field.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, check_metadata=False)
 |      Field.equals(self, Field other, bool check_metadata=False)
 |
 |      Test if this field is equal to the other
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Field
 |      check_metadata : bool, default False
 |          Whether Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> f1 = pa.field('key', pa.int32())
 |      >>> f2 = pa.field('key', pa.int32(), nullable=False)
 |      >>> f1.equals(f2)
 |      False
 |      >>> f1.equals(f1)
 |      True
 |
 |  flatten(self)
 |      Field.flatten(self)
 |
 |      Flatten this field.  If a struct field, individual child fields
 |      will be returned with their names prefixed by the parent's name.
 |
 |      Returns
 |      -------
 |      fields : List[pyarrow.Field]
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> f1 = pa.field('bar', pa.float64(), nullable=False)
 |      >>> f2 = pa.field('foo', pa.int32()).with_metadata({"key": "Something important"})
 |      >>> ff = pa.field('ff', pa.struct([f1, f2]), nullable=False)
 |
 |      Flatten a struct field:
 |
 |      >>> ff
 |      pyarrow.Field<ff: struct<bar: double not null, foo: int32> not null>
 |      >>> ff.flatten()
 |      [pyarrow.Field<ff.bar: double not null>, pyarrow.Field<ff.foo: int32>]
 |
 |  remove_metadata(self)
 |      Field.remove_metadata(self)
 |
 |      Create new field without metadata, if any
 |
 |      Returns
 |      -------
 |      field : pyarrow.Field
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> field = pa.field('key', pa.int32(),
 |      ...                  metadata={"key": "Something important"})
 |      >>> field.metadata
 |      {b'key': b'Something important'}
 |
 |      Create new field by removing the metadata from the existing one:
 |
 |      >>> field_new = field.remove_metadata()
 |      >>> field_new.metadata
 |
 |  with_metadata(self, metadata)
 |      Field.with_metadata(self, metadata)
 |
 |      Add metadata as dict of string keys and values to Field
 |
 |      Parameters
 |      ----------
 |      metadata : dict
 |          Keys and values must be string-like / coercible to bytes
 |
 |      Returns
 |      -------
 |      field : pyarrow.Field
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> field = pa.field('key', pa.int32())
 |
 |      Create new field by adding metadata to existing one:
 |
 |      >>> field_new = field.with_metadata({"key": "Something important"})
 |      >>> field_new
 |      pyarrow.Field<key: int32>
 |      >>> field_new.metadata
 |      {b'key': b'Something important'}
 |
 |  with_name(self, name)
 |      Field.with_name(self, name)
 |
 |      A copy of this field with the replaced name
 |
 |      Parameters
 |      ----------
 |      name : str
 |
 |      Returns
 |      -------
 |      field : pyarrow.Field
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> field = pa.field('key', pa.int32())
 |      >>> field
 |      pyarrow.Field<key: int32>
 |
 |      Create new field by replacing the name of an existing one:
 |
 |      >>> field_new = field.with_name('lock')
 |      >>> field_new
 |      pyarrow.Field<lock: int32>
 |
 |  with_nullable(self, nullable)
 |      Field.with_nullable(self, nullable)
 |
 |      A copy of this field with the replaced nullability
 |
 |      Parameters
 |      ----------
 |      nullable : bool
 |
 |      Returns
 |      -------
 |      field: pyarrow.Field
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> field = pa.field('key', pa.int32())
 |      >>> field
 |      pyarrow.Field<key: int32>
 |      >>> field.nullable
 |      True
 |
 |      Create new field by replacing the nullability of an existing one:
 |
 |      >>> field_new = field.with_nullable(False)
 |      >>> field_new
 |      pyarrow.Field<key: int32 not null>
 |      >>> field_new.nullable
 |      False
 |
 |  with_type(self, new_type)
 |      Field.with_type(self, DataType new_type)
 |
 |      A copy of this field with the replaced type
 |
 |      Parameters
 |      ----------
 |      new_type : pyarrow.DataType
 |
 |      Returns
 |      -------
 |      field : pyarrow.Field
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> field = pa.field('key', pa.int32())
 |      >>> field
 |      pyarrow.Field<key: int32>
 |
 |      Create new field by replacing type of an existing one:
 |
 |      >>> field_new = field.with_type(pa.int64())
 |      >>> field_new
 |      pyarrow.Field<key: int64>
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  metadata
 |      The field metadata (if any is set).
 |
 |      Returns
 |      -------
 |      metadata : dict or None
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> field = pa.field('key', pa.int32(),
 |      ...                  metadata={"key": "Something important"})
 |      >>> field.metadata
 |      {b'key': b'Something important'}
 |
 |  name
 |      The field name.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> field = pa.field('key', pa.int32())
 |      >>> field.name
 |      'key'
 |
 |  nullable
 |      The field nullability.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> f1 = pa.field('key', pa.int32())
 |      >>> f2 = pa.field('key', pa.int32(), nullable=False)
 |      >>> f1.nullable
 |      True
 |      >>> f2.nullable
 |      False
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class FixedShapeTensorArray in module pyarrow.lib

class FixedShapeTensorArray(ExtensionArray)
 |  Concrete class for fixed shape tensor extension arrays.
 |
 |  Examples
 |  --------
 |  Define the extension type for tensor array
 |
 |  >>> import pyarrow as pa
 |  >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), [2, 2])
 |
 |  Create an extension array
 |
 |  >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]]
 |  >>> storage = pa.array(arr, pa.list_(pa.int32(), 4))
 |  >>> pa.ExtensionArray.from_storage(tensor_type, storage)
 |  <pyarrow.lib.FixedShapeTensorArray object at ...>
 |  [
 |    [
 |      1,
 |      2,
 |      3,
 |      4
 |    ],
 |    [
 |      10,
 |      20,
 |      30,
 |      40
 |    ],
 |    [
 |      100,
 |      200,
 |      300,
 |      400
 |    ]
 |  ]
 |
 |  Method resolution order:
 |      FixedShapeTensorArray
 |      ExtensionArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  to_numpy_ndarray(self)
 |      FixedShapeTensorArray.to_numpy_ndarray(self)
 |
 |      Convert fixed shape tensor extension array to a multi-dimensional numpy.ndarray.
 |
 |      The resulting ndarray will have (ndim + 1) dimensions.
 |      The size of the first dimension will be the length of the fixed shape tensor array
 |      and the rest of the dimensions will match the permuted shape of the fixed
 |      shape tensor.
 |
 |      The conversion is zero-copy.
 |
 |      Returns
 |      -------
 |      numpy.ndarray
 |          Ndarray representing tensors in the fixed shape tensor array concatenated
 |          along the first dimension.
 |
 |  to_tensor(self)
 |      FixedShapeTensorArray.to_tensor(self)
 |
 |      Convert fixed shape tensor extension array to a pyarrow.Tensor.
 |
 |      The resulting Tensor will have (ndim + 1) dimensions.
 |      The size of the first dimension will be the length of the fixed shape tensor array
 |      and the rest of the dimensions will match the permuted shape of the fixed
 |      shape tensor.
 |
 |      The conversion is zero-copy.
 |
 |      Returns
 |      -------
 |      pyarrow.Tensor
 |          Tensor representing tensors in the fixed shape tensor array concatenated
 |          along the first dimension.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_numpy_ndarray(obj, dim_names=None)
 |      FixedShapeTensorArray.from_numpy_ndarray(obj, dim_names=None)
 |
 |      Convert numpy tensors (ndarrays) to a fixed shape tensor extension array.
 |      The first dimension of ndarray will become the length of the fixed
 |      shape tensor array.
 |      If input array data is not contiguous a copy will be made.
 |
 |      Parameters
 |      ----------
 |      obj : numpy.ndarray
 |      dim_names : tuple or list of strings, default None
 |          Explicit names to tensor dimensions.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> arr = np.array(
 |      ...         [[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]],
 |      ...         dtype=np.float32)
 |      >>> pa.FixedShapeTensorArray.from_numpy_ndarray(arr)
 |      <pyarrow.lib.FixedShapeTensorArray object at ...>
 |      [
 |        [
 |          1,
 |          2,
 |          3,
 |          4,
 |          5,
 |          6
 |        ],
 |        [
 |          1,
 |          2,
 |          3,
 |          4,
 |          5,
 |          6
 |        ]
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from ExtensionArray:
 |
 |  from_storage(typ, storage)
 |      ExtensionArray.from_storage(BaseExtensionType typ, Array storage)
 |
 |      Construct ExtensionArray from type and storage array.
 |
 |      Parameters
 |      ----------
 |      typ : DataType
 |          The extension type for the result array.
 |      storage : Array
 |          The underlying storage for the result array.
 |
 |      Returns
 |      -------
 |      ext_array : ExtensionArray
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ExtensionArray:
 |
 |  storage
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class FixedShapeTensorScalar in module pyarrow.lib

class FixedShapeTensorScalar(ExtensionScalar)
 |  Concrete class for fixed shape tensor extension scalar.
 |
 |  Method resolution order:
 |      FixedShapeTensorScalar
 |      ExtensionScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  to_numpy(self)
 |      FixedShapeTensorScalar.to_numpy(self)
 |
 |      Convert fixed shape tensor scalar to a numpy.ndarray.
 |
 |      The resulting ndarray's shape matches the permuted shape of the
 |      fixed shape tensor scalar.
 |      The conversion is zero-copy.
 |
 |      Returns
 |      -------
 |      numpy.ndarray
 |
 |  to_tensor(self)
 |      FixedShapeTensorScalar.to_tensor(self)
 |
 |      Convert fixed shape tensor extension scalar to a pyarrow.Tensor, using shape
 |      and strides derived from corresponding FixedShapeTensorType.
 |
 |      The conversion is zero-copy.
 |
 |      Returns
 |      -------
 |      pyarrow.Tensor
 |          Tensor represented stored in FixedShapeTensorScalar.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from ExtensionScalar:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      ExtensionScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this scalar as a Python object.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from ExtensionScalar:
 |
 |  from_storage(typ, value)
 |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
 |
 |      Construct ExtensionScalar from type and storage value.
 |
 |      Parameters
 |      ----------
 |      typ : DataType
 |          The extension type for the result scalar.
 |      value : object
 |          The storage value for the result scalar.
 |
 |      Returns
 |      -------
 |      ext_scalar : ExtensionScalar
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ExtensionScalar:
 |
 |  value
 |      Return storage value as a scalar.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class FixedShapeTensorType in module pyarrow.lib

class FixedShapeTensorType(BaseExtensionType)
 |  Concrete class for fixed shape tensor extension type.
 |
 |  Examples
 |  --------
 |  Create an instance of fixed shape tensor extension type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.fixed_shape_tensor(pa.int32(), [2, 2])
 |  FixedShapeTensorType(extension<arrow.fixed_shape_tensor[value_type=int32, shape=[2,2]]>)
 |
 |  Create an instance of fixed shape tensor extension type with
 |  permutation:
 |
 |  >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3),
 |  ...                                     permutation=[0, 2, 1])
 |  >>> tensor_type.permutation
 |  [0, 2, 1]
 |
 |  Method resolution order:
 |      FixedShapeTensorType
 |      BaseExtensionType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_ext_class__(self)
 |      FixedShapeTensorType.__arrow_ext_class__(self)
 |
 |  __arrow_ext_scalar_class__(self)
 |      FixedShapeTensorType.__arrow_ext_scalar_class__(self)
 |
 |  __reduce__(self)
 |      FixedShapeTensorType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  dim_names
 |      Explicit names of the dimensions.
 |
 |  permutation
 |      Indices of the dimensions ordering.
 |
 |  shape
 |      Shape of the tensors.
 |
 |  value_type
 |      Data type of an individual tensor.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseExtensionType:
 |
 |  wrap_array(self, storage)
 |      BaseExtensionType.wrap_array(self, storage)
 |
 |      Wrap the given storage array as an extension array.
 |
 |      Parameters
 |      ----------
 |      storage : Array or ChunkedArray
 |
 |      Returns
 |      -------
 |      array : Array or ChunkedArray
 |          Extension array wrapping the storage array
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from BaseExtensionType:
 |
 |  bit_width
 |      The bit width of the extension type.
 |
 |  byte_width
 |      The byte width of the extension type.
 |
 |  extension_name
 |      The extension type name.
 |
 |  storage_type
 |      The underlying storage type.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class FixedSizeBinaryArray in module pyarrow.lib

class FixedSizeBinaryArray(Array)
 |  Concrete class for Arrow arrays of a fixed-size binary data type.
 |
 |  Method resolution order:
 |      FixedSizeBinaryArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class FixedSizeBinaryScalar in module pyarrow.lib

class FixedSizeBinaryScalar(BinaryScalar)
 |  Method resolution order:
 |      FixedSizeBinaryScalar
 |      BinaryScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BinaryScalar:
 |
 |  __buffer__(self, flags, /)
 |      Return a buffer object that exposes the underlying memory of the object.
 |
 |  __bytes__(self)
 |      BinaryScalar.__bytes__(self)
 |
 |  as_buffer(self)
 |      BinaryScalar.as_buffer(self)
 |
 |      Return a view over this value as a Buffer object.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      BinaryScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python bytes.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class FixedSizeBinaryType in module pyarrow.lib

class FixedSizeBinaryType(DataType)
 |  Concrete class for fixed-size binary data types.
 |
 |  Examples
 |  --------
 |  Create an instance of fixed-size binary type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.binary(3)
 |  FixedSizeBinaryType(fixed_size_binary[3])
 |
 |  Method resolution order:
 |      FixedSizeBinaryType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      FixedSizeBinaryType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class FixedSizeBufferWriter in module pyarrow.lib

class FixedSizeBufferWriter(NativeFile)
 |  A stream writing to a Arrow buffer.
 |
 |  Examples
 |  --------
 |  Create a stream to write to ``pyarrow.Buffer``:
 |
 |  >>> import pyarrow as pa
 |  >>> buf = pa.allocate_buffer(5)
 |  >>> with pa.output_stream(buf) as stream:
 |  ...     stream.write(b'abcde')
 |  ...     stream
 |  ...
 |  5
 |  <pyarrow.FixedSizeBufferWriter closed=False own_file=False is_seekable=False is_writable=True is_readable=False>
 |
 |  Inspect the buffer:
 |
 |  >>> buf.to_pybytes()
 |  b'abcde'
 |  >>> buf
 |  <pyarrow.Buffer address=... size=5 is_cpu=True is_mutable=True>
 |
 |  Method resolution order:
 |      FixedSizeBufferWriter
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__ = __reduce_cython__(...)
 |      FixedSizeBufferWriter.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      FixedSizeBufferWriter.__setstate_cython__(self, __pyx_state)
 |
 |  set_memcopy_blocksize(self, blocksize)
 |      FixedSizeBufferWriter.set_memcopy_blocksize(self, int64_t blocksize)
 |
 |      Parameters
 |      ----------
 |      blocksize : int64
 |
 |  set_memcopy_threads(self, num_threads)
 |      FixedSizeBufferWriter.set_memcopy_threads(self, int num_threads)
 |
 |      Parameters
 |      ----------
 |      num_threads : int
 |
 |  set_memcopy_threshold(self, threshold)
 |      FixedSizeBufferWriter.set_memcopy_threshold(self, int64_t threshold)
 |
 |      Parameters
 |      ----------
 |      threshold : int64
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  fileno(self)
 |      NativeFile.fileno(self)
 |
 |      NOT IMPLEMENTED
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class FixedSizeListArray in module pyarrow.lib

class FixedSizeListArray(BaseListArray)
 |  Concrete class for Arrow arrays of a fixed size list data type.
 |
 |  Method resolution order:
 |      FixedSizeListArray
 |      BaseListArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_arrays(values, list_size=None, type=None, mask=None)
 |      FixedSizeListArray.from_arrays(values, list_size=None, DataType type=None, mask=None)
 |
 |      Construct FixedSizeListArray from array of values and a list length.
 |
 |      Parameters
 |      ----------
 |      values : Array (any type)
 |      list_size : int
 |          The fixed length of the lists.
 |      type : DataType, optional
 |          If not specified, a default ListType with the values' type and
 |          `list_size` length is used.
 |      mask : Array (boolean type), optional
 |          Indicate which values are null (True) or not null (False).
 |
 |
 |      Returns
 |      -------
 |      FixedSizeListArray
 |
 |      Examples
 |      --------
 |
 |      Create from a values array and a list size:
 |
 |      >>> import pyarrow as pa
 |      >>> values = pa.array([1, 2, 3, 4])
 |      >>> arr = pa.FixedSizeListArray.from_arrays(values, 2)
 |      >>> arr
 |      <pyarrow.lib.FixedSizeListArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        [
 |          3,
 |          4
 |        ]
 |      ]
 |
 |      Or create from a values array, list size and matching type:
 |
 |      >>> typ = pa.list_(pa.field("values", pa.int64()), 2)
 |      >>> arr = pa.FixedSizeListArray.from_arrays(values,type=typ)
 |      >>> arr
 |      <pyarrow.lib.FixedSizeListArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        [
 |          3,
 |          4
 |        ]
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  values
 |      Return the underlying array of values which backs the
 |      FixedSizeListArray ignoring the array's offset.
 |
 |      Note even null elements are included.
 |
 |      Compare with :meth:`flatten`, which returns only the non-null
 |      sub-list values.
 |
 |      Returns
 |      -------
 |      values : Array
 |
 |      See Also
 |      --------
 |      FixedSizeListArray.flatten : ...
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> array = pa.array(
 |      ...     [[1, 2], None, [3, None]],
 |      ...     type=pa.list_(pa.int32(), 2)
 |      ... )
 |      >>> array.values
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        1,
 |        2,
 |        null,
 |        null,
 |        3,
 |        null
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseListArray:
 |
 |  flatten(self, recursive=False)
 |      BaseListArray.flatten(self, recursive=False)
 |
 |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
 |      according to 'recursive'.
 |
 |      Note that this method is different from ``self.values`` in that
 |      it takes care of the slicing offset as well as null elements backed
 |      by non-empty sub-lists.
 |
 |      Parameters
 |      ----------
 |      recursive : bool, default False, optional
 |          When True, flatten this logical list-array recursively until an
 |          array of non-list values is formed.
 |
 |          When False, flatten only the top level.
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |      Examples
 |      --------
 |
 |      Basic logical list-array's flatten
 |
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, 3, 4]
 |      >>> offsets = [2, 1, 0]
 |      >>> sizes = [2, 2, 2]
 |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array
 |      <pyarrow.lib.ListViewArray object at ...>
 |      [
 |        [
 |          3,
 |          4
 |        ],
 |        [
 |          2,
 |          3
 |        ],
 |        [
 |          1,
 |          2
 |        ]
 |      ]
 |      >>> array.flatten()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        3,
 |        4,
 |        2,
 |        3,
 |        1,
 |        2
 |      ]
 |
 |      When recursive=True, nested list arrays are flattened recursively
 |      until an array of non-list values is formed.
 |
 |      >>> array = pa.array([
 |      ...    None,
 |      ...    [
 |      ...        [1, None, 2],
 |      ...        None,
 |      ...        [3, 4]
 |      ...    ],
 |      ...    [],
 |      ...    [
 |      ...        [],
 |      ...        [5, 6],
 |      ...        None
 |      ...    ],
 |      ...    [
 |      ...        [7, 8]
 |      ...    ]
 |      ... ], type=pa.list_(pa.list_(pa.int64())))
 |      >>> array.flatten(True)
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        null,
 |        2,
 |        3,
 |        4,
 |        5,
 |        6,
 |        7,
 |        8
 |      ]
 |
 |  value_lengths(self)
 |      BaseListArray.value_lengths(self)
 |
 |      Return integers array with values equal to the respective length of
 |      each list element. Null list values are null in the output.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_lengths()
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        3,
 |        0,
 |        null,
 |        1
 |      ]
 |
 |  value_parent_indices(self)
 |      BaseListArray.value_parent_indices(self)
 |
 |      Return array of same length as list child values array where each
 |      output value is the index of the parent list array slot containing each
 |      child value.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_parent_indices()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        0,
 |        0,
 |        0,
 |        3
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class FixedSizeListScalar in module pyarrow.lib

class FixedSizeListScalar(ListScalar)
 |  Method resolution order:
 |      FixedSizeListScalar
 |      ListScalar
 |      Scalar
 |      _Weakrefable
 |      collections.abc.Sequence
 |      collections.abc.Reversible
 |      collections.abc.Collection
 |      collections.abc.Sized
 |      collections.abc.Iterable
 |      collections.abc.Container
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from ListScalar:
 |
 |  __getitem__(...)
 |      ListScalar.__getitem__(self, i)
 |
 |      Return the value at the given index.
 |
 |  __iter__(...)
 |      ListScalar.__iter__(self)
 |
 |      Iterate over this element's values.
 |
 |  __len__(...)
 |      ListScalar.__len__(self)
 |
 |      Return the number of values.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      ListScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python list.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ListScalar:
 |
 |  values
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from collections.abc.Sequence:
 |
 |  __contains__(self, value)
 |
 |  __reversed__(self)
 |
 |  count(self, value)
 |      S.count(value) -> integer -- return number of occurrences of value
 |
 |  index(self, value, start=0, stop=None)
 |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
 |      Raises ValueError if the value is not present.
 |
 |      Supporting start and stop arguments is optional, but
 |      recommended.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from collections.abc.Sequence:
 |
 |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Reversible:
 |
 |  __subclasshook__(C)
 |      Abstract classes can override this to customize issubclass().
 |
 |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
 |      It should return True, False or NotImplemented.  If it returns
 |      NotImplemented, the normal algorithm is used.  Otherwise, it
 |      overrides the normal algorithm (and the outcome is cached).
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Iterable:
 |
 |  __class_getitem__ = GenericAlias(...)
 |      Represent a PEP 585 generic type
 |
 |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

Python Library Documentation: class FixedSizeListType in module pyarrow.lib

class FixedSizeListType(DataType)
 |  Concrete class for fixed size list data types.
 |
 |  Examples
 |  --------
 |  Create an instance of FixedSizeListType:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.list_(pa.int32(), 2)
 |  FixedSizeListType(fixed_size_list<item: int32>[2])
 |
 |  Method resolution order:
 |      FixedSizeListType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      FixedSizeListType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  list_size
 |      The size of the fixed size lists.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.list_(pa.int32(), 2).list_size
 |      2
 |
 |  value_field
 |      The field for list values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.list_(pa.int32(), 2).value_field
 |      pyarrow.Field<item: int32>
 |
 |  value_type
 |      The data type of large list values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.list_(pa.int32(), 2).value_type
 |      DataType(int32)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class FloatArray in module pyarrow.lib

class FloatArray(FloatingPointArray)
 |  Concrete class for Arrow arrays of float32 data type.
 |
 |  Method resolution order:
 |      FloatArray
 |      FloatingPointArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class FloatScalar in module pyarrow.lib

class FloatScalar(Scalar)
 |  Concrete class for float scalars.
 |
 |  Method resolution order:
 |      FloatScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __float__(self, /)
 |      float(self)
 |
 |  __int__(self, /)
 |      int(self)
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      FloatScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python float.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class FloatingPointArray in module pyarrow.lib

class FloatingPointArray(NumericArray)
 |  A base class for Arrow floating-point arrays.
 |
 |  Method resolution order:
 |      FloatingPointArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class HalfFloatArray in module pyarrow.lib

class HalfFloatArray(FloatingPointArray)
 |  Concrete class for Arrow arrays of float16 data type.
 |
 |  Method resolution order:
 |      HalfFloatArray
 |      FloatingPointArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class HalfFloatScalar in module pyarrow.lib

class HalfFloatScalar(Scalar)
 |  Concrete class for float scalars.
 |
 |  Method resolution order:
 |      HalfFloatScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __float__(self, /)
 |      float(self)
 |
 |  __int__(self, /)
 |      int(self)
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      HalfFloatScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python float.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Int16Array in module pyarrow.lib

class Int16Array(IntegerArray)
 |  Concrete class for Arrow arrays of int16 data type.
 |
 |  Method resolution order:
 |      Int16Array
 |      IntegerArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Int16Scalar in module pyarrow.lib

class Int16Scalar(Scalar)
 |  Concrete class for int16 scalars.
 |
 |  Method resolution order:
 |      Int16Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __index__(self, /)
 |      Return self converted to an integer, if self is suitable for use as an index into a list.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Int16Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python int.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Int32Array in module pyarrow.lib

class Int32Array(IntegerArray)
 |  Concrete class for Arrow arrays of int32 data type.
 |
 |  Method resolution order:
 |      Int32Array
 |      IntegerArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Int32Scalar in module pyarrow.lib

class Int32Scalar(Scalar)
 |  Concrete class for int32 scalars.
 |
 |  Method resolution order:
 |      Int32Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __index__(self, /)
 |      Return self converted to an integer, if self is suitable for use as an index into a list.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Int32Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python int.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Int64Array in module pyarrow.lib

class Int64Array(IntegerArray)
 |  Concrete class for Arrow arrays of int64 data type.
 |
 |  Method resolution order:
 |      Int64Array
 |      IntegerArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Int64Scalar in module pyarrow.lib

class Int64Scalar(Scalar)
 |  Concrete class for int64 scalars.
 |
 |  Method resolution order:
 |      Int64Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __index__(self, /)
 |      Return self converted to an integer, if self is suitable for use as an index into a list.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Int64Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python int.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Int8Array in module pyarrow.lib

class Int8Array(IntegerArray)
 |  Concrete class for Arrow arrays of int8 data type.
 |
 |  Method resolution order:
 |      Int8Array
 |      IntegerArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Int8Scalar in module pyarrow.lib

class Int8Scalar(Scalar)
 |  Concrete class for int8 scalars.
 |
 |  Method resolution order:
 |      Int8Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __index__(self, /)
 |      Return self converted to an integer, if self is suitable for use as an index into a list.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Int8Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python int.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class IntegerArray in module pyarrow.lib

class IntegerArray(NumericArray)
 |  A base class for Arrow integer arrays.
 |
 |  Method resolution order:
 |      IntegerArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class JsonArray in module pyarrow.lib

class JsonArray(ExtensionArray)
 |  Concrete class for Arrow arrays of JSON data type.
 |
 |  This does not guarantee that the JSON data actually
 |  is valid JSON.
 |
 |  Examples
 |  --------
 |  Define the extension type for JSON array
 |
 |  >>> import pyarrow as pa
 |  >>> json_type = pa.json_(pa.large_utf8())
 |
 |  Create an extension array
 |
 |  >>> arr = [None, '{ "id":30, "values":["a", "b"] }']
 |  >>> storage = pa.array(arr, pa.large_utf8())
 |  >>> pa.ExtensionArray.from_storage(json_type, storage)
 |  <pyarrow.lib.JsonArray object at ...>
 |  [
 |    null,
 |    "{ "id":30, "values":["a", "b"] }"
 |  ]
 |
 |  Method resolution order:
 |      JsonArray
 |      ExtensionArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Data descriptors defined here:
 |
 |  __dict__
 |      dictionary for instance variables
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from ExtensionArray:
 |
 |  __new__(*args, **kwargs) class method of pyarrow.lib.ExtensionArray
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_storage(typ, storage)
 |      ExtensionArray.from_storage(BaseExtensionType typ, Array storage)
 |
 |      Construct ExtensionArray from type and storage array.
 |
 |      Parameters
 |      ----------
 |      typ : DataType
 |          The extension type for the result array.
 |      storage : Array
 |          The underlying storage for the result array.
 |
 |      Returns
 |      -------
 |      ext_array : ExtensionArray
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ExtensionArray:
 |
 |  storage
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from ExtensionArray:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class JsonScalar in module pyarrow.lib

class JsonScalar(ExtensionScalar)
 |  Concrete class for JSON extension scalar.
 |
 |  Method resolution order:
 |      JsonScalar
 |      ExtensionScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Data descriptors defined here:
 |
 |  __dict__
 |      dictionary for instance variables
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from ExtensionScalar:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      ExtensionScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this scalar as a Python object.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from ExtensionScalar:
 |
 |  __new__(*args, **kwargs) class method of pyarrow.lib.ExtensionScalar
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_storage(typ, value)
 |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
 |
 |      Construct ExtensionScalar from type and storage value.
 |
 |      Parameters
 |      ----------
 |      typ : DataType
 |          The extension type for the result scalar.
 |      value : object
 |          The storage value for the result scalar.
 |
 |      Returns
 |      -------
 |      ext_scalar : ExtensionScalar
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ExtensionScalar:
 |
 |  value
 |      Return storage value as a scalar.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from ExtensionScalar:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class JsonType in module pyarrow.lib

class JsonType(BaseExtensionType)
 |  Concrete class for JSON extension type.
 |
 |  Examples
 |  --------
 |  Define the extension type for JSON array
 |
 |  >>> import pyarrow as pa
 |  >>> json_type = pa.json_(pa.large_utf8())
 |
 |  Create an extension array
 |
 |  >>> arr = [None, '{ "id":30, "values":["a", "b"] }']
 |  >>> storage = pa.array(arr, pa.large_utf8())
 |  >>> pa.ExtensionArray.from_storage(json_type, storage)
 |  <pyarrow.lib.JsonArray object at ...>
 |  [
 |    null,
 |    "{ "id":30, "values":["a", "b"] }"
 |  ]
 |
 |  Method resolution order:
 |      JsonType
 |      BaseExtensionType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_ext_class__(self)
 |      JsonType.__arrow_ext_class__(self)
 |
 |  __arrow_ext_scalar_class__(self)
 |      JsonType.__arrow_ext_scalar_class__(self)
 |
 |  __reduce__(self)
 |      JsonType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseExtensionType:
 |
 |  wrap_array(self, storage)
 |      BaseExtensionType.wrap_array(self, storage)
 |
 |      Wrap the given storage array as an extension array.
 |
 |      Parameters
 |      ----------
 |      storage : Array or ChunkedArray
 |
 |      Returns
 |      -------
 |      array : Array or ChunkedArray
 |          Extension array wrapping the storage array
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from BaseExtensionType:
 |
 |  bit_width
 |      The bit width of the extension type.
 |
 |  byte_width
 |      The byte width of the extension type.
 |
 |  extension_name
 |      The extension type name.
 |
 |  storage_type
 |      The underlying storage type.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class KeyValueMetadata in module pyarrow.lib

class KeyValueMetadata(_Metadata, collections.abc.Mapping)
 |  KeyValueMetadata(__arg0__=None, **kwargs)
 |
 |  KeyValueMetadata
 |
 |  Parameters
 |  ----------
 |  __arg0__ : dict
 |      A dict of the key-value metadata
 |  **kwargs : optional
 |      additional key-value metadata
 |
 |  Method resolution order:
 |      KeyValueMetadata
 |      _Metadata
 |      _Weakrefable
 |      collections.abc.Mapping
 |      collections.abc.Collection
 |      collections.abc.Sized
 |      collections.abc.Iterable
 |      collections.abc.Container
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __contains__(self, key, /)
 |      Return bool(key in self).
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      KeyValueMetadata.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other)
 |      KeyValueMetadata.equals(self, KeyValueMetadata other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.KeyValueMetadata
 |
 |      Returns
 |      -------
 |      bool
 |
 |  get_all(self, key)
 |      KeyValueMetadata.get_all(self, key)
 |
 |      Parameters
 |      ----------
 |      key : str
 |
 |      Returns
 |      -------
 |      list[byte]
 |
 |  items(self)
 |      KeyValueMetadata.items(self)
 |
 |  key(self, i)
 |      KeyValueMetadata.key(self, i)
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      byte
 |
 |  keys(self)
 |      KeyValueMetadata.keys(self)
 |
 |  to_dict(self)
 |      KeyValueMetadata.to_dict(self)
 |
 |      Convert KeyValueMetadata to dict. If a key occurs twice, the value for
 |      the first one is returned
 |
 |  value(self, i)
 |      KeyValueMetadata.value(self, i)
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      byte
 |
 |  values(self)
 |      KeyValueMetadata.values(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from collections.abc.Mapping:
 |
 |  get(self, key, default=None)
 |      D.get(k[,d]) -> D[k] if k in D, else d.  d defaults to None.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from collections.abc.Mapping:
 |
 |  __abstractmethods__ = frozenset({'__getitem__', '__iter__', '__len__'}...
 |
 |  __reversed__ = None
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Collection:
 |
 |  __subclasshook__(C)
 |      Abstract classes can override this to customize issubclass().
 |
 |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
 |      It should return True, False or NotImplemented.  If it returns
 |      NotImplemented, the normal algorithm is used.  Otherwise, it
 |      overrides the normal algorithm (and the outcome is cached).
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Iterable:
 |
 |  __class_getitem__ = GenericAlias(...)
 |      Represent a PEP 585 generic type
 |
 |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

Python Library Documentation: class LargeBinaryArray in module pyarrow.lib

class LargeBinaryArray(Array)
 |  Concrete class for Arrow arrays of large variable-sized binary data type.
 |
 |  Method resolution order:
 |      LargeBinaryArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  total_values_length
 |      The number of bytes from beginning to end of the data buffer addressed
 |      by the offsets of this LargeBinaryArray.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class LargeBinaryScalar in module pyarrow.lib

class LargeBinaryScalar(BinaryScalar)
 |  Method resolution order:
 |      LargeBinaryScalar
 |      BinaryScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BinaryScalar:
 |
 |  __buffer__(self, flags, /)
 |      Return a buffer object that exposes the underlying memory of the object.
 |
 |  __bytes__(self)
 |      BinaryScalar.__bytes__(self)
 |
 |  as_buffer(self)
 |      BinaryScalar.as_buffer(self)
 |
 |      Return a view over this value as a Buffer object.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      BinaryScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python bytes.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class LargeListArray in module pyarrow.lib

class LargeListArray(BaseListArray)
 |  Concrete class for Arrow arrays of a large list data type.
 |
 |  Identical to ListArray, but 64-bit offsets.
 |
 |  Method resolution order:
 |      LargeListArray
 |      BaseListArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_arrays(offsets, values, type=None, pool=None, mask=None)
 |      LargeListArray.from_arrays(offsets, values, DataType type=None, MemoryPool pool=None, mask=None)
 |
 |      Construct LargeListArray from arrays of int64 offsets and values.
 |
 |      Parameters
 |      ----------
 |      offsets : Array (int64 type)
 |      values : Array (any type)
 |      type : DataType, optional
 |          If not specified, a default ListType with the values' type is
 |          used.
 |      pool : MemoryPool, optional
 |      mask : Array (boolean type), optional
 |          Indicate which values are null (True) or not null (False).
 |
 |      Returns
 |      -------
 |      list_array : LargeListArray
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  offsets
 |      Return the list offsets as an int64 array.
 |
 |      The returned array will not have a validity bitmap, so you cannot
 |      expect to pass it to `LargeListArray.from_arrays` and get back the
 |      same list array if the original one has nulls.
 |
 |      Returns
 |      -------
 |      offsets : Int64Array
 |
 |  values
 |      Return the underlying array of values which backs the LargeListArray
 |      ignoring the array's offset.
 |
 |      If any of the list elements are null, but are backed by a
 |      non-empty sub-list, those elements will be included in the
 |      output.
 |
 |      Compare with :meth:`flatten`, which returns only the non-null
 |      values taking into consideration the array's offset.
 |
 |      Returns
 |      -------
 |      values : Array
 |
 |      See Also
 |      --------
 |      LargeListArray.flatten : ...
 |
 |      Examples
 |      --------
 |
 |      The values include null elements from the sub-lists:
 |
 |      >>> import pyarrow as pa
 |      >>> array = pa.array(
 |      ...     [[1, 2], None, [3, 4, None, 6]],
 |      ...     type=pa.large_list(pa.int32()),
 |      ... )
 |      >>> array.values
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        1,
 |        2,
 |        3,
 |        4,
 |        null,
 |        6
 |      ]
 |
 |      If an array is sliced, the slice still uses the same
 |      underlying data as the original array, just with an
 |      offset. Since values ignores the offset, the values are the
 |      same:
 |
 |      >>> sliced = array.slice(1, 2)
 |      >>> sliced
 |      <pyarrow.lib.LargeListArray object at ...>
 |      [
 |        null,
 |        [
 |          3,
 |          4,
 |          null,
 |          6
 |        ]
 |      ]
 |      >>> sliced.values
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        1,
 |        2,
 |        3,
 |        4,
 |        null,
 |        6
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseListArray:
 |
 |  flatten(self, recursive=False)
 |      BaseListArray.flatten(self, recursive=False)
 |
 |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
 |      according to 'recursive'.
 |
 |      Note that this method is different from ``self.values`` in that
 |      it takes care of the slicing offset as well as null elements backed
 |      by non-empty sub-lists.
 |
 |      Parameters
 |      ----------
 |      recursive : bool, default False, optional
 |          When True, flatten this logical list-array recursively until an
 |          array of non-list values is formed.
 |
 |          When False, flatten only the top level.
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |      Examples
 |      --------
 |
 |      Basic logical list-array's flatten
 |
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, 3, 4]
 |      >>> offsets = [2, 1, 0]
 |      >>> sizes = [2, 2, 2]
 |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array
 |      <pyarrow.lib.ListViewArray object at ...>
 |      [
 |        [
 |          3,
 |          4
 |        ],
 |        [
 |          2,
 |          3
 |        ],
 |        [
 |          1,
 |          2
 |        ]
 |      ]
 |      >>> array.flatten()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        3,
 |        4,
 |        2,
 |        3,
 |        1,
 |        2
 |      ]
 |
 |      When recursive=True, nested list arrays are flattened recursively
 |      until an array of non-list values is formed.
 |
 |      >>> array = pa.array([
 |      ...    None,
 |      ...    [
 |      ...        [1, None, 2],
 |      ...        None,
 |      ...        [3, 4]
 |      ...    ],
 |      ...    [],
 |      ...    [
 |      ...        [],
 |      ...        [5, 6],
 |      ...        None
 |      ...    ],
 |      ...    [
 |      ...        [7, 8]
 |      ...    ]
 |      ... ], type=pa.list_(pa.list_(pa.int64())))
 |      >>> array.flatten(True)
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        null,
 |        2,
 |        3,
 |        4,
 |        5,
 |        6,
 |        7,
 |        8
 |      ]
 |
 |  value_lengths(self)
 |      BaseListArray.value_lengths(self)
 |
 |      Return integers array with values equal to the respective length of
 |      each list element. Null list values are null in the output.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_lengths()
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        3,
 |        0,
 |        null,
 |        1
 |      ]
 |
 |  value_parent_indices(self)
 |      BaseListArray.value_parent_indices(self)
 |
 |      Return array of same length as list child values array where each
 |      output value is the index of the parent list array slot containing each
 |      child value.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_parent_indices()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        0,
 |        0,
 |        0,
 |        3
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class LargeListScalar in module pyarrow.lib

class LargeListScalar(ListScalar)
 |  Method resolution order:
 |      LargeListScalar
 |      ListScalar
 |      Scalar
 |      _Weakrefable
 |      collections.abc.Sequence
 |      collections.abc.Reversible
 |      collections.abc.Collection
 |      collections.abc.Sized
 |      collections.abc.Iterable
 |      collections.abc.Container
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from ListScalar:
 |
 |  __getitem__(...)
 |      ListScalar.__getitem__(self, i)
 |
 |      Return the value at the given index.
 |
 |  __iter__(...)
 |      ListScalar.__iter__(self)
 |
 |      Iterate over this element's values.
 |
 |  __len__(...)
 |      ListScalar.__len__(self)
 |
 |      Return the number of values.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      ListScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python list.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ListScalar:
 |
 |  values
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from collections.abc.Sequence:
 |
 |  __contains__(self, value)
 |
 |  __reversed__(self)
 |
 |  count(self, value)
 |      S.count(value) -> integer -- return number of occurrences of value
 |
 |  index(self, value, start=0, stop=None)
 |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
 |      Raises ValueError if the value is not present.
 |
 |      Supporting start and stop arguments is optional, but
 |      recommended.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from collections.abc.Sequence:
 |
 |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Reversible:
 |
 |  __subclasshook__(C)
 |      Abstract classes can override this to customize issubclass().
 |
 |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
 |      It should return True, False or NotImplemented.  If it returns
 |      NotImplemented, the normal algorithm is used.  Otherwise, it
 |      overrides the normal algorithm (and the outcome is cached).
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Iterable:
 |
 |  __class_getitem__ = GenericAlias(...)
 |      Represent a PEP 585 generic type
 |
 |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

Python Library Documentation: class LargeListType in module pyarrow.lib

class LargeListType(DataType)
 |  Concrete class for large list data types
 |  (like ListType, but with 64-bit offsets).
 |
 |  Examples
 |  --------
 |  Create an instance of LargeListType:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.large_list(pa.string())
 |  LargeListType(large_list<item: string>)
 |
 |  Method resolution order:
 |      LargeListType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      LargeListType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value_field
 |
 |  value_type
 |      The data type of large list values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.large_list(pa.string()).value_type
 |      DataType(string)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class LargeListViewArray in module pyarrow.lib

class LargeListViewArray(BaseListArray)
 |  Concrete class for Arrow arrays of a large list view data type.
 |
 |  Identical to ListViewArray, but with 64-bit offsets.
 |
 |  Method resolution order:
 |      LargeListViewArray
 |      BaseListArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_arrays(offsets, sizes, values, type=None, pool=None, mask=None)
 |      LargeListViewArray.from_arrays(offsets, sizes, values, DataType type=None, MemoryPool pool=None, mask=None)
 |
 |      Construct LargeListViewArray from arrays of int64 offsets and values.
 |
 |      Parameters
 |      ----------
 |      offsets : Array (int64 type)
 |      sizes : Array (int64 type)
 |      values : Array (any type)
 |      type : DataType, optional
 |          If not specified, a default ListType with the values' type is
 |          used.
 |      pool : MemoryPool, optional
 |      mask : Array (boolean type), optional
 |          Indicate which values are null (True) or not null (False).
 |
 |      Returns
 |      -------
 |      list_view_array : LargeListViewArray
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> values = pa.array([1, 2, 3, 4])
 |      >>> offsets = pa.array([0, 1, 2])
 |      >>> sizes = pa.array([2, 2, 2])
 |      >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values)
 |      <pyarrow.lib.LargeListViewArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        [
 |          2,
 |          3
 |        ],
 |        [
 |          3,
 |          4
 |        ]
 |      ]
 |      >>> # use a null mask to represent null values
 |      >>> mask = pa.array([False, True, False])
 |      >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values, mask=mask)
 |      <pyarrow.lib.LargeListViewArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        null,
 |        [
 |          3,
 |          4
 |        ]
 |      ]
 |      >>> # null values can be defined in either offsets or sizes arrays
 |      >>> # WARNING: this will result in a copy of the offsets or sizes arrays
 |      >>> offsets = pa.array([0, None, 2])
 |      >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values)
 |      <pyarrow.lib.LargeListViewArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        null,
 |        [
 |          3,
 |          4
 |        ]
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  offsets
 |      Return the list view offsets as an int64 array.
 |
 |      The returned array will not have a validity bitmap, so you cannot
 |      expect to pass it to `LargeListViewArray.from_arrays` and get back the
 |      same list array if the original one has nulls.
 |
 |      Returns
 |      -------
 |      offsets : Int64Array
 |
 |      Examples
 |      --------
 |
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, None, 3, 4]
 |      >>> offsets = [0, 0, 1]
 |      >>> sizes = [2, 0, 4]
 |      >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array.offsets
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        0,
 |        0,
 |        1
 |      ]
 |
 |  sizes
 |      Return the list view sizes as an int64 array.
 |
 |      The returned array will not have a validity bitmap, so you cannot
 |      expect to pass it to `LargeListViewArray.from_arrays` and get back the
 |      same list array if the original one has nulls.
 |
 |      Returns
 |      -------
 |      sizes : Int64Array
 |
 |      Examples
 |      --------
 |
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, None, 3, 4]
 |      >>> offsets = [0, 0, 1]
 |      >>> sizes = [2, 0, 4]
 |      >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array.sizes
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        2,
 |        0,
 |        4
 |      ]
 |
 |  values
 |      Return the underlying array of values which backs the LargeListArray
 |      ignoring the array's offset.
 |
 |      The values array may be out of order and/or contain additional values
 |      that are not found in the logical representation of the array. The only
 |      guarantee is that each non-null value in the ListView Array is contiguous.
 |
 |      Compare with :meth:`flatten`, which returns only the non-null
 |      values taking into consideration the array's order and offset.
 |
 |      Returns
 |      -------
 |      values : Array
 |
 |      See Also
 |      --------
 |      LargeListArray.flatten : ...
 |
 |      Examples
 |      --------
 |
 |      The values include null elements from sub-lists:
 |
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, None, 3, 4]
 |      >>> offsets = [0, 0, 1]
 |      >>> sizes = [2, 0, 4]
 |      >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array
 |      <pyarrow.lib.LargeListViewArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        [],
 |        [
 |          2,
 |          null,
 |          3,
 |          4
 |        ]
 |      ]
 |      >>> array.values
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        2,
 |        null,
 |        3,
 |        4
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseListArray:
 |
 |  flatten(self, recursive=False)
 |      BaseListArray.flatten(self, recursive=False)
 |
 |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
 |      according to 'recursive'.
 |
 |      Note that this method is different from ``self.values`` in that
 |      it takes care of the slicing offset as well as null elements backed
 |      by non-empty sub-lists.
 |
 |      Parameters
 |      ----------
 |      recursive : bool, default False, optional
 |          When True, flatten this logical list-array recursively until an
 |          array of non-list values is formed.
 |
 |          When False, flatten only the top level.
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |      Examples
 |      --------
 |
 |      Basic logical list-array's flatten
 |
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, 3, 4]
 |      >>> offsets = [2, 1, 0]
 |      >>> sizes = [2, 2, 2]
 |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array
 |      <pyarrow.lib.ListViewArray object at ...>
 |      [
 |        [
 |          3,
 |          4
 |        ],
 |        [
 |          2,
 |          3
 |        ],
 |        [
 |          1,
 |          2
 |        ]
 |      ]
 |      >>> array.flatten()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        3,
 |        4,
 |        2,
 |        3,
 |        1,
 |        2
 |      ]
 |
 |      When recursive=True, nested list arrays are flattened recursively
 |      until an array of non-list values is formed.
 |
 |      >>> array = pa.array([
 |      ...    None,
 |      ...    [
 |      ...        [1, None, 2],
 |      ...        None,
 |      ...        [3, 4]
 |      ...    ],
 |      ...    [],
 |      ...    [
 |      ...        [],
 |      ...        [5, 6],
 |      ...        None
 |      ...    ],
 |      ...    [
 |      ...        [7, 8]
 |      ...    ]
 |      ... ], type=pa.list_(pa.list_(pa.int64())))
 |      >>> array.flatten(True)
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        null,
 |        2,
 |        3,
 |        4,
 |        5,
 |        6,
 |        7,
 |        8
 |      ]
 |
 |  value_lengths(self)
 |      BaseListArray.value_lengths(self)
 |
 |      Return integers array with values equal to the respective length of
 |      each list element. Null list values are null in the output.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_lengths()
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        3,
 |        0,
 |        null,
 |        1
 |      ]
 |
 |  value_parent_indices(self)
 |      BaseListArray.value_parent_indices(self)
 |
 |      Return array of same length as list child values array where each
 |      output value is the index of the parent list array slot containing each
 |      child value.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_parent_indices()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        0,
 |        0,
 |        0,
 |        3
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class LargeListViewScalar in module pyarrow.lib

class LargeListViewScalar(ListScalar)
 |  Method resolution order:
 |      LargeListViewScalar
 |      ListScalar
 |      Scalar
 |      _Weakrefable
 |      collections.abc.Sequence
 |      collections.abc.Reversible
 |      collections.abc.Collection
 |      collections.abc.Sized
 |      collections.abc.Iterable
 |      collections.abc.Container
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from ListScalar:
 |
 |  __getitem__(...)
 |      ListScalar.__getitem__(self, i)
 |
 |      Return the value at the given index.
 |
 |  __iter__(...)
 |      ListScalar.__iter__(self)
 |
 |      Iterate over this element's values.
 |
 |  __len__(...)
 |      ListScalar.__len__(self)
 |
 |      Return the number of values.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      ListScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python list.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ListScalar:
 |
 |  values
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from collections.abc.Sequence:
 |
 |  __contains__(self, value)
 |
 |  __reversed__(self)
 |
 |  count(self, value)
 |      S.count(value) -> integer -- return number of occurrences of value
 |
 |  index(self, value, start=0, stop=None)
 |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
 |      Raises ValueError if the value is not present.
 |
 |      Supporting start and stop arguments is optional, but
 |      recommended.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from collections.abc.Sequence:
 |
 |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Reversible:
 |
 |  __subclasshook__(C)
 |      Abstract classes can override this to customize issubclass().
 |
 |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
 |      It should return True, False or NotImplemented.  If it returns
 |      NotImplemented, the normal algorithm is used.  Otherwise, it
 |      overrides the normal algorithm (and the outcome is cached).
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Iterable:
 |
 |  __class_getitem__ = GenericAlias(...)
 |      Represent a PEP 585 generic type
 |
 |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

Python Library Documentation: class LargeListViewType in module pyarrow.lib

class LargeListViewType(DataType)
 |  Concrete class for large list view data types
 |  (like ListViewType, but with 64-bit offsets).
 |
 |  Examples
 |  --------
 |  Create an instance of LargeListViewType:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.large_list_view(pa.string())
 |  LargeListViewType(large_list_view<item: string>)
 |
 |  Method resolution order:
 |      LargeListViewType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      LargeListViewType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value_field
 |      The field for large list view values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.large_list_view(pa.string()).value_field
 |      pyarrow.Field<item: string>
 |
 |  value_type
 |      The data type of large list view values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.large_list_view(pa.string()).value_type
 |      DataType(string)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class LargeStringArray in module pyarrow.lib

class LargeStringArray(Array)
 |  Concrete class for Arrow arrays of large string (or utf8) data type.
 |
 |  Method resolution order:
 |      LargeStringArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_buffers(length, value_offsets, data, null_bitmap=None, null_count=-1, offset=0)
 |      LargeStringArray.from_buffers(int length, Buffer value_offsets, Buffer data, Buffer null_bitmap=None, int null_count=-1, int offset=0)
 |
 |      Construct a LargeStringArray from value_offsets and data buffers.
 |      If there are nulls in the data, also a null_bitmap and the matching
 |      null_count must be passed.
 |
 |      Parameters
 |      ----------
 |      length : int
 |      value_offsets : Buffer
 |      data : Buffer
 |      null_bitmap : Buffer, optional
 |      null_count : int, default 0
 |      offset : int, default 0
 |
 |      Returns
 |      -------
 |      string_array : StringArray
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class LargeStringScalar in module pyarrow.lib

class LargeStringScalar(StringScalar)
 |  Method resolution order:
 |      LargeStringScalar
 |      StringScalar
 |      BinaryScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from StringScalar:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      StringScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python string.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BinaryScalar:
 |
 |  __buffer__(self, flags, /)
 |      Return a buffer object that exposes the underlying memory of the object.
 |
 |  __bytes__(self)
 |      BinaryScalar.__bytes__(self)
 |
 |  as_buffer(self)
 |      BinaryScalar.as_buffer(self)
 |
 |      Return a view over this value as a Buffer object.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class ListArray in module pyarrow.lib

class ListArray(BaseListArray)
 |  Concrete class for Arrow arrays of a list data type.
 |
 |  Method resolution order:
 |      ListArray
 |      BaseListArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_arrays(offsets, values, type=None, pool=None, mask=None)
 |      ListArray.from_arrays(offsets, values, DataType type=None, MemoryPool pool=None, mask=None)
 |
 |      Construct ListArray from arrays of int32 offsets and values.
 |
 |      Parameters
 |      ----------
 |      offsets : Array (int32 type)
 |      values : Array (any type)
 |      type : DataType, optional
 |          If not specified, a default ListType with the values' type is
 |          used.
 |      pool : MemoryPool, optional
 |      mask : Array (boolean type), optional
 |          Indicate which values are null (True) or not null (False).
 |
 |      Returns
 |      -------
 |      list_array : ListArray
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> values = pa.array([1, 2, 3, 4])
 |      >>> offsets = pa.array([0, 2, 4])
 |      >>> pa.ListArray.from_arrays(offsets, values)
 |      <pyarrow.lib.ListArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        [
 |          3,
 |          4
 |        ]
 |      ]
 |      >>> # nulls in the offsets array become null lists
 |      >>> offsets = pa.array([0, None, 2, 4])
 |      >>> pa.ListArray.from_arrays(offsets, values)
 |      <pyarrow.lib.ListArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        null,
 |        [
 |          3,
 |          4
 |        ]
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  offsets
 |      Return the list offsets as an int32 array.
 |
 |      The returned array will not have a validity bitmap, so you cannot
 |      expect to pass it to `ListArray.from_arrays` and get back the same
 |      list array if the original one has nulls.
 |
 |      Returns
 |      -------
 |      offsets : Int32Array
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> array = pa.array([[1, 2], None, [3, 4, 5]])
 |      >>> array.offsets
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        0,
 |        2,
 |        2,
 |        5
 |      ]
 |
 |  values
 |      Return the underlying array of values which backs the ListArray
 |      ignoring the array's offset.
 |
 |      If any of the list elements are null, but are backed by a
 |      non-empty sub-list, those elements will be included in the
 |      output.
 |
 |      Compare with :meth:`flatten`, which returns only the non-null
 |      values taking into consideration the array's offset.
 |
 |      Returns
 |      -------
 |      values : Array
 |
 |      See Also
 |      --------
 |      ListArray.flatten : ...
 |
 |      Examples
 |      --------
 |
 |      The values include null elements from sub-lists:
 |
 |      >>> import pyarrow as pa
 |      >>> array = pa.array([[1, 2], None, [3, 4, None, 6]])
 |      >>> array.values
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        2,
 |        3,
 |        4,
 |        null,
 |        6
 |      ]
 |
 |      If an array is sliced, the slice still uses the same
 |      underlying data as the original array, just with an
 |      offset. Since values ignores the offset, the values are the
 |      same:
 |
 |      >>> sliced = array.slice(1, 2)
 |      >>> sliced
 |      <pyarrow.lib.ListArray object at ...>
 |      [
 |        null,
 |        [
 |          3,
 |          4,
 |          null,
 |          6
 |        ]
 |      ]
 |      >>> sliced.values
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        2,
 |        3,
 |        4,
 |        null,
 |        6
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseListArray:
 |
 |  flatten(self, recursive=False)
 |      BaseListArray.flatten(self, recursive=False)
 |
 |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
 |      according to 'recursive'.
 |
 |      Note that this method is different from ``self.values`` in that
 |      it takes care of the slicing offset as well as null elements backed
 |      by non-empty sub-lists.
 |
 |      Parameters
 |      ----------
 |      recursive : bool, default False, optional
 |          When True, flatten this logical list-array recursively until an
 |          array of non-list values is formed.
 |
 |          When False, flatten only the top level.
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |      Examples
 |      --------
 |
 |      Basic logical list-array's flatten
 |
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, 3, 4]
 |      >>> offsets = [2, 1, 0]
 |      >>> sizes = [2, 2, 2]
 |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array
 |      <pyarrow.lib.ListViewArray object at ...>
 |      [
 |        [
 |          3,
 |          4
 |        ],
 |        [
 |          2,
 |          3
 |        ],
 |        [
 |          1,
 |          2
 |        ]
 |      ]
 |      >>> array.flatten()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        3,
 |        4,
 |        2,
 |        3,
 |        1,
 |        2
 |      ]
 |
 |      When recursive=True, nested list arrays are flattened recursively
 |      until an array of non-list values is formed.
 |
 |      >>> array = pa.array([
 |      ...    None,
 |      ...    [
 |      ...        [1, None, 2],
 |      ...        None,
 |      ...        [3, 4]
 |      ...    ],
 |      ...    [],
 |      ...    [
 |      ...        [],
 |      ...        [5, 6],
 |      ...        None
 |      ...    ],
 |      ...    [
 |      ...        [7, 8]
 |      ...    ]
 |      ... ], type=pa.list_(pa.list_(pa.int64())))
 |      >>> array.flatten(True)
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        null,
 |        2,
 |        3,
 |        4,
 |        5,
 |        6,
 |        7,
 |        8
 |      ]
 |
 |  value_lengths(self)
 |      BaseListArray.value_lengths(self)
 |
 |      Return integers array with values equal to the respective length of
 |      each list element. Null list values are null in the output.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_lengths()
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        3,
 |        0,
 |        null,
 |        1
 |      ]
 |
 |  value_parent_indices(self)
 |      BaseListArray.value_parent_indices(self)
 |
 |      Return array of same length as list child values array where each
 |      output value is the index of the parent list array slot containing each
 |      child value.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_parent_indices()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        0,
 |        0,
 |        0,
 |        3
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class ListScalar in module pyarrow.lib

class ListScalar(Scalar, collections.abc.Sequence)
 |  Concrete class for list-like scalars.
 |
 |  Method resolution order:
 |      ListScalar
 |      Scalar
 |      _Weakrefable
 |      collections.abc.Sequence
 |      collections.abc.Reversible
 |      collections.abc.Collection
 |      collections.abc.Sized
 |      collections.abc.Iterable
 |      collections.abc.Container
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getitem__(...)
 |      ListScalar.__getitem__(self, i)
 |
 |      Return the value at the given index.
 |
 |  __iter__(...)
 |      ListScalar.__iter__(self)
 |
 |      Iterate over this element's values.
 |
 |  __len__(...)
 |      ListScalar.__len__(self)
 |
 |      Return the number of values.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      ListScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python list.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  values
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from collections.abc.Sequence:
 |
 |  __contains__(self, value)
 |
 |  __reversed__(self)
 |
 |  count(self, value)
 |      S.count(value) -> integer -- return number of occurrences of value
 |
 |  index(self, value, start=0, stop=None)
 |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
 |      Raises ValueError if the value is not present.
 |
 |      Supporting start and stop arguments is optional, but
 |      recommended.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from collections.abc.Sequence:
 |
 |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Reversible:
 |
 |  __subclasshook__(C)
 |      Abstract classes can override this to customize issubclass().
 |
 |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
 |      It should return True, False or NotImplemented.  If it returns
 |      NotImplemented, the normal algorithm is used.  Otherwise, it
 |      overrides the normal algorithm (and the outcome is cached).
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Iterable:
 |
 |  __class_getitem__ = GenericAlias(...)
 |      Represent a PEP 585 generic type
 |
 |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

Python Library Documentation: class ListType in module pyarrow.lib

class ListType(DataType)
 |  Concrete class for list data types.
 |
 |  Examples
 |  --------
 |  Create an instance of ListType:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.list_(pa.string())
 |  ListType(list<item: string>)
 |
 |  Method resolution order:
 |      ListType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      ListType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value_field
 |      The field for list values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.list_(pa.string()).value_field
 |      pyarrow.Field<item: string>
 |
 |  value_type
 |      The data type of list values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.list_(pa.string()).value_type
 |      DataType(string)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class ListViewArray in module pyarrow.lib

class ListViewArray(BaseListArray)
 |  Concrete class for Arrow arrays of a list view data type.
 |
 |  Method resolution order:
 |      ListViewArray
 |      BaseListArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_arrays(offsets, sizes, values, type=None, pool=None, mask=None)
 |      ListViewArray.from_arrays(offsets, sizes, values, DataType type=None, MemoryPool pool=None, mask=None)
 |
 |      Construct ListViewArray from arrays of int32 offsets, sizes, and values.
 |
 |      Parameters
 |      ----------
 |      offsets : Array (int32 type)
 |      sizes : Array (int32 type)
 |      values : Array (any type)
 |      type : DataType, optional
 |          If not specified, a default ListType with the values' type is
 |          used.
 |      pool : MemoryPool, optional
 |      mask : Array (boolean type), optional
 |          Indicate which values are null (True) or not null (False).
 |
 |      Returns
 |      -------
 |      list_view_array : ListViewArray
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> values = pa.array([1, 2, 3, 4])
 |      >>> offsets = pa.array([0, 1, 2])
 |      >>> sizes = pa.array([2, 2, 2])
 |      >>> pa.ListViewArray.from_arrays(offsets, sizes, values)
 |      <pyarrow.lib.ListViewArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        [
 |          2,
 |          3
 |        ],
 |        [
 |          3,
 |          4
 |        ]
 |      ]
 |      >>> # use a null mask to represent null values
 |      >>> mask = pa.array([False, True, False])
 |      >>> pa.ListViewArray.from_arrays(offsets, sizes, values, mask=mask)
 |      <pyarrow.lib.ListViewArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        null,
 |        [
 |          3,
 |          4
 |        ]
 |      ]
 |      >>> # null values can be defined in either offsets or sizes arrays
 |      >>> # WARNING: this will result in a copy of the offsets or sizes arrays
 |      >>> offsets = pa.array([0, None, 2])
 |      >>> pa.ListViewArray.from_arrays(offsets, sizes, values)
 |      <pyarrow.lib.ListViewArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        null,
 |        [
 |          3,
 |          4
 |        ]
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  offsets
 |      Return the list offsets as an int32 array.
 |
 |      The returned array will not have a validity bitmap, so you cannot
 |      expect to pass it to `ListViewArray.from_arrays` and get back the same
 |      list array if the original one has nulls.
 |
 |      Returns
 |      -------
 |      offsets : Int32Array
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, None, 3, 4]
 |      >>> offsets = [0, 0, 1]
 |      >>> sizes = [2, 0, 4]
 |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array.offsets
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        0,
 |        0,
 |        1
 |      ]
 |
 |  sizes
 |      Return the list sizes as an int32 array.
 |
 |      The returned array will not have a validity bitmap, so you cannot
 |      expect to pass it to `ListViewArray.from_arrays` and get back the same
 |      list array if the original one has nulls.
 |
 |      Returns
 |      -------
 |      sizes : Int32Array
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, None, 3, 4]
 |      >>> offsets = [0, 0, 1]
 |      >>> sizes = [2, 0, 4]
 |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array.sizes
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        2,
 |        0,
 |        4
 |      ]
 |
 |  values
 |      Return the underlying array of values which backs the ListViewArray
 |      ignoring the array's offset and sizes.
 |
 |      The values array may be out of order and/or contain additional values
 |      that are not found in the logical representation of the array. The only
 |      guarantee is that each non-null value in the ListView Array is contiguous.
 |
 |      Compare with :meth:`flatten`, which returns only the non-null
 |      values taking into consideration the array's order and offset.
 |
 |      Returns
 |      -------
 |      values : Array
 |
 |      Examples
 |      --------
 |      The values include null elements from sub-lists:
 |
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, None, 3, 4]
 |      >>> offsets = [0, 0, 1]
 |      >>> sizes = [2, 0, 4]
 |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array
 |      <pyarrow.lib.ListViewArray object at ...>
 |      [
 |        [
 |          1,
 |          2
 |        ],
 |        [],
 |        [
 |          2,
 |          null,
 |          3,
 |          4
 |        ]
 |      ]
 |      >>> array.values
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        2,
 |        null,
 |        3,
 |        4
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseListArray:
 |
 |  flatten(self, recursive=False)
 |      BaseListArray.flatten(self, recursive=False)
 |
 |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
 |      according to 'recursive'.
 |
 |      Note that this method is different from ``self.values`` in that
 |      it takes care of the slicing offset as well as null elements backed
 |      by non-empty sub-lists.
 |
 |      Parameters
 |      ----------
 |      recursive : bool, default False, optional
 |          When True, flatten this logical list-array recursively until an
 |          array of non-list values is formed.
 |
 |          When False, flatten only the top level.
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |      Examples
 |      --------
 |
 |      Basic logical list-array's flatten
 |
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, 3, 4]
 |      >>> offsets = [2, 1, 0]
 |      >>> sizes = [2, 2, 2]
 |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array
 |      <pyarrow.lib.ListViewArray object at ...>
 |      [
 |        [
 |          3,
 |          4
 |        ],
 |        [
 |          2,
 |          3
 |        ],
 |        [
 |          1,
 |          2
 |        ]
 |      ]
 |      >>> array.flatten()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        3,
 |        4,
 |        2,
 |        3,
 |        1,
 |        2
 |      ]
 |
 |      When recursive=True, nested list arrays are flattened recursively
 |      until an array of non-list values is formed.
 |
 |      >>> array = pa.array([
 |      ...    None,
 |      ...    [
 |      ...        [1, None, 2],
 |      ...        None,
 |      ...        [3, 4]
 |      ...    ],
 |      ...    [],
 |      ...    [
 |      ...        [],
 |      ...        [5, 6],
 |      ...        None
 |      ...    ],
 |      ...    [
 |      ...        [7, 8]
 |      ...    ]
 |      ... ], type=pa.list_(pa.list_(pa.int64())))
 |      >>> array.flatten(True)
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        null,
 |        2,
 |        3,
 |        4,
 |        5,
 |        6,
 |        7,
 |        8
 |      ]
 |
 |  value_lengths(self)
 |      BaseListArray.value_lengths(self)
 |
 |      Return integers array with values equal to the respective length of
 |      each list element. Null list values are null in the output.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_lengths()
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        3,
 |        0,
 |        null,
 |        1
 |      ]
 |
 |  value_parent_indices(self)
 |      BaseListArray.value_parent_indices(self)
 |
 |      Return array of same length as list child values array where each
 |      output value is the index of the parent list array slot containing each
 |      child value.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_parent_indices()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        0,
 |        0,
 |        0,
 |        3
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class ListViewScalar in module pyarrow.lib

class ListViewScalar(ListScalar)
 |  Method resolution order:
 |      ListViewScalar
 |      ListScalar
 |      Scalar
 |      _Weakrefable
 |      collections.abc.Sequence
 |      collections.abc.Reversible
 |      collections.abc.Collection
 |      collections.abc.Sized
 |      collections.abc.Iterable
 |      collections.abc.Container
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from ListScalar:
 |
 |  __getitem__(...)
 |      ListScalar.__getitem__(self, i)
 |
 |      Return the value at the given index.
 |
 |  __iter__(...)
 |      ListScalar.__iter__(self)
 |
 |      Iterate over this element's values.
 |
 |  __len__(...)
 |      ListScalar.__len__(self)
 |
 |      Return the number of values.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      ListScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python list.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ListScalar:
 |
 |  values
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from collections.abc.Sequence:
 |
 |  __contains__(self, value)
 |
 |  __reversed__(self)
 |
 |  count(self, value)
 |      S.count(value) -> integer -- return number of occurrences of value
 |
 |  index(self, value, start=0, stop=None)
 |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
 |      Raises ValueError if the value is not present.
 |
 |      Supporting start and stop arguments is optional, but
 |      recommended.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from collections.abc.Sequence:
 |
 |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Reversible:
 |
 |  __subclasshook__(C)
 |      Abstract classes can override this to customize issubclass().
 |
 |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
 |      It should return True, False or NotImplemented.  If it returns
 |      NotImplemented, the normal algorithm is used.  Otherwise, it
 |      overrides the normal algorithm (and the outcome is cached).
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Iterable:
 |
 |  __class_getitem__ = GenericAlias(...)
 |      Represent a PEP 585 generic type
 |
 |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

Python Library Documentation: class ListViewType in module pyarrow.lib

class ListViewType(DataType)
 |  Concrete class for list view data types.
 |
 |  Examples
 |  --------
 |  Create an instance of ListViewType:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.list_view(pa.string())
 |  ListViewType(list_view<item: string>)
 |
 |  Method resolution order:
 |      ListViewType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      ListViewType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value_field
 |      The field for list view values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.list_view(pa.string()).value_field
 |      pyarrow.Field<item: string>
 |
 |  value_type
 |      The data type of list view values.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.list_view(pa.string()).value_type
 |      DataType(string)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class LoggingMemoryPool in module pyarrow.lib

class LoggingMemoryPool(MemoryPool)
 |  LoggingMemoryPool()
 |
 |  Method resolution order:
 |      LoggingMemoryPool
 |      MemoryPool
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      LoggingMemoryPool.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      LoggingMemoryPool.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from MemoryPool:
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  bytes_allocated(self)
 |      MemoryPool.bytes_allocated(self)
 |
 |      Return the number of bytes that are currently allocated from this
 |      memory pool.
 |
 |  max_memory(self)
 |      MemoryPool.max_memory(self)
 |
 |      Return the peak memory allocation in this memory pool.
 |      This can be an approximate number in multi-threaded applications.
 |
 |      None is returned if the pool implementation doesn't know how to
 |      compute this number.
 |
 |  num_allocations(self)
 |      MemoryPool.num_allocations(self)
 |
 |      Return the number of allocations or reallocations that were made
 |      using this memory pool.
 |
 |  print_stats(self)
 |      MemoryPool.print_stats(self)
 |
 |      Print statistics about this memory pool.
 |
 |      The output format is implementation-specific. Not all memory pools
 |      implement this method.
 |
 |  release_unused(self)
 |      MemoryPool.release_unused(self)
 |
 |      Attempt to return to the OS any memory being held onto by the pool.
 |
 |      This function should not be called except potentially for
 |      benchmarking or debugging as it could be expensive and detrimental to
 |      performance.
 |
 |      This is best effort and may not have any effect on some memory pools
 |      or in some situations (e.g. fragmentation).
 |
 |  total_bytes_allocated(self)
 |      MemoryPool.total_bytes_allocated(self)
 |
 |      Return the total number of bytes that have been allocated from this
 |      memory pool.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from MemoryPool:
 |
 |  backend_name
 |      The name of the backend used by this MemoryPool (e.g. "jemalloc").

Python Library Documentation: class MapArray in module pyarrow.lib

class MapArray(ListArray)
 |  Concrete class for Arrow arrays of a map data type.
 |
 |  Method resolution order:
 |      MapArray
 |      ListArray
 |      BaseListArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_arrays(offsets, keys, items, type=None, pool=None, mask=None)
 |      MapArray.from_arrays(offsets, keys, items, DataType type=None, MemoryPool pool=None, mask=None)
 |
 |      Construct MapArray from arrays of int32 offsets and key, item arrays.
 |
 |      Parameters
 |      ----------
 |      offsets : array-like or sequence (int32 type)
 |      keys : array-like or sequence (any type)
 |      items : array-like or sequence (any type)
 |      type : DataType, optional
 |          If not specified, a default MapArray with the keys' and items' type is used.
 |      pool : MemoryPool
 |      mask : Array (boolean type), optional
 |          Indicate which values are null (True) or not null (False).
 |
 |      Returns
 |      -------
 |      map_array : MapArray
 |
 |      Examples
 |      --------
 |      First, let's understand the structure of our dataset when viewed in a rectangular data model.
 |      The total of 5 respondents answered the question "How much did you like the movie x?".
 |      The value -1 in the integer array means that the value is missing. The boolean array
 |      represents the null bitmask corresponding to the missing values in the integer array.
 |
 |      >>> import pyarrow as pa
 |      >>> movies_rectangular = np.ma.masked_array([
 |      ...     [10, -1, -1],
 |      ...     [8, 4, 5],
 |      ...     [-1, 10, 3],
 |      ...     [-1, -1, -1],
 |      ...     [-1, -1, -1]
 |      ... ],
 |      ... [
 |      ...     [False, True, True],
 |      ...     [False, False, False],
 |      ...     [True, False, False],
 |      ...     [True, True, True],
 |      ...     [True, True, True],
 |      ... ])
 |
 |      To represent the same data with the MapArray and from_arrays, the data is
 |      formed like this:
 |
 |      >>> offsets = [
 |      ...     0, #  -- row 1 start
 |      ...     1, #  -- row 2 start
 |      ...     4, #  -- row 3 start
 |      ...     6, #  -- row 4 start
 |      ...     6, #  -- row 5 start
 |      ...     6, #  -- row 5 end
 |      ... ]
 |      >>> movies = [
 |      ...     "Dark Knight", #  ---------------------------------- row 1
 |      ...     "Dark Knight", "Meet the Parents", "Superman", #  -- row 2
 |      ...     "Meet the Parents", "Superman", #  ----------------- row 3
 |      ... ]
 |      >>> likings = [
 |      ...     10, #  -------- row 1
 |      ...     8, 4, 5, #  --- row 2
 |      ...     10, 3 #  ------ row 3
 |      ... ]
 |      >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas()
 |      0                                  [(Dark Knight, 10)]
 |      1    [(Dark Knight, 8), (Meet the Parents, 4), (Sup...
 |      2              [(Meet the Parents, 10), (Superman, 3)]
 |      3                                                   []
 |      4                                                   []
 |      dtype: object
 |
 |      If the data in the empty rows needs to be marked as missing, it's possible
 |      to do so by modifying the offsets argument, so that we specify `None` as
 |      the starting positions of the rows we want marked as missing. The end row
 |      offset still has to refer to the existing value from keys (and values):
 |
 |      >>> offsets = [
 |      ...     0, #  ----- row 1 start
 |      ...     1, #  ----- row 2 start
 |      ...     4, #  ----- row 3 start
 |      ...     None, #  -- row 4 start
 |      ...     None, #  -- row 5 start
 |      ...     6, #  ----- row 5 end
 |      ... ]
 |      >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas()
 |      0                                  [(Dark Knight, 10)]
 |      1    [(Dark Knight, 8), (Meet the Parents, 4), (Sup...
 |      2              [(Meet the Parents, 10), (Superman, 3)]
 |      3                                                 None
 |      4                                                 None
 |      dtype: object
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  items
 |      Flattened array of items across all maps in array
 |
 |  keys
 |      Flattened array of keys across all maps in array
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ListArray:
 |
 |  offsets
 |      Return the list offsets as an int32 array.
 |
 |      The returned array will not have a validity bitmap, so you cannot
 |      expect to pass it to `ListArray.from_arrays` and get back the same
 |      list array if the original one has nulls.
 |
 |      Returns
 |      -------
 |      offsets : Int32Array
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> array = pa.array([[1, 2], None, [3, 4, 5]])
 |      >>> array.offsets
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        0,
 |        2,
 |        2,
 |        5
 |      ]
 |
 |  values
 |      Return the underlying array of values which backs the ListArray
 |      ignoring the array's offset.
 |
 |      If any of the list elements are null, but are backed by a
 |      non-empty sub-list, those elements will be included in the
 |      output.
 |
 |      Compare with :meth:`flatten`, which returns only the non-null
 |      values taking into consideration the array's offset.
 |
 |      Returns
 |      -------
 |      values : Array
 |
 |      See Also
 |      --------
 |      ListArray.flatten : ...
 |
 |      Examples
 |      --------
 |
 |      The values include null elements from sub-lists:
 |
 |      >>> import pyarrow as pa
 |      >>> array = pa.array([[1, 2], None, [3, 4, None, 6]])
 |      >>> array.values
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        2,
 |        3,
 |        4,
 |        null,
 |        6
 |      ]
 |
 |      If an array is sliced, the slice still uses the same
 |      underlying data as the original array, just with an
 |      offset. Since values ignores the offset, the values are the
 |      same:
 |
 |      >>> sliced = array.slice(1, 2)
 |      >>> sliced
 |      <pyarrow.lib.ListArray object at ...>
 |      [
 |        null,
 |        [
 |          3,
 |          4,
 |          null,
 |          6
 |        ]
 |      ]
 |      >>> sliced.values
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        2,
 |        3,
 |        4,
 |        null,
 |        6
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseListArray:
 |
 |  flatten(self, recursive=False)
 |      BaseListArray.flatten(self, recursive=False)
 |
 |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
 |      according to 'recursive'.
 |
 |      Note that this method is different from ``self.values`` in that
 |      it takes care of the slicing offset as well as null elements backed
 |      by non-empty sub-lists.
 |
 |      Parameters
 |      ----------
 |      recursive : bool, default False, optional
 |          When True, flatten this logical list-array recursively until an
 |          array of non-list values is formed.
 |
 |          When False, flatten only the top level.
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |      Examples
 |      --------
 |
 |      Basic logical list-array's flatten
 |
 |      >>> import pyarrow as pa
 |      >>> values = [1, 2, 3, 4]
 |      >>> offsets = [2, 1, 0]
 |      >>> sizes = [2, 2, 2]
 |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
 |      >>> array
 |      <pyarrow.lib.ListViewArray object at ...>
 |      [
 |        [
 |          3,
 |          4
 |        ],
 |        [
 |          2,
 |          3
 |        ],
 |        [
 |          1,
 |          2
 |        ]
 |      ]
 |      >>> array.flatten()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        3,
 |        4,
 |        2,
 |        3,
 |        1,
 |        2
 |      ]
 |
 |      When recursive=True, nested list arrays are flattened recursively
 |      until an array of non-list values is formed.
 |
 |      >>> array = pa.array([
 |      ...    None,
 |      ...    [
 |      ...        [1, None, 2],
 |      ...        None,
 |      ...        [3, 4]
 |      ...    ],
 |      ...    [],
 |      ...    [
 |      ...        [],
 |      ...        [5, 6],
 |      ...        None
 |      ...    ],
 |      ...    [
 |      ...        [7, 8]
 |      ...    ]
 |      ... ], type=pa.list_(pa.list_(pa.int64())))
 |      >>> array.flatten(True)
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        1,
 |        null,
 |        2,
 |        3,
 |        4,
 |        5,
 |        6,
 |        7,
 |        8
 |      ]
 |
 |  value_lengths(self)
 |      BaseListArray.value_lengths(self)
 |
 |      Return integers array with values equal to the respective length of
 |      each list element. Null list values are null in the output.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_lengths()
 |      <pyarrow.lib.Int32Array object at ...>
 |      [
 |        3,
 |        0,
 |        null,
 |        1
 |      ]
 |
 |  value_parent_indices(self)
 |      BaseListArray.value_parent_indices(self)
 |
 |      Return array of same length as list child values array where each
 |      output value is the index of the parent list array slot containing each
 |      child value.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
 |      ...                type=pa.list_(pa.int32()))
 |      >>> arr.value_parent_indices()
 |      <pyarrow.lib.Int64Array object at ...>
 |      [
 |        0,
 |        0,
 |        0,
 |        3
 |      ]
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class MapScalar in module pyarrow.lib

class MapScalar(ListScalar, collections.abc.Mapping)
 |  Concrete class for map scalars.
 |
 |  Method resolution order:
 |      MapScalar
 |      ListScalar
 |      Scalar
 |      _Weakrefable
 |      collections.abc.Sequence
 |      collections.abc.Reversible
 |      collections.abc.Mapping
 |      collections.abc.Collection
 |      collections.abc.Sized
 |      collections.abc.Iterable
 |      collections.abc.Container
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getitem__(...)
 |      MapScalar.__getitem__(self, i)
 |
 |      Return the value at the given index or key.
 |
 |  __iter__(...)
 |      MapScalar.__iter__(self)
 |
 |      Iterate over this element's values.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      MapScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python list or dict, depending on 'maps_as_pydicts'.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  keys(self)
 |      MapScalar.keys(self)
 |
 |      Return the keys of the map as a list.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from ListScalar:
 |
 |  __len__(...)
 |      ListScalar.__len__(self)
 |
 |      Return the number of values.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ListScalar:
 |
 |  values
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from collections.abc.Sequence:
 |
 |  __contains__(self, value)
 |
 |  __reversed__(self)
 |
 |  count(self, value)
 |      S.count(value) -> integer -- return number of occurrences of value
 |
 |  index(self, value, start=0, stop=None)
 |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
 |      Raises ValueError if the value is not present.
 |
 |      Supporting start and stop arguments is optional, but
 |      recommended.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from collections.abc.Sequence:
 |
 |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Reversible:
 |
 |  __subclasshook__(C)
 |      Abstract classes can override this to customize issubclass().
 |
 |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
 |      It should return True, False or NotImplemented.  If it returns
 |      NotImplemented, the normal algorithm is used.  Otherwise, it
 |      overrides the normal algorithm (and the outcome is cached).
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from collections.abc.Mapping:
 |
 |  get(self, key, default=None)
 |      D.get(k[,d]) -> D[k] if k in D, else d.  d defaults to None.
 |
 |  items(self)
 |      D.items() -> a set-like object providing a view on D's items
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Iterable:
 |
 |  __class_getitem__ = GenericAlias(...)
 |      Represent a PEP 585 generic type
 |
 |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

Python Library Documentation: class MapType in module pyarrow.lib

class MapType(DataType)
 |  Concrete class for map data types.
 |
 |  Examples
 |  --------
 |  Create an instance of MapType:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.map_(pa.string(), pa.int32())
 |  MapType(map<string, int32>)
 |  >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True)
 |  MapType(map<string, int32, keys_sorted>)
 |
 |  Method resolution order:
 |      MapType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      MapType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  item_field
 |      The field for items in the map entries.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.map_(pa.string(), pa.int32()).item_field
 |      pyarrow.Field<value: int32>
 |
 |  item_type
 |      The data type of items in the map entries.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.map_(pa.string(), pa.int32()).item_type
 |      DataType(int32)
 |
 |  key_field
 |      The field for keys in the map entries.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.map_(pa.string(), pa.int32()).key_field
 |      pyarrow.Field<key: string not null>
 |
 |  key_type
 |      The data type of keys in the map entries.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.map_(pa.string(), pa.int32()).key_type
 |      DataType(string)
 |
 |  keys_sorted
 |      Should the entries be sorted according to keys.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True).keys_sorted
 |      True
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class MemoryManager in module pyarrow.lib

class MemoryManager(_Weakrefable)
 |  MemoryManager()
 |
 |  An object that provides memory management primitives.
 |
 |  A MemoryManager is always tied to a particular Device instance.
 |  It can also have additional parameters (such as a MemoryPool to
 |  allocate CPU memory).
 |
 |  Method resolution order:
 |      MemoryManager
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      MemoryManager.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      MemoryManager.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  device
 |      The device this MemoryManager is tied to.
 |
 |  is_cpu
 |      Whether this MemoryManager is tied to the main CPU device.
 |
 |      This shorthand method is very useful when deciding whether a memory
 |      address is CPU-accessible.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class MemoryMappedFile in module pyarrow.lib

class MemoryMappedFile(NativeFile)
 |  A stream that represents a memory-mapped file.
 |
 |  Supports 'r', 'r+', 'w' modes.
 |
 |  Examples
 |  --------
 |  Create a new file with memory map:
 |
 |  >>> import pyarrow as pa
 |  >>> mmap = pa.create_memory_map('example_mmap.dat', 10)
 |  >>> mmap
 |  <pyarrow.MemoryMappedFile closed=False own_file=False is_seekable=True is_writable=True is_readable=True>
 |  >>> mmap.close()
 |
 |  Open an existing file with memory map:
 |
 |  >>> with pa.memory_map('example_mmap.dat') as mmap:
 |  ...     mmap
 |  ...
 |  <pyarrow.MemoryMappedFile closed=False own_file=False is_seekable=True is_writable=False is_readable=True>
 |
 |  Method resolution order:
 |      MemoryMappedFile
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__ = __reduce_cython__(...)
 |      MemoryMappedFile.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      MemoryMappedFile.__setstate_cython__(self, __pyx_state)
 |
 |  fileno(self)
 |      MemoryMappedFile.fileno(self)
 |
 |  resize(self, new_size)
 |      MemoryMappedFile.resize(self, new_size)
 |
 |      Resize the map and underlying file.
 |
 |      Parameters
 |      ----------
 |      new_size : new size in bytes
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  create(path, size)
 |      MemoryMappedFile.create(path, size)
 |
 |      Create a MemoryMappedFile
 |
 |      Parameters
 |      ----------
 |      path : str
 |          Where to create the file.
 |      size : int
 |          Size of the memory mapped file.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class MemoryPool in module pyarrow.lib

class MemoryPool(_Weakrefable)
 |  MemoryPool()
 |
 |  Base class for memory allocation.
 |
 |  Besides tracking its number of allocated bytes, a memory pool also
 |  takes care of the required 64-byte alignment for Arrow data.
 |
 |  Method resolution order:
 |      MemoryPool
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      MemoryPool.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      MemoryPool.__setstate_cython__(self, __pyx_state)
 |
 |  bytes_allocated(self)
 |      MemoryPool.bytes_allocated(self)
 |
 |      Return the number of bytes that are currently allocated from this
 |      memory pool.
 |
 |  max_memory(self)
 |      MemoryPool.max_memory(self)
 |
 |      Return the peak memory allocation in this memory pool.
 |      This can be an approximate number in multi-threaded applications.
 |
 |      None is returned if the pool implementation doesn't know how to
 |      compute this number.
 |
 |  num_allocations(self)
 |      MemoryPool.num_allocations(self)
 |
 |      Return the number of allocations or reallocations that were made
 |      using this memory pool.
 |
 |  print_stats(self)
 |      MemoryPool.print_stats(self)
 |
 |      Print statistics about this memory pool.
 |
 |      The output format is implementation-specific. Not all memory pools
 |      implement this method.
 |
 |  release_unused(self)
 |      MemoryPool.release_unused(self)
 |
 |      Attempt to return to the OS any memory being held onto by the pool.
 |
 |      This function should not be called except potentially for
 |      benchmarking or debugging as it could be expensive and detrimental to
 |      performance.
 |
 |      This is best effort and may not have any effect on some memory pools
 |      or in some situations (e.g. fragmentation).
 |
 |  total_bytes_allocated(self)
 |      MemoryPool.total_bytes_allocated(self)
 |
 |      Return the total number of bytes that have been allocated from this
 |      memory pool.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  backend_name
 |      The name of the backend used by this MemoryPool (e.g. "jemalloc").
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class Message in module pyarrow.lib

class Message(_Weakrefable)
 |  Message()
 |
 |  Container for an Arrow IPC message with metadata and optional body
 |
 |  Method resolution order:
 |      Message
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      Message.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      Message.__setstate_cython__(self, __pyx_state)
 |
 |  equals(self, other)
 |      Message.equals(self, Message other)
 |
 |      Returns True if the message contents (metadata and body) are identical
 |
 |      Parameters
 |      ----------
 |      other : Message
 |
 |      Returns
 |      -------
 |      are_equal : bool
 |
 |  serialize(self, alignment=8, memory_pool=None)
 |      Message.serialize(self, alignment=8, memory_pool=None)
 |
 |      Write message as encapsulated IPC message
 |
 |      Parameters
 |      ----------
 |      alignment : int, default 8
 |          Byte alignment for metadata and body
 |      memory_pool : MemoryPool, default None
 |          Uses default memory pool if not specified
 |
 |      Returns
 |      -------
 |      serialized : Buffer
 |
 |  serialize_to(self, sink, alignment=8, memory_pool=None)
 |      Message.serialize_to(self, NativeFile sink, alignment=8, memory_pool=None)
 |
 |      Write message to generic OutputStream
 |
 |      Parameters
 |      ----------
 |      sink : NativeFile
 |      alignment : int, default 8
 |          Byte alignment for metadata and body
 |      memory_pool : MemoryPool, default None
 |          Uses default memory pool if not specified
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  body
 |
 |  metadata
 |
 |  metadata_version
 |
 |  type

Python Library Documentation: class MessageReader in module pyarrow.lib

class MessageReader(_Weakrefable)
 |  MessageReader()
 |
 |  Interface for reading Message objects from some source (like an
 |  InputStream)
 |
 |  Method resolution order:
 |      MessageReader
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __reduce__ = __reduce_cython__(...)
 |      MessageReader.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      MessageReader.__setstate_cython__(self, __pyx_state)
 |
 |  read_next_message(self)
 |      MessageReader.read_next_message(self)
 |
 |      Read next Message from the stream.
 |
 |      Raises
 |      ------
 |      StopIteration
 |          At end of stream
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  open_stream(source)
 |      MessageReader.open_stream(source)
 |
 |      Open stream from source, if you want to use memory map use
 |      MemoryMappedFile as source.
 |
 |      Parameters
 |      ----------
 |      source : bytes/buffer-like, pyarrow.NativeFile, or file-like Python object
 |          A readable source, like an InputStream

Python Library Documentation: class MetadataVersion in module pyarrow.lib

class MetadataVersion(enum.IntFlag)
 |  MetadataVersion(*values)
 |
 |  Method resolution order:
 |      MetadataVersion
 |      enum.IntFlag
 |      builtins.int
 |      enum.ReprEnum
 |      enum.Flag
 |      enum.Enum
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __and__(self, other) from enum.Flag
 |
 |  __format__(self, format_spec, /) from builtins.int
 |      Convert to a string according to format_spec.
 |
 |  __invert__(self) from enum.Flag
 |
 |  __new__(cls, value) from enum.Enum
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  __or__(self, other) from enum.Flag
 |      Return self|value.
 |
 |  __rand__ = __and__(self, other)
 |
 |  __ror__ = __or__(self, other)
 |
 |  __rxor__ = __xor__(self, other)
 |
 |  __xor__(self, other) from enum.Flag
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  V1 = <MetadataVersion.V1: 0>
 |
 |  V2 = <MetadataVersion.V2: 1>
 |
 |  V3 = <MetadataVersion.V3: 2>
 |
 |  V4 = <MetadataVersion.V4: 3>
 |
 |  V5 = <MetadataVersion.V5: 4>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from enum.IntFlag:
 |
 |  __repr__(self) from enum.Flag
 |      Return repr(self).
 |
 |  __str__ = __repr__(self, /) from builtins.int
 |      Return repr(self).
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.int:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(self, value, /)
 |      Return self+value.
 |
 |  __bool__(self, /)
 |      True if self else False
 |
 |  __ceil__(...)
 |      Ceiling of an Integral returns itself.
 |
 |  __divmod__(self, value, /)
 |      Return divmod(self, value).
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __float__(self, /)
 |      float(self)
 |
 |  __floor__(...)
 |      Flooring an Integral returns itself.
 |
 |  __floordiv__(self, value, /)
 |      Return self//value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __getnewargs__(self, /)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __index__(self, /)
 |      Return self converted to an integer, if self is suitable for use as an index into a list.
 |
 |  __int__(self, /)
 |      int(self)
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(self, value, /)
 |      Return self<<value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mod__(self, value, /)
 |      Return self%value.
 |
 |  __mul__(self, value, /)
 |      Return self*value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __pos__(self, /)
 |      +self
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rdivmod__(self, value, /)
 |      Return divmod(value, self).
 |
 |  __rfloordiv__(self, value, /)
 |      Return value//self.
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmod__(self, value, /)
 |      Return value%self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __round__(...)
 |      Rounding an Integral returns itself.
 |
 |      Rounding with an ndigits argument also returns an integer.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(self, value, /)
 |      Return self>>value.
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __sizeof__(self, /)
 |      Returns size in memory, in bytes.
 |
 |  __sub__(self, value, /)
 |      Return self-value.
 |
 |  __truediv__(self, value, /)
 |      Return self/value.
 |
 |  __trunc__(...)
 |      Truncating an Integral returns itself.
 |
 |  as_integer_ratio(self, /)
 |      Return a pair of integers, whose ratio is equal to the original int.
 |
 |      The ratio is in lowest terms and has a positive denominator.
 |
 |      >>> (10).as_integer_ratio()
 |      (10, 1)
 |      >>> (-10).as_integer_ratio()
 |      (-10, 1)
 |      >>> (0).as_integer_ratio()
 |      (0, 1)
 |
 |  bit_count(self, /)
 |      Number of ones in the binary representation of the absolute value of self.
 |
 |      Also known as the population count.
 |
 |      >>> bin(13)
 |      '0b1101'
 |      >>> (13).bit_count()
 |      3
 |
 |  bit_length(self, /)
 |      Number of bits necessary to represent self in binary.
 |
 |      >>> bin(37)
 |      '0b100101'
 |      >>> (37).bit_length()
 |      6
 |
 |  conjugate(...)
 |      Returns self, the complex conjugate of any int.
 |
 |  is_integer(self, /)
 |      Returns True. Exists for duck type compatibility with float.is_integer.
 |
 |  to_bytes(self, /, length=1, byteorder='big', *, signed=False)
 |      Return an array of bytes representing an integer.
 |
 |      length
 |        Length of bytes object to use.  An OverflowError is raised if the
 |        integer is not representable with the given number of bytes.  Default
 |        is length 1.
 |      byteorder
 |        The byte order used to represent the integer.  If byteorder is 'big',
 |        the most significant byte is at the beginning of the byte array.  If
 |        byteorder is 'little', the most significant byte is at the end of the
 |        byte array.  To request the native byte order of the host system, use
 |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
 |      signed
 |        Determines whether two's complement is used to represent the integer.
 |        If signed is False and a negative integer is given, an OverflowError
 |        is raised.
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from builtins.int:
 |
 |  from_bytes(bytes, byteorder='big', *, signed=False)
 |      Return the integer represented by the given array of bytes.
 |
 |      bytes
 |        Holds the array of bytes to convert.  The argument must either
 |        support the buffer protocol or be an iterable object producing bytes.
 |        Bytes and bytearray are examples of built-in objects that support the
 |        buffer protocol.
 |      byteorder
 |        The byte order used to represent the integer.  If byteorder is 'big',
 |        the most significant byte is at the beginning of the byte array.  If
 |        byteorder is 'little', the most significant byte is at the end of the
 |        byte array.  To request the native byte order of the host system, use
 |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
 |      signed
 |        Indicates whether two's complement is used to represent the integer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from builtins.int:
 |
 |  denominator
 |      the denominator of a rational number in lowest terms
 |
 |  imag
 |      the imaginary part of a complex number
 |
 |  numerator
 |      the numerator of a rational number in lowest terms
 |
 |  real
 |      the real part of a complex number
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from enum.Flag:
 |
 |  __contains__(self, other)
 |      Returns True if self has at least the same flags set as other.
 |
 |  __iter__(self)
 |      Returns flags in definition order.
 |
 |  __len__(self)
 |      Return the number of members (no aliases)
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from enum.Enum:
 |
 |  __dir__(self)
 |      Returns public methods and other interesting attributes.
 |
 |  __init__(self, *args, **kwds)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce_ex__(self, proto)
 |      Helper for pickle.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from enum.Enum:
 |
 |  name
 |      The name of the Enum member.
 |
 |  value
 |      The value of the Enum member.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from enum.EnumType:
 |
 |  __getitem__(name)
 |      Return the member matching `name`.
 |
 |  ----------------------------------------------------------------------
 |  Readonly properties inherited from enum.EnumType:
 |
 |  __members__
 |      Returns a mapping of member name->value.
 |
 |      This mapping lists all enum members, including aliases. Note that this
 |      is a read-only view of the internal mapping.

Python Library Documentation: class MockOutputStream in module pyarrow.lib

class MockOutputStream(NativeFile)
 |  Method resolution order:
 |      MockOutputStream
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__ = __reduce_cython__(...)
 |      MockOutputStream.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      MockOutputStream.__setstate_cython__(self, __pyx_state)
 |
 |  size(self)
 |      MockOutputStream.size(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  fileno(self)
 |      NativeFile.fileno(self)
 |
 |      NOT IMPLEMENTED
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class MonthDayNano in module builtins

class MonthDayNano(tuple)
 |  MonthDayNano(iterable=(), /)
 |
 |  A calendar interval consisting of months, days and nanoseconds.
 |
 |  Method resolution order:
 |      MonthDayNano
 |      tuple
 |      object
 |
 |  Methods defined here:
 |
 |  __reduce__(...)
 |      Helper for pickle.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  days
 |      The number days in the interval
 |
 |  months
 |      The number of months in the interval
 |
 |  nanoseconds
 |      The number of nanoseconds in the interval
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __match_args__ = ('months', 'days', 'nanoseconds')
 |
 |  n_fields = 3
 |
 |  n_sequence_fields = 3
 |
 |  n_unnamed_fields = 0
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from tuple:
 |
 |  __add__(self, value, /)
 |      Return self+value.
 |
 |  __contains__(self, key, /)
 |      Return bool(key in self).
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __getnewargs__(self, /)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(self, value, /)
 |      Return self*value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  count(self, value, /)
 |      Return number of occurrences of value.
 |
 |  index(self, value, start=0, stop=9223372036854775807, /)
 |      Return first index of value.
 |
 |      Raises ValueError if the value is not present.
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from tuple:
 |
 |  __class_getitem__(...)
 |      See PEP 585

Python Library Documentation: class MonthDayNanoIntervalArray in module pyarrow.lib

class MonthDayNanoIntervalArray(Array)
 |  Concrete class for Arrow arrays of interval[MonthDayNano] type.
 |
 |  Method resolution order:
 |      MonthDayNanoIntervalArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      MonthDayNanoIntervalArray.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      pyarrow.MonthDayNano is used as the native representation.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class MonthDayNanoIntervalScalar in module pyarrow.lib

class MonthDayNanoIntervalScalar(Scalar)
 |  Concrete class for month, day, nanosecond interval scalars.
 |
 |  Method resolution order:
 |      MonthDayNanoIntervalScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      MonthDayNanoIntervalScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a pyarrow.MonthDayNano.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value
 |      Same as self.as_py()
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: NullScalar object

class NullScalar(Scalar)
 |  NullScalar()
 |
 |  Concrete class for null scalars.
 |
 |  Method resolution order:
 |      NullScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      NullScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python None.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: NullScalar object

class NullScalar(Scalar)
 |  NullScalar()
 |
 |  Concrete class for null scalars.
 |
 |  Method resolution order:
 |      NullScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      NullScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python None.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class NativeFile in module pyarrow.lib

class NativeFile(_Weakrefable)
 |  The base class for all Arrow streams.
 |
 |  Streams are either readable, writable, or both.
 |  They optionally support seeking.
 |
 |  While this class exposes methods to read or write data from Python, the
 |  primary intent of using a Arrow stream is to pass it to other Arrow
 |  facilities that will make use of it, such as Arrow IPC routines.
 |
 |  Be aware that there are subtle differences with regular Python files,
 |  e.g. destroying a writable Arrow stream without closing it explicitly
 |  will not flush any pending data.
 |
 |  Method resolution order:
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __reduce__ = __reduce_cython__(...)
 |      NativeFile.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      NativeFile.__setstate_cython__(self, __pyx_state)
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  fileno(self)
 |      NativeFile.fileno(self)
 |
 |      NOT IMPLEMENTED
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class NullArray in module pyarrow.lib

class NullArray(Array)
 |  Concrete class for Arrow arrays of null data type.
 |
 |  Method resolution order:
 |      NullArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class NullScalar in module pyarrow.lib

class NullScalar(Scalar)
 |  NullScalar()
 |
 |  Concrete class for null scalars.
 |
 |  Method resolution order:
 |      NullScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      NullScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python None.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class NumericArray in module pyarrow.lib

class NumericArray(Array)
 |  A base class for Arrow numeric arrays.
 |
 |  Method resolution order:
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class OSFile in module pyarrow.lib

class OSFile(NativeFile)
 |  A stream backed by a regular file descriptor.
 |
 |  Examples
 |  --------
 |  Create a new file to write to:
 |
 |  >>> import pyarrow as pa
 |  >>> with pa.OSFile('example_osfile.arrow', mode='w') as f:
 |  ...     f.writable()
 |  ...     f.write(b'OSFile')
 |  ...     f.seekable()
 |  ...
 |  True
 |  6
 |  False
 |
 |  Open the file to read:
 |
 |  >>> with pa.OSFile('example_osfile.arrow', mode='r') as f:
 |  ...     f.mode
 |  ...     f.read()
 |  ...
 |  'rb'
 |  b'OSFile'
 |
 |  Open the file to append:
 |
 |  >>> with pa.OSFile('example_osfile.arrow', mode='ab') as f:
 |  ...     f.mode
 |  ...     f.write(b' is super!')
 |  ...
 |  'ab'
 |  10
 |  >>> with pa.OSFile('example_osfile.arrow') as f:
 |  ...     f.read()
 |  ...
 |  b'OSFile is super!'
 |
 |  Inspect created OSFile:
 |
 |  >>> pa.OSFile('example_osfile.arrow')
 |  <pyarrow.OSFile closed=False own_file=False is_seekable=True is_writable=False is_readable=True>
 |
 |  Method resolution order:
 |      OSFile
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__ = __reduce_cython__(...)
 |      OSFile.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      OSFile.__setstate_cython__(self, __pyx_state)
 |
 |  fileno(self)
 |      OSFile.fileno(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class OpaqueArray in module pyarrow.lib

class OpaqueArray(ExtensionArray)
 |  Concrete class for opaque extension arrays.
 |
 |  Examples
 |  --------
 |  Define the extension type for an opaque array
 |
 |  >>> import pyarrow as pa
 |  >>> opaque_type = pa.opaque(
 |  ...     pa.binary(),
 |  ...     type_name="geometry",
 |  ...     vendor_name="postgis",
 |  ... )
 |
 |  Create an extension array
 |
 |  >>> arr = [None, b"data"]
 |  >>> storage = pa.array(arr, pa.binary())
 |  >>> pa.ExtensionArray.from_storage(opaque_type, storage)
 |  <pyarrow.lib.OpaqueArray object at ...>
 |  [
 |    null,
 |    64617461
 |  ]
 |
 |  Method resolution order:
 |      OpaqueArray
 |      ExtensionArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from ExtensionArray:
 |
 |  from_storage(typ, storage)
 |      ExtensionArray.from_storage(BaseExtensionType typ, Array storage)
 |
 |      Construct ExtensionArray from type and storage array.
 |
 |      Parameters
 |      ----------
 |      typ : DataType
 |          The extension type for the result array.
 |      storage : Array
 |          The underlying storage for the result array.
 |
 |      Returns
 |      -------
 |      ext_array : ExtensionArray
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ExtensionArray:
 |
 |  storage
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class OpaqueScalar in module pyarrow.lib

class OpaqueScalar(ExtensionScalar)
 |  Concrete class for opaque extension scalar.
 |
 |  Method resolution order:
 |      OpaqueScalar
 |      ExtensionScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from ExtensionScalar:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      ExtensionScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this scalar as a Python object.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from ExtensionScalar:
 |
 |  from_storage(typ, value)
 |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
 |
 |      Construct ExtensionScalar from type and storage value.
 |
 |      Parameters
 |      ----------
 |      typ : DataType
 |          The extension type for the result scalar.
 |      value : object
 |          The storage value for the result scalar.
 |
 |      Returns
 |      -------
 |      ext_scalar : ExtensionScalar
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ExtensionScalar:
 |
 |  value
 |      Return storage value as a scalar.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class OpaqueType in module pyarrow.lib

class OpaqueType(BaseExtensionType)
 |  Concrete class for opaque extension type.
 |
 |  Opaque is a placeholder for a type from an external (often non-Arrow)
 |  system that could not be interpreted.
 |
 |  Examples
 |  --------
 |  Create an instance of opaque extension type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.opaque(pa.int32(), "geometry", "postgis")
 |  OpaqueType(extension<arrow.opaque[storage_type=int32, type_name=geometry, vendor_name=postgis]>)
 |
 |  Method resolution order:
 |      OpaqueType
 |      BaseExtensionType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_ext_class__(self)
 |      OpaqueType.__arrow_ext_class__(self)
 |
 |  __arrow_ext_scalar_class__(self)
 |      OpaqueType.__arrow_ext_scalar_class__(self)
 |
 |  __reduce__(self)
 |      OpaqueType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  type_name
 |      The name of the type in the external system.
 |
 |  vendor_name
 |      The name of the external system.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseExtensionType:
 |
 |  wrap_array(self, storage)
 |      BaseExtensionType.wrap_array(self, storage)
 |
 |      Wrap the given storage array as an extension array.
 |
 |      Parameters
 |      ----------
 |      storage : Array or ChunkedArray
 |
 |      Returns
 |      -------
 |      array : Array or ChunkedArray
 |          Extension array wrapping the storage array
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from BaseExtensionType:
 |
 |  bit_width
 |      The bit width of the extension type.
 |
 |  byte_width
 |      The byte width of the extension type.
 |
 |  extension_name
 |      The extension type name.
 |
 |  storage_type
 |      The underlying storage type.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class ProxyMemoryPool in module pyarrow.lib

class ProxyMemoryPool(MemoryPool)
 |  ProxyMemoryPool()
 |
 |  Memory pool implementation that tracks the number of bytes and
 |  maximum memory allocated through its direct calls, while redirecting
 |  to another memory pool.
 |
 |  Method resolution order:
 |      ProxyMemoryPool
 |      MemoryPool
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      ProxyMemoryPool.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      ProxyMemoryPool.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from MemoryPool:
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  bytes_allocated(self)
 |      MemoryPool.bytes_allocated(self)
 |
 |      Return the number of bytes that are currently allocated from this
 |      memory pool.
 |
 |  max_memory(self)
 |      MemoryPool.max_memory(self)
 |
 |      Return the peak memory allocation in this memory pool.
 |      This can be an approximate number in multi-threaded applications.
 |
 |      None is returned if the pool implementation doesn't know how to
 |      compute this number.
 |
 |  num_allocations(self)
 |      MemoryPool.num_allocations(self)
 |
 |      Return the number of allocations or reallocations that were made
 |      using this memory pool.
 |
 |  print_stats(self)
 |      MemoryPool.print_stats(self)
 |
 |      Print statistics about this memory pool.
 |
 |      The output format is implementation-specific. Not all memory pools
 |      implement this method.
 |
 |  release_unused(self)
 |      MemoryPool.release_unused(self)
 |
 |      Attempt to return to the OS any memory being held onto by the pool.
 |
 |      This function should not be called except potentially for
 |      benchmarking or debugging as it could be expensive and detrimental to
 |      performance.
 |
 |      This is best effort and may not have any effect on some memory pools
 |      or in some situations (e.g. fragmentation).
 |
 |  total_bytes_allocated(self)
 |      MemoryPool.total_bytes_allocated(self)
 |
 |      Return the total number of bytes that have been allocated from this
 |      memory pool.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from MemoryPool:
 |
 |  backend_name
 |      The name of the backend used by this MemoryPool (e.g. "jemalloc").

Python Library Documentation: class PythonFile in module pyarrow.lib

class PythonFile(NativeFile)
 |  A stream backed by a Python file object.
 |
 |  This class allows using Python file objects with arbitrary Arrow
 |  functions, including functions written in another language than Python.
 |
 |  As a downside, there is a non-zero redirection cost in translating
 |  Arrow stream calls to Python method calls.  Furthermore, Python's
 |  Global Interpreter Lock may limit parallelism in some situations.
 |
 |  Examples
 |  --------
 |  >>> import io
 |  >>> import pyarrow as pa
 |  >>> pa.PythonFile(io.BytesIO())
 |  <pyarrow.PythonFile closed=False own_file=False is_seekable=False is_writable=True is_readable=False>
 |
 |  Create a stream for writing:
 |
 |  >>> buf = io.BytesIO()
 |  >>> f =  pa.PythonFile(buf, mode = 'w')
 |  >>> f.writable()
 |  True
 |  >>> f.write(b'PythonFile')
 |  10
 |  >>> buf.getvalue()
 |  b'PythonFile'
 |  >>> f.close()
 |  >>> f
 |  <pyarrow.PythonFile closed=True own_file=False is_seekable=False is_writable=True is_readable=False>
 |
 |  Create a stream for reading:
 |
 |  >>> buf = io.BytesIO(b'PythonFile')
 |  >>> f =  pa.PythonFile(buf, mode = 'r')
 |  >>> f.mode
 |  'rb'
 |  >>> f.read()
 |  b'PythonFile'
 |  >>> f
 |  <pyarrow.PythonFile closed=False own_file=False is_seekable=True is_writable=False is_readable=True>
 |  >>> f.close()
 |  >>> f
 |  <pyarrow.PythonFile closed=True own_file=False is_seekable=True is_writable=False is_readable=True>
 |
 |  Method resolution order:
 |      PythonFile
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__ = __reduce_cython__(...)
 |      PythonFile.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      PythonFile.__setstate_cython__(self, __pyx_state)
 |
 |  readline(self, size=None)
 |      PythonFile.readline(self, size=None)
 |
 |      Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Parameters
 |      ----------
 |      size : int
 |          Maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      PythonFile.readlines(self, hint=None)
 |
 |      Read lines of the file.
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          Maximum number of bytes read until we stop
 |
 |  truncate(self, pos=None)
 |      PythonFile.truncate(self, pos=None)
 |
 |      Parameters
 |      ----------
 |      pos : int, optional
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  fileno(self)
 |      NativeFile.fileno(self)
 |
 |      NOT IMPLEMENTED
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class RecordBatch in module pyarrow.lib

class RecordBatch(_Tabular)
 |  Batch of rows of columns of equal length
 |
 |  Warnings
 |  --------
 |  Do not call this class's constructor directly, use one of the
 |  ``RecordBatch.from_*`` functions instead.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |  >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |  >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |  >>> names = ["n_legs", "animals"]
 |
 |  Constructing a RecordBatch from arrays:
 |
 |  >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names)
 |  pyarrow.RecordBatch
 |  n_legs: int64
 |  animals: string
 |  ----
 |  n_legs: [2,2,4,4,5,100]
 |  animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
 |  >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas()
 |     n_legs        animals
 |  0       2       Flamingo
 |  1       2         Parrot
 |  2       4            Dog
 |  3       4          Horse
 |  4       5  Brittle stars
 |  5     100      Centipede
 |
 |  Constructing a RecordBatch from pandas DataFrame:
 |
 |  >>> import pandas as pd
 |  >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022],
 |  ...                    'month': [3, 5, 7, 9],
 |  ...                    'day': [1, 5, 9, 13],
 |  ...                    'n_legs': [2, 4, 5, 100],
 |  ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |  >>> pa.RecordBatch.from_pandas(df)
 |  pyarrow.RecordBatch
 |  year: int64
 |  month: int64
 |  day: int64
 |  n_legs: int64
 |  animals: large_string
 |  ----
 |  year: [2020,2022,2021,2022]
 |  month: [3,5,7,9]
 |  day: [1,5,9,13]
 |  n_legs: [2,4,5,100]
 |  animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |  >>> pa.RecordBatch.from_pandas(df).to_pandas()
 |     year  month  day  n_legs        animals
 |  0  2020      3    1       2       Flamingo
 |  1  2022      5    5       4          Horse
 |  2  2021      7    9       5  Brittle stars
 |  3  2022      9   13     100      Centipede
 |
 |  Constructing a RecordBatch from pylist:
 |
 |  >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'},
 |  ...           {'n_legs': 4, 'animals': 'Dog'}]
 |  >>> pa.RecordBatch.from_pylist(pylist).to_pandas()
 |     n_legs   animals
 |  0       2  Flamingo
 |  1       4       Dog
 |
 |  You can also construct a RecordBatch using :func:`pyarrow.record_batch`:
 |
 |  >>> pa.record_batch([n_legs, animals], names=names).to_pandas()
 |     n_legs        animals
 |  0       2       Flamingo
 |  1       2         Parrot
 |  2       4            Dog
 |  3       4          Horse
 |  4       5  Brittle stars
 |  5     100      Centipede
 |
 |  >>> pa.record_batch(df)
 |  pyarrow.RecordBatch
 |  year: int64
 |  month: int64
 |  day: int64
 |  n_legs: int64
 |  animals: large_string
 |  ----
 |  year: [2020,2022,2021,2022]
 |  month: [3,5,7,9]
 |  day: [1,5,9,13]
 |  n_legs: [2,4,5,100]
 |  animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |
 |  Method resolution order:
 |      RecordBatch
 |      _Tabular
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      RecordBatch.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the batch to this schema.
 |          If None, the batch will be returned as-is, with a schema matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      RecordBatch.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the batch to this data type.
 |          If None, the batch will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __arrow_c_stream__(self, requested_schema=None)
 |      RecordBatch.__arrow_c_stream__(self, requested_schema=None)
 |
 |      Export the batch as an Arrow C stream PyCapsule.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule, default None
 |          The schema to which the stream should be casted, passed as a
 |          PyCapsule containing a C ArrowSchema representation of the
 |          requested schema.
 |          Currently, this is not supported and will raise a
 |          NotImplementedError if the schema doesn't match the current schema.
 |
 |      Returns
 |      -------
 |      PyCapsule
 |
 |  __reduce__(self)
 |      RecordBatch.__reduce__(self)
 |
 |  __sizeof__(self)
 |      RecordBatch.__sizeof__(self)
 |
 |  add_column(self, i, field_, column)
 |      RecordBatch.add_column(self, int i, field_, column)
 |
 |      Add column to RecordBatch at position i.
 |
 |      A new record batch is returned with the column added, the original record batch
 |      object is left unchanged.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          Index to place the column at.
 |      field_ : str or Field
 |          If a string is passed then the type is deduced from the column
 |          data.
 |      column : Array or value coercible to array
 |          Column data.
 |
 |      Returns
 |      -------
 |      RecordBatch
 |          New record batch with the passed column added.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> batch = pa.RecordBatch.from_arrays(
 |      ...     [[2, 4, 5, 100], ["Flamingo", "Horse", "Brittle stars", "Centipede"]],
 |      ...     names=['n_legs', 'animals'])
 |
 |      Add column:
 |
 |      >>> year = [2021, 2022, 2019, 2021]
 |      >>> batch.add_column(0,"year", year)
 |      pyarrow.RecordBatch
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [2021,2022,2019,2021]
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |
 |      Original record batch is left unchanged:
 |
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |
 |  cast(self, target_schema, safe=None, options=None)
 |      RecordBatch.cast(self, Schema target_schema, safe=None, options=None)
 |
 |      Cast record batch values to another schema.
 |
 |      Parameters
 |      ----------
 |      target_schema : Schema
 |          Schema to cast to, the names and order of fields must match.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |
 |      Returns
 |      -------
 |      RecordBatch
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> batch = pa.RecordBatch.from_arrays(
 |      ...     [[2, 4, 5, 100], ["Flamingo", "Horse", "Brittle stars", "Centipede"]],
 |      ...     names=['n_legs', 'animals'])
 |      >>> batch.schema
 |      n_legs: int64
 |      animals: string
 |
 |      Define new schema and cast batch values:
 |
 |      >>> my_schema = pa.schema([
 |      ...     pa.field('n_legs', pa.duration('s')),
 |      ...     pa.field('animals', pa.string())]
 |      ...     )
 |      >>> batch.cast(target_schema=my_schema)
 |      pyarrow.RecordBatch
 |      n_legs: duration[s]
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |
 |  copy_to(self, destination)
 |      RecordBatch.copy_to(self, destination)
 |
 |      Copy the entire RecordBatch to destination device.
 |
 |      This copies each column of the record batch to create
 |      a new record batch where all underlying buffers for the columns have
 |      been copied to the destination MemoryManager.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      RecordBatch
 |
 |  equals(self, other, check_metadata=False)
 |      RecordBatch.equals(self, other, bool check_metadata=False)
 |
 |      Check if contents of two record batches are equal.
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.RecordBatch
 |          RecordBatch to compare against.
 |      check_metadata : bool, default False
 |          Whether schema metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      are_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
 |      ...                                     names=["n_legs", "animals"])
 |      >>> batch_0 = pa.record_batch([])
 |      >>> batch_1 = pa.RecordBatch.from_arrays([n_legs, animals],
 |      ...                                       names=["n_legs", "animals"],
 |      ...                                       metadata={"n_legs": "Number of legs per animal"})
 |      >>> batch.equals(batch)
 |      True
 |      >>> batch.equals(batch_0)
 |      False
 |      >>> batch.equals(batch_1)
 |      True
 |      >>> batch.equals(batch_1, check_metadata=True)
 |      False
 |
 |  get_total_buffer_size(self)
 |      RecordBatch.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the record batch
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
 |      ...                                     names=["n_legs", "animals"])
 |      >>> batch.get_total_buffer_size()
 |      120
 |
 |  remove_column(self, i)
 |      RecordBatch.remove_column(self, int i)
 |
 |      Create new RecordBatch with the indicated column removed.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          Index of column to remove.
 |
 |      Returns
 |      -------
 |      Table
 |          New record batch without the column.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> batch = pa.RecordBatch.from_arrays(
 |      ...     [[2, 4, 5, 100], ["Flamingo", "Horse", "Brittle stars", "Centipede"]],
 |      ...     names=['n_legs', 'animals'])
 |      >>> batch.remove_column(1)
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      ----
 |      n_legs: [2,4,5,100]
 |
 |  rename_columns(self, names)
 |      RecordBatch.rename_columns(self, names)
 |
 |      Create new record batch with columns renamed to provided names.
 |
 |      Parameters
 |      ----------
 |      names : list[str] or dict[str, str]
 |          List of new column names or mapping of old column names to new column names.
 |
 |          If a mapping of old to new column names is passed, then all columns which are
 |          found to match a provided old column name will be renamed to the new column name.
 |          If any column names are not found in the mapping, a KeyError will be raised.
 |
 |      Raises
 |      ------
 |      KeyError
 |          If any of the column names passed in the names mapping do not exist.
 |
 |      Returns
 |      -------
 |      RecordBatch
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> batch = pa.RecordBatch.from_arrays(
 |      ...     [[2, 4, 5, 100], ["Flamingo", "Horse", "Brittle stars", "Centipede"]],
 |      ...     names=['n_legs', 'animals'])
 |      >>> new_names = ["n", "name"]
 |      >>> batch.rename_columns(new_names)
 |      pyarrow.RecordBatch
 |      n: int64
 |      name: string
 |      ----
 |      n: [2,4,5,100]
 |      name: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> new_names = {"n_legs": "n", "animals": "name"}
 |      >>> batch.rename_columns(new_names)
 |      pyarrow.RecordBatch
 |      n: int64
 |      name: string
 |      ----
 |      n: [2,4,5,100]
 |      name: ["Flamingo","Horse","Brittle stars","Centipede"]
 |
 |  replace_schema_metadata(self, metadata=None)
 |      RecordBatch.replace_schema_metadata(self, metadata=None)
 |
 |      Create shallow copy of record batch by replacing schema
 |      key-value metadata with the indicated new metadata (which may be None,
 |      which deletes any existing metadata
 |
 |      Parameters
 |      ----------
 |      metadata : dict, default None
 |
 |      Returns
 |      -------
 |      shallow_copy : RecordBatch
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |
 |      Constructing a RecordBatch with schema and metadata:
 |
 |      >>> my_schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |      >>> batch = pa.RecordBatch.from_arrays([n_legs], schema=my_schema)
 |      >>> batch.schema
 |      n_legs: int64
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |      Shallow copy of a RecordBatch with deleted schema metadata:
 |
 |      >>> batch.replace_schema_metadata().schema
 |      n_legs: int64
 |
 |  select(self, columns)
 |      RecordBatch.select(self, columns)
 |
 |      Select columns of the RecordBatch.
 |
 |      Returns a new RecordBatch with the specified columns, and metadata
 |      preserved.
 |
 |      Parameters
 |      ----------
 |      columns : list-like
 |          The column names or integer indices to select.
 |
 |      Returns
 |      -------
 |      RecordBatch
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                          names=["n_legs", "animals"])
 |
 |      Select columns my indices:
 |
 |      >>> batch.select([1])
 |      pyarrow.RecordBatch
 |      animals: string
 |      ----
 |      animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
 |
 |      Select columns by names:
 |
 |      >>> batch.select(["n_legs"])
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      ----
 |      n_legs: [2,2,4,4,5,100]
 |
 |  serialize(self, memory_pool=None)
 |      RecordBatch.serialize(self, memory_pool=None)
 |
 |      Write RecordBatch to Buffer as encapsulated IPC message, which does not
 |      include a Schema.
 |
 |      To reconstruct a RecordBatch from the encapsulated IPC message Buffer
 |      returned by this function, a Schema must be passed separately. See
 |      Examples.
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Uses default memory pool if not specified
 |
 |      Returns
 |      -------
 |      serialized : Buffer
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
 |      ...                                     names=["n_legs", "animals"])
 |      >>> buf = batch.serialize()
 |      >>> buf
 |      <pyarrow.Buffer address=0x... size=... is_cpu=True is_mutable=True>
 |
 |      Reconstruct RecordBatch from IPC message Buffer and original Schema
 |
 |      >>> pa.ipc.read_record_batch(buf, batch.schema)
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,2,4,4,5,100]
 |      animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
 |
 |  set_column(self, i, field_, column)
 |      RecordBatch.set_column(self, int i, field_, column)
 |
 |      Replace column in RecordBatch at position.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          Index to place the column at.
 |      field_ : str or Field
 |          If a string is passed then the type is deduced from the column
 |          data.
 |      column : Array or value coercible to array
 |          Column data.
 |
 |      Returns
 |      -------
 |      RecordBatch
 |          New record batch with the passed column set.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> batch = pa.RecordBatch.from_arrays(
 |      ...     [[2, 4, 5, 100], ["Flamingo", "Horse", "Brittle stars", "Centipede"]],
 |      ...     names=['n_legs', 'animals'])
 |
 |      Replace a column:
 |
 |      >>> year = [2021, 2022, 2019, 2021]
 |      >>> batch.set_column(1,'year', year)
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      year: int64
 |      ----
 |      n_legs: [2,4,5,100]
 |      year: [2021,2022,2019,2021]
 |
 |  slice(self, offset=0, length=None)
 |      RecordBatch.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this RecordBatch
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of record batch to slice
 |      length : int, default None
 |          Length of slice (default is until end of batch starting from
 |          offset)
 |
 |      Returns
 |      -------
 |      sliced : RecordBatch
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
 |      ...                                     names=["n_legs", "animals"])
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       2         Parrot
 |      2       4            Dog
 |      3       4          Horse
 |      4       5  Brittle stars
 |      5     100      Centipede
 |      >>> batch.slice(offset=3).to_pandas()
 |         n_legs        animals
 |      0       4          Horse
 |      1       5  Brittle stars
 |      2     100      Centipede
 |      >>> batch.slice(length=2).to_pandas()
 |         n_legs   animals
 |      0       2  Flamingo
 |      1       2    Parrot
 |      >>> batch.slice(offset=3, length=1).to_pandas()
 |         n_legs animals
 |      0       4   Horse
 |
 |  to_struct_array(self)
 |      RecordBatch.to_struct_array(self)
 |
 |      Convert to a struct array.
 |
 |  to_tensor(self, null_to_nan=False, row_major=True, memory_pool=None)
 |      RecordBatch.to_tensor(self, bool null_to_nan=False, bool row_major=True, MemoryPool memory_pool=None)
 |
 |      Convert to a :class:`~pyarrow.Tensor`.
 |
 |      RecordBatches that can be converted have fields of type signed or unsigned
 |      integer or float, including all bit-widths.
 |
 |      ``null_to_nan`` is ``False`` by default and this method will raise an error in case
 |      any nulls are present. RecordBatches with nulls can be converted with ``null_to_nan``
 |      set to ``True``. In this case null values are converted to ``NaN`` and integer type
 |      arrays are promoted to the appropriate float type.
 |
 |      Parameters
 |      ----------
 |      null_to_nan : bool, default False
 |          Whether to write null values in the result as ``NaN``.
 |      row_major : bool, default True
 |          Whether resulting Tensor is row-major or column-major
 |      memory_pool : MemoryPool, default None
 |          For memory allocations, if required, otherwise use default pool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> batch = pa.record_batch(
 |      ...    [
 |      ...         pa.array([1, 2, 3, 4, None], type=pa.int32()),
 |      ...         pa.array([10, 20, 30, 40, None], type=pa.float32()),
 |      ...     ], names = ["a", "b"]
 |      ... )
 |
 |      >>> batch
 |      pyarrow.RecordBatch
 |      a: int32
 |      b: float
 |      ----
 |      a: [1,2,3,4,null]
 |      b: [10,20,30,40,null]
 |
 |      Convert a RecordBatch to row-major Tensor with null values
 |      written as NaN values
 |
 |      >>> batch.to_tensor(null_to_nan=True)
 |      <pyarrow.Tensor>
 |      type: double
 |      shape: (5, 2)
 |      strides: (16, 8)
 |      >>> batch.to_tensor(null_to_nan=True).to_numpy()
 |      array([[ 1., 10.],
 |             [ 2., 20.],
 |             [ 3., 30.],
 |             [ 4., 40.],
 |             [nan, nan]])
 |
 |      Convert a RecordBatch to column-major Tensor
 |
 |      >>> batch.to_tensor(null_to_nan=True, row_major=False)
 |      <pyarrow.Tensor>
 |      type: double
 |      shape: (5, 2)
 |      strides: (8, 40)
 |      >>> batch.to_tensor(null_to_nan=True, row_major=False).to_numpy()
 |      array([[ 1., 10.],
 |             [ 2., 20.],
 |             [ 3., 30.],
 |             [ 4., 40.],
 |             [nan, nan]])
 |
 |  validate(self, *, full=False)
 |      RecordBatch.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  from_pandas(df, schema=None, preserve_index=None, nthreads=None, columns=None)
 |      RecordBatch.from_pandas(cls, df, Schema schema=None, preserve_index=None, nthreads=None, columns=None)
 |
 |      Convert pandas.DataFrame to an Arrow RecordBatch
 |
 |      Parameters
 |      ----------
 |      df : pandas.DataFrame
 |      schema : pyarrow.Schema, optional
 |          The expected schema of the RecordBatch. This can be used to
 |          indicate the type of columns if we cannot infer it automatically.
 |          If passed, the output will have exactly this schema. Columns
 |          specified in the schema that are not found in the DataFrame columns
 |          or its index will raise an error. Additional columns or index
 |          levels in the DataFrame which are not specified in the schema will
 |          be ignored.
 |      preserve_index : bool, optional
 |          Whether to store the index as an additional column in the resulting
 |          ``RecordBatch``. The default of None will store the index as a
 |          column, except for RangeIndex which is stored as metadata only. Use
 |          ``preserve_index=True`` to force it to be stored as a column.
 |      nthreads : int, default None
 |          If greater than 1, convert columns to Arrow in parallel using
 |          indicated number of threads. By default, this follows
 |          :func:`pyarrow.cpu_count` (may use up to system CPU count threads).
 |      columns : list, optional
 |         List of column to be converted. If None, use all columns.
 |
 |      Returns
 |      -------
 |      pyarrow.RecordBatch
 |
 |
 |      Examples
 |      --------
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022],
 |      ...                    'month': [3, 5, 7, 9],
 |      ...                    'day': [1, 5, 9, 13],
 |      ...                    'n_legs': [2, 4, 5, 100],
 |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |
 |      Convert pandas DataFrame to RecordBatch:
 |
 |      >>> import pyarrow as pa
 |      >>> pa.RecordBatch.from_pandas(df)
 |      pyarrow.RecordBatch
 |      year: int64
 |      month: int64
 |      day: int64
 |      n_legs: int64
 |      animals: large_string
 |      ----
 |      year: [2020,2022,2021,2022]
 |      month: [3,5,7,9]
 |      day: [1,5,9,13]
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |
 |      Convert pandas DataFrame to RecordBatch using schema:
 |
 |      >>> my_schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |      >>> pa.RecordBatch.from_pandas(df, schema=my_schema)
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |
 |      Convert pandas DataFrame to RecordBatch specifying columns:
 |
 |      >>> pa.RecordBatch.from_pandas(df, columns=["n_legs"])
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      ----
 |      n_legs: [2,4,5,100]
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_arrays(arrays, names=None, schema=None, metadata=None)
 |      RecordBatch.from_arrays(list arrays, names=None, schema=None, metadata=None)
 |
 |      Construct a RecordBatch from multiple pyarrow.Arrays
 |
 |      Parameters
 |      ----------
 |      arrays : list of pyarrow.Array
 |          One for each field in RecordBatch
 |      names : list of str, optional
 |          Names for the batch fields. If not passed, schema must be passed
 |      schema : Schema, default None
 |          Schema for the created batch. If not passed, names must be passed
 |      metadata : dict or Mapping, default None
 |          Optional metadata for the schema (if inferred).
 |
 |      Returns
 |      -------
 |      pyarrow.RecordBatch
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> names = ["n_legs", "animals"]
 |
 |      Construct a RecordBatch from pyarrow Arrays using names:
 |
 |      >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names)
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,2,4,4,5,100]
 |      animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
 |      >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       2         Parrot
 |      2       4            Dog
 |      3       4          Horse
 |      4       5  Brittle stars
 |      5     100      Centipede
 |
 |      Construct a RecordBatch from pyarrow Arrays using schema:
 |
 |      >>> my_schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |      >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       2         Parrot
 |      2       4            Dog
 |      3       4          Horse
 |      4       5  Brittle stars
 |      5     100      Centipede
 |      >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |  from_struct_array(struct_array)
 |      RecordBatch.from_struct_array(StructArray struct_array)
 |
 |      Construct a RecordBatch from a StructArray.
 |
 |      Each field in the StructArray will become a column in the resulting
 |      ``RecordBatch``.
 |
 |      Parameters
 |      ----------
 |      struct_array : StructArray
 |          Array to construct the record batch from.
 |
 |      Returns
 |      -------
 |      pyarrow.RecordBatch
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
 |      ...                    {'year': 2022, 'n_legs': 4, 'animals': 'Goat'}])
 |      >>> pa.RecordBatch.from_struct_array(struct).to_pandas()
 |         n_legs animals    year
 |      0       2  Parrot     NaN
 |      1       4    Goat  2022.0
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  device_type
 |      The device type where the arrays in the RecordBatch reside.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the RecordBatch's arrays are CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the record batch.
 |
 |      In other words, the sum of bytes from all buffer ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will only be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
 |      ...                                     names=["n_legs", "animals"])
 |      >>> batch.nbytes
 |      116
 |
 |  num_columns
 |      Number of columns
 |
 |      Returns
 |      -------
 |      int
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
 |      ...                                     names=["n_legs", "animals"])
 |      >>> batch.num_columns
 |      2
 |
 |  num_rows
 |      Number of rows
 |
 |      Due to the definition of a RecordBatch, all columns have the same
 |      number of rows.
 |
 |      Returns
 |      -------
 |      int
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
 |      ...                                     names=["n_legs", "animals"])
 |      >>> batch.num_rows
 |      6
 |
 |  schema
 |      Schema of the RecordBatch and its columns
 |
 |      Returns
 |      -------
 |      pyarrow.Schema
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
 |      ...                                     names=["n_legs", "animals"])
 |      >>> batch.schema
 |      n_legs: int64
 |      animals: string
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _Tabular:
 |
 |  __array__(self, dtype=None, copy=None)
 |      _Tabular.__array__(self, dtype=None, copy=None)
 |
 |  __dataframe__(self, nan_as_null: 'bool' = False, allow_copy: 'bool' = True)
 |      _Tabular.__dataframe__(self, bool nan_as_null: bool = False, bool allow_copy: bool = True)
 |
 |      Return the dataframe interchange object implementing the interchange protocol.
 |
 |      Parameters
 |      ----------
 |      nan_as_null : bool, default False
 |          Whether to tell the DataFrame to overwrite null values in the data
 |          with ``NaN`` (or ``NaT``).
 |      allow_copy : bool, default True
 |          Whether to allow memory copying when exporting. If set to False
 |          it would cause non-zero-copy exports to fail.
 |
 |      Returns
 |      -------
 |      DataFrame interchange object
 |          The object which consuming library can use to ingress the dataframe.
 |
 |      Notes
 |      -----
 |      Details on the interchange protocol:
 |      https://data-apis.org/dataframe-protocol/latest/index.html
 |      `nan_as_null` currently has no effect; once support for nullable extension
 |      dtypes is added, this value should be propagated to columns.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      _Tabular.__getitem__(self, key)
 |
 |      Slice or return column at given index or column name
 |
 |      Parameters
 |      ----------
 |      key : integer, str, or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      Array (from RecordBatch) or ChunkedArray (from Table) for column input.
 |      RecordBatch or Table for slice input.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce_cython__(self)
 |      _Tabular.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _Tabular.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _Tabular.__setstate_cython__(self, __pyx_state)
 |
 |  append_column(self, field_, column)
 |      _Tabular.append_column(self, field_, column)
 |
 |      Append column at end of columns.
 |
 |      Parameters
 |      ----------
 |      field_ : str or Field
 |          If a string is passed then the type is deduced from the column
 |          data.
 |      column : Array or value coercible to array
 |          Column data.
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |          New table or record batch with the passed column added.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |
 |      Append column at the end:
 |
 |      >>> year = [2021, 2022, 2019, 2021]
 |      >>> table.append_column('year', [year])
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      year: int64
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |      year: [[2021,2022,2019,2021]]
 |
 |  column(self, i)
 |      _Tabular.column(self, i)
 |
 |      Select single column from Table or RecordBatch.
 |
 |      Parameters
 |      ----------
 |      i : int or string
 |          The index or name of the column to retrieve.
 |
 |      Returns
 |      -------
 |      column : Array (for RecordBatch) or ChunkedArray (for Table)
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
 |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table = pa.Table.from_pandas(df)
 |
 |      Select a column by numeric index:
 |
 |      >>> table.column(0)
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |
 |      Select a column by its name:
 |
 |      >>> table.column("animals")
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          "Flamingo",
 |          "Horse",
 |          "Brittle stars",
 |          "Centipede"
 |        ]
 |      ]
 |
 |  drop_columns(self, columns)
 |      _Tabular.drop_columns(self, columns)
 |
 |      Drop one or more columns and return a new Table or RecordBatch.
 |
 |      Parameters
 |      ----------
 |      columns : str or list[str]
 |          Field name(s) referencing existing column(s).
 |
 |      Raises
 |      ------
 |      KeyError
 |          If any of the passed column names do not exist.
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |          A tabular object without the column(s).
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
 |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table = pa.Table.from_pandas(df)
 |
 |      Drop one column:
 |
 |      >>> table.drop_columns("animals")
 |      pyarrow.Table
 |      n_legs: int64
 |      ----
 |      n_legs: [[2,4,5,100]]
 |
 |      Drop one or more columns:
 |
 |      >>> table.drop_columns(["n_legs", "animals"])
 |      pyarrow.Table
 |      ...
 |      ----
 |
 |  drop_null(self)
 |      _Tabular.drop_null(self)
 |
 |      Remove rows that contain missing values from a Table or RecordBatch.
 |
 |      See :func:`pyarrow.compute.drop_null` for full usage.
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |          A tabular object with the same schema, with rows containing
 |          no missing values.
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'year': [None, 2022, 2019, 2021],
 |      ...                   'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> table.drop_null()
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2022,2021]]
 |      n_legs: [[4,100]]
 |      animals: [["Horse","Centipede"]]
 |
 |  field(self, i)
 |      _Tabular.field(self, i)
 |
 |      Select a schema field by its column name or numeric index.
 |
 |      Parameters
 |      ----------
 |      i : int or string
 |          The index or name of the field to retrieve.
 |
 |      Returns
 |      -------
 |      Field
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table.field(0)
 |      pyarrow.Field<n_legs: int64>
 |      >>> table.field(1)
 |      pyarrow.Field<animals: string>
 |
 |  filter(self, mask, null_selection_behavior='drop')
 |      _Tabular.filter(self, mask, null_selection_behavior='drop')
 |
 |      Select rows from the table or record batch based on a boolean mask.
 |
 |      The Table can be filtered based on a mask, which will be passed to
 |      :func:`pyarrow.compute.filter` to perform the filtering, or it can
 |      be filtered through a boolean :class:`.Expression`
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like or .Expression
 |          The boolean mask or the :class:`.Expression` to filter the table with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled, does nothing if
 |          an :class:`.Expression` is used.
 |
 |      Returns
 |      -------
 |      filtered : Table or RecordBatch
 |          A tabular object of the same schema, with only the rows selected
 |          by applied filtering
 |
 |      Examples
 |      --------
 |      Using a Table (works similarly for RecordBatch):
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
 |      ...                   'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |
 |      Define an expression and select rows:
 |
 |      >>> import pyarrow.compute as pc
 |      >>> expr = pc.field("year") <= 2020
 |      >>> table.filter(expr)
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2020,2019]]
 |      n_legs: [[2,5]]
 |      animals: [["Flamingo","Brittle stars"]]
 |
 |      Define a mask and select rows:
 |
 |      >>> mask=[True, True, False, None]
 |      >>> table.filter(mask)
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2020,2022]]
 |      n_legs: [[2,4]]
 |      animals: [["Flamingo","Horse"]]
 |      >>> table.filter(mask, null_selection_behavior='emit_null')
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2020,2022,null]]
 |      n_legs: [[2,4,null]]
 |      animals: [["Flamingo","Horse",null]]
 |
 |  itercolumns(self)
 |      _Tabular.itercolumns(self)
 |
 |      Iterator over all columns in their numerical order.
 |
 |      Yields
 |      ------
 |      Array (for RecordBatch) or ChunkedArray (for Table)
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
 |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> for i in table.itercolumns():
 |      ...     print(i.null_count)
 |      ...
 |      2
 |      1
 |
 |  sort_by(self, sorting, **kwargs)
 |      _Tabular.sort_by(self, sorting, **kwargs)
 |
 |      Sort the Table or RecordBatch by one or multiple columns.
 |
 |      Parameters
 |      ----------
 |      sorting : str or list[tuple(name, order)]
 |          Name of the column to use to sort (ascending), or
 |          a list of multiple sorting conditions where
 |          each entry is a tuple with column name
 |          and sorting order ("ascending" or "descending")
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |          A new tabular object sorted according to the sort keys.
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],
 |      ...                   'n_legs': [2, 2, 4, 4, 5, 100],
 |      ...                   'animal': ["Flamingo", "Parrot", "Dog", "Horse",
 |      ...                   "Brittle stars", "Centipede"]})
 |      >>> table.sort_by('animal')
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animal: string
 |      ----
 |      year: [[2019,2021,2021,2020,2022,2022]]
 |      n_legs: [[5,100,4,2,4,2]]
 |      animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]]
 |
 |  take(self, indices)
 |      _Tabular.take(self, indices)
 |
 |      Select rows from a Table or RecordBatch.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the tabular object whose rows will be returned.
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |          A tabular object with the same schema, containing the taken rows.
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
 |      ...                   'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table.take([1,3])
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2022,2021]]
 |      n_legs: [[4,100]]
 |      animals: [["Horse","Centipede"]]
 |
 |  to_pydict(self, *, maps_as_pydicts=None)
 |      _Tabular.to_pydict(self, *, maps_as_pydicts=None)
 |
 |      Convert the Table or RecordBatch to a dict or OrderedDict.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      dict
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> table = pa.Table.from_arrays([n_legs, animals], names=["n_legs", "animals"])
 |      >>> table.to_pydict()
 |      {'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']}
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      _Tabular.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert the Table or RecordBatch to a list of rows / dictionaries.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      list
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> data = [[2, 4, 5, 100],
 |      ...         ["Flamingo", "Horse", "Brittle stars", "Centipede"]]
 |      >>> table = pa.table(data, names=["n_legs", "animals"])
 |      >>> table.to_pylist()
 |      [{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ...
 |
 |  to_string(self, *, show_metadata=False, preview_cols=0)
 |      _Tabular.to_string(self, *, show_metadata=False, preview_cols=0)
 |
 |      Return human-readable string representation of Table or RecordBatch.
 |
 |      Parameters
 |      ----------
 |      show_metadata : bool, default False
 |          Display Field-level and Schema-level KeyValueMetadata.
 |      preview_cols : int, default 0
 |          Display values of the columns for the first N columns.
 |
 |      Returns
 |      -------
 |      str
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from _Tabular:
 |
 |  from_pydict(mapping, schema=None, metadata=None)
 |      _Tabular.from_pydict(cls, mapping, schema=None, metadata=None)
 |
 |      Construct a Table or RecordBatch from Arrow arrays or columns.
 |
 |      Parameters
 |      ----------
 |      mapping : dict or Mapping
 |          A mapping of strings to Arrays or Python lists.
 |      schema : Schema, default None
 |          If not passed, will be inferred from the Mapping values.
 |      metadata : dict or Mapping, default None
 |          Optional metadata for the schema (if inferred).
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> pydict = {'n_legs': n_legs, 'animals': animals}
 |
 |      Construct a Table from a dictionary of arrays:
 |
 |      >>> pa.Table.from_pydict(pydict)
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |      >>> pa.Table.from_pydict(pydict).schema
 |      n_legs: int64
 |      animals: string
 |
 |      Construct a Table from a dictionary of arrays with metadata:
 |
 |      >>> my_metadata={"n_legs": "Number of legs per animal"}
 |      >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |      Construct a Table from a dictionary of arrays with pyarrow schema:
 |
 |      >>> my_schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |      >>> pa.Table.from_pydict(pydict, schema=my_schema).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |  from_pylist(mapping, schema=None, metadata=None)
 |      _Tabular.from_pylist(cls, mapping, schema=None, metadata=None)
 |
 |      Construct a Table or RecordBatch from list of rows / dictionaries.
 |
 |      Parameters
 |      ----------
 |      mapping : list of dicts of rows
 |          A mapping of strings to row values.
 |      schema : Schema, default None
 |          If not passed, will be inferred from the first row of the
 |          mapping values.
 |      metadata : dict or Mapping, default None
 |          Optional metadata for the schema (if inferred).
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'},
 |      ...           {'n_legs': 4, 'animals': 'Dog'}]
 |
 |      Construct a Table from a list of rows:
 |
 |      >>> pa.Table.from_pylist(pylist)
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,4]]
 |      animals: [["Flamingo","Dog"]]
 |
 |      Construct a Table from a list of rows with metadata:
 |
 |      >>> my_metadata={"n_legs": "Number of legs per animal"}
 |      >>> pa.Table.from_pylist(pylist, metadata=my_metadata).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |      Construct a Table from a list of rows with pyarrow schema:
 |
 |      >>> my_schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |      >>> pa.Table.from_pylist(pylist, schema=my_schema).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from _Tabular:
 |
 |  column_names
 |      Names of the Table or RecordBatch columns.
 |
 |      Returns
 |      -------
 |      list of str
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table.column_names
 |      ['n_legs', 'animals']
 |
 |  columns
 |      List of all columns in numerical order.
 |
 |      Returns
 |      -------
 |      columns : list of Array (for RecordBatch) or list of ChunkedArray (for Table)
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
 |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> table = pa.Table.from_pandas(df)
 |      >>> table.columns
 |      [<pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          null,
 |          4,
 |          5,
 |          null
 |        ]
 |      ], <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          "Flamingo",
 |          "Horse",
 |          null,
 |          "Centipede"
 |        ]
 |      ]]
 |
 |  shape
 |      Dimensions of the table or record batch: (#rows, #columns).
 |
 |      Returns
 |      -------
 |      (int, int)
 |          Number of rows and number of columns.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
 |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> table.shape
 |      (4, 2)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from _Tabular:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class RecordBatchFileReader in module pyarrow.ipc

class RecordBatchFileReader(pyarrow.lib._RecordBatchFileReader)
 |  RecordBatchFileReader(source, footer_offset=None, *, options=None, memory_pool=None)
 |
 |  Class for reading Arrow record batch data from the Arrow binary file format
 |
 |  Parameters
 |  ----------
 |  source : bytes/buffer-like, pyarrow.NativeFile, or file-like Python object
 |      Either an in-memory buffer, or a readable file object.
 |      If you want to use memory map use MemoryMappedFile as source.
 |  footer_offset : int, default None
 |      If the file is embedded in some larger file, this is the byte offset to
 |      the very end of the file data
 |  options : pyarrow.ipc.IpcReadOptions
 |      Options for IPC serialization.
 |      If None, default values will be used.
 |  memory_pool : MemoryPool, default None
 |      If None, default memory pool is used.
 |
 |  Method resolution order:
 |      RecordBatchFileReader
 |      pyarrow.lib._RecordBatchFileReader
 |      pyarrow.lib._Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, source, footer_offset=None, *, options=None, memory_pool=None)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  __dict__
 |      dictionary for instance variables
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyarrow.lib._RecordBatchFileReader:
 |
 |  __enter__(self)
 |      _RecordBatchFileReader.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, traceback)
 |      _RecordBatchFileReader.__exit__(self, exc_type, exc_value, traceback)
 |
 |  __reduce__ = __reduce_cython__(...)
 |      _RecordBatchFileReader.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _RecordBatchFileReader.__setstate_cython__(self, __pyx_state)
 |
 |  get_batch(self, i)
 |      _RecordBatchFileReader.get_batch(self, int i)
 |
 |      Read the record batch with the given index.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          The index of the record batch in the IPC file.
 |
 |      Returns
 |      -------
 |      batch : RecordBatch
 |
 |  get_batch_with_custom_metadata(self, i)
 |      _RecordBatchFileReader.get_batch_with_custom_metadata(self, int i)
 |
 |      Read the record batch with the given index along with
 |      its custom metadata
 |
 |      Parameters
 |      ----------
 |      i : int
 |          The index of the record batch in the IPC file.
 |
 |      Returns
 |      -------
 |      batch : RecordBatch
 |      custom_metadata : KeyValueMetadata
 |
 |  get_record_batch = get_batch(self, i)
 |
 |  read_all(self)
 |      _RecordBatchFileReader.read_all(self)
 |
 |      Read all record batches as a pyarrow.Table
 |
 |  read_pandas(self, **options)
 |      _ReadPandasMixin.read_pandas(self, **options)
 |
 |      Read contents of stream to a pandas.DataFrame.
 |
 |      Read all record batches as a pyarrow.Table then convert it to a
 |      pandas.DataFrame using Table.to_pandas.
 |
 |      Parameters
 |      ----------
 |      **options
 |          Arguments to forward to :meth:`Table.to_pandas`.
 |
 |      Returns
 |      -------
 |      df : pandas.DataFrame
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from pyarrow.lib._RecordBatchFileReader:
 |
 |  __new__(*args, **kwargs) class method of pyarrow.lib._RecordBatchFileReader
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from pyarrow.lib._RecordBatchFileReader:
 |
 |  metadata
 |      File-level custom metadata as dict, where both keys and values are byte-like.
 |      This kind of metadata can be written via ``ipc.new_file(..., metadata=...)``.
 |
 |  num_record_batches
 |      The number of record batches in the IPC file.
 |
 |  schema
 |
 |  stats
 |      Current IPC read statistics.

Python Library Documentation: class RecordBatchFileWriter in module pyarrow.ipc

class RecordBatchFileWriter(pyarrow.lib._RecordBatchFileWriter)
 |  RecordBatchFileWriter(sink, schema, *, options=None, metadata=None)
 |
 |  Writer to create the Arrow binary file format
 |
 |  Parameters
 |  ----------
 |  sink : str, pyarrow.NativeFile, or file-like Python object
 |      Either a file path, or a writable file object.
 |  schema : pyarrow.Schema
 |      The Arrow schema for data to be written to the file.
 |  options : pyarrow.ipc.IpcWriteOptions
 |      Options for IPC serialization.
 |
 |      If None, default values will be used: the legacy format will not
 |      be used unless overridden by setting the environment variable
 |      ARROW_PRE_0_15_IPC_FORMAT=1, and the V5 metadata version will be
 |      used unless overridden by setting the environment variable
 |      ARROW_PRE_1_0_METADATA_VERSION=1.
 |  metadata : dict | pyarrow.KeyValueMetadata, optional
 |      Key/value pairs (both must be bytes-like) that will be stored
 |      in the file footer and are retrievable via
 |      pyarrow.ipc.open_file(...).metadata.
 |
 |  Method resolution order:
 |      RecordBatchFileWriter
 |      pyarrow.lib._RecordBatchFileWriter
 |      pyarrow.lib._RecordBatchStreamWriter
 |      pyarrow.lib._CRecordBatchWriter
 |      pyarrow.lib._Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, sink, schema, *, options=None, metadata=None)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  __dict__
 |      dictionary for instance variables
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyarrow.lib._RecordBatchFileWriter:
 |
 |  __reduce__ = __reduce_cython__(...)
 |      _RecordBatchFileWriter.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _RecordBatchFileWriter.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from pyarrow.lib._RecordBatchFileWriter:
 |
 |  __new__(*args, **kwargs) class method of pyarrow.lib._RecordBatchFileWriter
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyarrow.lib._CRecordBatchWriter:
 |
 |  __enter__(self)
 |      _CRecordBatchWriter.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_val, exc_tb)
 |      _CRecordBatchWriter.__exit__(self, exc_type, exc_val, exc_tb)
 |
 |  close(self)
 |      _CRecordBatchWriter.close(self)
 |
 |      Close stream and write end-of-stream 0 marker.
 |
 |  write(self, table_or_batch)
 |      _CRecordBatchWriter.write(self, table_or_batch)
 |
 |      Write RecordBatch or Table to stream.
 |
 |      Parameters
 |      ----------
 |      table_or_batch : {RecordBatch, Table}
 |
 |  write_batch(self, batch, custom_metadata=None)
 |      _CRecordBatchWriter.write_batch(self, RecordBatch batch, custom_metadata=None)
 |
 |      Write RecordBatch to stream.
 |
 |      Parameters
 |      ----------
 |      batch : RecordBatch
 |      custom_metadata : mapping or KeyValueMetadata
 |          Keys and values must be string-like / coercible to bytes
 |
 |  write_table(self, table, max_chunksize=None)
 |      _CRecordBatchWriter.write_table(self, Table table, max_chunksize=None)
 |
 |      Write Table to stream in (contiguous) RecordBatch objects.
 |
 |      Parameters
 |      ----------
 |      table : Table
 |      max_chunksize : int, default None
 |          Maximum number of rows for RecordBatch chunks. Individual chunks may
 |          be smaller depending on the chunk layout of individual columns.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from pyarrow.lib._CRecordBatchWriter:
 |
 |  stats
 |      Current IPC write statistics.

Python Library Documentation: class RecordBatchReader in module pyarrow.lib

class RecordBatchReader(_Weakrefable)
 |  RecordBatchReader()
 |
 |  Base class for reading stream of record batches.
 |
 |  Record batch readers function as iterators of record batches that also
 |  provide the schema (without the need to get any batches).
 |
 |  Warnings
 |  --------
 |  Do not call this class's constructor directly, use one of the
 |  ``RecordBatchReader.from_*`` functions instead.
 |
 |  Notes
 |  -----
 |  To import and export using the Arrow C stream interface, use the
 |  ``_import_from_c`` and ``_export_to_c`` methods. However, keep in mind this
 |  interface is intended for expert users.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |  >>> schema = pa.schema([('x', pa.int64())])
 |  >>> def iter_record_batches():
 |  ...     for i in range(2):
 |  ...         yield pa.RecordBatch.from_arrays([pa.array([1, 2, 3])], schema=schema)
 |  >>> reader = pa.RecordBatchReader.from_batches(schema, iter_record_batches())
 |  >>> print(reader.schema)
 |  x: int64
 |  >>> for batch in reader:
 |  ...     print(batch)
 |  pyarrow.RecordBatch
 |  x: int64
 |  ----
 |  x: [1,2,3]
 |  pyarrow.RecordBatch
 |  x: int64
 |  ----
 |  x: [1,2,3]
 |
 |  Method resolution order:
 |      RecordBatchReader
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_c_stream__(self, requested_schema=None)
 |      RecordBatchReader.__arrow_c_stream__(self, requested_schema=None)
 |
 |      Export to a C ArrowArrayStream PyCapsule.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule, default None
 |          The schema to which the stream should be casted, passed as a
 |          PyCapsule containing a C ArrowSchema representation of the
 |          requested schema.
 |
 |      Returns
 |      -------
 |      PyCapsule
 |          A capsule containing a C ArrowArrayStream struct.
 |
 |  __enter__(self)
 |      RecordBatchReader.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_val, exc_tb)
 |      RecordBatchReader.__exit__(self, exc_type, exc_val, exc_tb)
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __reduce__ = __reduce_cython__(...)
 |      RecordBatchReader.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      RecordBatchReader.__setstate_cython__(self, __pyx_state)
 |
 |  cast(self, target_schema)
 |      RecordBatchReader.cast(self, target_schema)
 |
 |      Wrap this reader with one that casts each batch lazily as it is pulled.
 |      Currently only a safe cast to target_schema is implemented.
 |
 |      Parameters
 |      ----------
 |      target_schema : Schema
 |          Schema to cast to, the names and order of fields must match.
 |
 |      Returns
 |      -------
 |      RecordBatchReader
 |
 |  close(self)
 |      RecordBatchReader.close(self)
 |
 |      Release any resources associated with the reader.
 |
 |  iter_batches_with_custom_metadata(self)
 |      RecordBatchReader.iter_batches_with_custom_metadata(self)
 |
 |      Iterate over record batches from the stream along with their custom
 |      metadata.
 |
 |      Yields
 |      ------
 |      RecordBatchWithMetadata
 |
 |  read_all(self)
 |      RecordBatchReader.read_all(self)
 |
 |      Read all record batches as a pyarrow.Table.
 |
 |      Returns
 |      -------
 |      Table
 |
 |  read_next_batch(self)
 |      RecordBatchReader.read_next_batch(self)
 |
 |      Read next RecordBatch from the stream.
 |
 |      Raises
 |      ------
 |      StopIteration:
 |          At end of stream.
 |
 |      Returns
 |      -------
 |      RecordBatch
 |
 |  read_next_batch_with_custom_metadata(self)
 |      RecordBatchReader.read_next_batch_with_custom_metadata(self)
 |
 |      Read next RecordBatch from the stream along with its custom metadata.
 |
 |      Raises
 |      ------
 |      StopIteration:
 |          At end of stream.
 |
 |      Returns
 |      -------
 |      batch : RecordBatch
 |      custom_metadata : KeyValueMetadata
 |
 |  read_pandas(self, **options)
 |      _ReadPandasMixin.read_pandas(self, **options)
 |
 |      Read contents of stream to a pandas.DataFrame.
 |
 |      Read all record batches as a pyarrow.Table then convert it to a
 |      pandas.DataFrame using Table.to_pandas.
 |
 |      Parameters
 |      ----------
 |      **options
 |          Arguments to forward to :meth:`Table.to_pandas`.
 |
 |      Returns
 |      -------
 |      df : pandas.DataFrame
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_batches(schema, batches)
 |      RecordBatchReader.from_batches(Schema schema, batches)
 |
 |      Create RecordBatchReader from an iterable of batches.
 |
 |      Parameters
 |      ----------
 |      schema : Schema
 |          The shared schema of the record batches
 |      batches : Iterable[RecordBatch]
 |          The batches that this reader will return.
 |
 |      Returns
 |      -------
 |      reader : RecordBatchReader
 |
 |  from_stream(data, schema=None)
 |      RecordBatchReader.from_stream(data, schema=None)
 |
 |      Create RecordBatchReader from a Arrow-compatible stream object.
 |
 |      This accepts objects implementing the Arrow PyCapsule Protocol for
 |      streams, i.e. objects that have a ``__arrow_c_stream__`` method.
 |
 |      Parameters
 |      ----------
 |      data : Arrow-compatible stream object
 |          Any object that implements the Arrow PyCapsule Protocol for
 |          streams.
 |      schema : Schema, default None
 |          The schema to which the stream should be casted, if supported
 |          by the stream object.
 |
 |      Returns
 |      -------
 |      RecordBatchReader
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  schema
 |      Shared schema of the record batches in the stream.
 |
 |      Returns
 |      -------
 |      Schema

Python Library Documentation: class RecordBatchStreamReader in module pyarrow.ipc

class RecordBatchStreamReader(pyarrow.lib._RecordBatchStreamReader)
 |  RecordBatchStreamReader(source, *, options=None, memory_pool=None)
 |
 |  Reader for the Arrow streaming binary format.
 |
 |  Parameters
 |  ----------
 |  source : bytes/buffer-like, pyarrow.NativeFile, or file-like Python object
 |      Either an in-memory buffer, or a readable file object.
 |      If you want to use memory map use MemoryMappedFile as source.
 |  options : pyarrow.ipc.IpcReadOptions
 |      Options for IPC deserialization.
 |      If None, default values will be used.
 |  memory_pool : MemoryPool, default None
 |      If None, default memory pool is used.
 |
 |  Method resolution order:
 |      RecordBatchStreamReader
 |      pyarrow.lib._RecordBatchStreamReader
 |      pyarrow.lib.RecordBatchReader
 |      pyarrow.lib._Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, source, *, options=None, memory_pool=None)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  __dict__
 |      dictionary for instance variables
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyarrow.lib._RecordBatchStreamReader:
 |
 |  __reduce__ = __reduce_cython__(...)
 |      _RecordBatchStreamReader.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _RecordBatchStreamReader.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from pyarrow.lib._RecordBatchStreamReader:
 |
 |  __new__(*args, **kwargs) class method of pyarrow.lib._RecordBatchStreamReader
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from pyarrow.lib._RecordBatchStreamReader:
 |
 |  stats
 |      Current IPC read statistics.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyarrow.lib.RecordBatchReader:
 |
 |  __arrow_c_stream__(self, requested_schema=None)
 |      RecordBatchReader.__arrow_c_stream__(self, requested_schema=None)
 |
 |      Export to a C ArrowArrayStream PyCapsule.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule, default None
 |          The schema to which the stream should be casted, passed as a
 |          PyCapsule containing a C ArrowSchema representation of the
 |          requested schema.
 |
 |      Returns
 |      -------
 |      PyCapsule
 |          A capsule containing a C ArrowArrayStream struct.
 |
 |  __enter__(self)
 |      RecordBatchReader.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_val, exc_tb)
 |      RecordBatchReader.__exit__(self, exc_type, exc_val, exc_tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  cast(self, target_schema)
 |      RecordBatchReader.cast(self, target_schema)
 |
 |      Wrap this reader with one that casts each batch lazily as it is pulled.
 |      Currently only a safe cast to target_schema is implemented.
 |
 |      Parameters
 |      ----------
 |      target_schema : Schema
 |          Schema to cast to, the names and order of fields must match.
 |
 |      Returns
 |      -------
 |      RecordBatchReader
 |
 |  close(self)
 |      RecordBatchReader.close(self)
 |
 |      Release any resources associated with the reader.
 |
 |  iter_batches_with_custom_metadata(self)
 |      RecordBatchReader.iter_batches_with_custom_metadata(self)
 |
 |      Iterate over record batches from the stream along with their custom
 |      metadata.
 |
 |      Yields
 |      ------
 |      RecordBatchWithMetadata
 |
 |  read_all(self)
 |      RecordBatchReader.read_all(self)
 |
 |      Read all record batches as a pyarrow.Table.
 |
 |      Returns
 |      -------
 |      Table
 |
 |  read_next_batch(self)
 |      RecordBatchReader.read_next_batch(self)
 |
 |      Read next RecordBatch from the stream.
 |
 |      Raises
 |      ------
 |      StopIteration:
 |          At end of stream.
 |
 |      Returns
 |      -------
 |      RecordBatch
 |
 |  read_next_batch_with_custom_metadata(self)
 |      RecordBatchReader.read_next_batch_with_custom_metadata(self)
 |
 |      Read next RecordBatch from the stream along with its custom metadata.
 |
 |      Raises
 |      ------
 |      StopIteration:
 |          At end of stream.
 |
 |      Returns
 |      -------
 |      batch : RecordBatch
 |      custom_metadata : KeyValueMetadata
 |
 |  read_pandas(self, **options)
 |      _ReadPandasMixin.read_pandas(self, **options)
 |
 |      Read contents of stream to a pandas.DataFrame.
 |
 |      Read all record batches as a pyarrow.Table then convert it to a
 |      pandas.DataFrame using Table.to_pandas.
 |
 |      Parameters
 |      ----------
 |      **options
 |          Arguments to forward to :meth:`Table.to_pandas`.
 |
 |      Returns
 |      -------
 |      df : pandas.DataFrame
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from pyarrow.lib.RecordBatchReader:
 |
 |  from_batches(schema, batches)
 |      RecordBatchReader.from_batches(Schema schema, batches)
 |
 |      Create RecordBatchReader from an iterable of batches.
 |
 |      Parameters
 |      ----------
 |      schema : Schema
 |          The shared schema of the record batches
 |      batches : Iterable[RecordBatch]
 |          The batches that this reader will return.
 |
 |      Returns
 |      -------
 |      reader : RecordBatchReader
 |
 |  from_stream(data, schema=None)
 |      RecordBatchReader.from_stream(data, schema=None)
 |
 |      Create RecordBatchReader from a Arrow-compatible stream object.
 |
 |      This accepts objects implementing the Arrow PyCapsule Protocol for
 |      streams, i.e. objects that have a ``__arrow_c_stream__`` method.
 |
 |      Parameters
 |      ----------
 |      data : Arrow-compatible stream object
 |          Any object that implements the Arrow PyCapsule Protocol for
 |          streams.
 |      schema : Schema, default None
 |          The schema to which the stream should be casted, if supported
 |          by the stream object.
 |
 |      Returns
 |      -------
 |      RecordBatchReader
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from pyarrow.lib.RecordBatchReader:
 |
 |  schema
 |      Shared schema of the record batches in the stream.
 |
 |      Returns
 |      -------
 |      Schema

Python Library Documentation: class RecordBatchStreamWriter in module pyarrow.ipc

class RecordBatchStreamWriter(pyarrow.lib._RecordBatchStreamWriter)
 |  RecordBatchStreamWriter(sink, schema, *, options=None)
 |
 |  Writer for the Arrow streaming binary format
 |
 |  Parameters
 |  ----------
 |  sink : str, pyarrow.NativeFile, or file-like Python object
 |      Either a file path, or a writable file object.
 |  schema : pyarrow.Schema
 |      The Arrow schema for data to be written to the file.
 |  options : pyarrow.ipc.IpcWriteOptions
 |      Options for IPC serialization.
 |
 |      If None, default values will be used: the legacy format will not
 |      be used unless overridden by setting the environment variable
 |      ARROW_PRE_0_15_IPC_FORMAT=1, and the V5 metadata version will be
 |      used unless overridden by setting the environment variable
 |      ARROW_PRE_1_0_METADATA_VERSION=1.
 |
 |  Method resolution order:
 |      RecordBatchStreamWriter
 |      pyarrow.lib._RecordBatchStreamWriter
 |      pyarrow.lib._CRecordBatchWriter
 |      pyarrow.lib._Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, sink, schema, *, options=None)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  __dict__
 |      dictionary for instance variables
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyarrow.lib._RecordBatchStreamWriter:
 |
 |  __reduce__ = __reduce_cython__(...)
 |      _RecordBatchStreamWriter.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _RecordBatchStreamWriter.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from pyarrow.lib._RecordBatchStreamWriter:
 |
 |  __new__(*args, **kwargs) class method of pyarrow.lib._RecordBatchStreamWriter
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyarrow.lib._CRecordBatchWriter:
 |
 |  __enter__(self)
 |      _CRecordBatchWriter.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_val, exc_tb)
 |      _CRecordBatchWriter.__exit__(self, exc_type, exc_val, exc_tb)
 |
 |  close(self)
 |      _CRecordBatchWriter.close(self)
 |
 |      Close stream and write end-of-stream 0 marker.
 |
 |  write(self, table_or_batch)
 |      _CRecordBatchWriter.write(self, table_or_batch)
 |
 |      Write RecordBatch or Table to stream.
 |
 |      Parameters
 |      ----------
 |      table_or_batch : {RecordBatch, Table}
 |
 |  write_batch(self, batch, custom_metadata=None)
 |      _CRecordBatchWriter.write_batch(self, RecordBatch batch, custom_metadata=None)
 |
 |      Write RecordBatch to stream.
 |
 |      Parameters
 |      ----------
 |      batch : RecordBatch
 |      custom_metadata : mapping or KeyValueMetadata
 |          Keys and values must be string-like / coercible to bytes
 |
 |  write_table(self, table, max_chunksize=None)
 |      _CRecordBatchWriter.write_table(self, Table table, max_chunksize=None)
 |
 |      Write Table to stream in (contiguous) RecordBatch objects.
 |
 |      Parameters
 |      ----------
 |      table : Table
 |      max_chunksize : int, default None
 |          Maximum number of rows for RecordBatch chunks. Individual chunks may
 |          be smaller depending on the chunk layout of individual columns.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from pyarrow.lib._CRecordBatchWriter:
 |
 |  stats
 |      Current IPC write statistics.

Python Library Documentation: class ResizableBuffer in module pyarrow.lib

class ResizableBuffer(Buffer)
 |  A base class for buffers that can be resized.
 |
 |  Method resolution order:
 |      ResizableBuffer
 |      Buffer
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  resize(self, new_size, shrink_to_fit=False)
 |      ResizableBuffer.resize(self, int64_t new_size, shrink_to_fit=False)
 |
 |      Resize buffer to indicated size.
 |
 |      Parameters
 |      ----------
 |      new_size : int
 |          New size of buffer (padding may be added internally).
 |      shrink_to_fit : bool, default False
 |          If this is true, the buffer is shrunk when new_size is less
 |          than the current size.
 |          If this is false, the buffer is never shrunk.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Buffer:
 |
 |  __buffer__(self, flags, /)
 |      Return a buffer object that exposes the underlying memory of the object.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce_ex__(self, protocol)
 |      Buffer.__reduce_ex__(self, protocol)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  equals(self, other)
 |      Buffer.equals(self, Buffer other)
 |
 |      Determine if two buffers contain exactly the same data.
 |
 |      Parameters
 |      ----------
 |      other : Buffer
 |
 |      Returns
 |      -------
 |      are_equal : bool
 |          True if buffer contents and size are equal
 |
 |  hex(self)
 |      Buffer.hex(self)
 |
 |      Compute hexadecimal representation of the buffer.
 |
 |      Returns
 |      -------
 |      : bytes
 |
 |  slice(self, offset=0, length=None)
 |      Buffer.slice(self, offset=0, length=None)
 |
 |      Slice this buffer.  Memory is not copied.
 |
 |      You can also use the Python slice notation ``buffer[start:stop]``.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of buffer to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Buffer starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Buffer
 |          A logical view over this buffer.
 |
 |  to_pybytes(self)
 |      Buffer.to_pybytes(self)
 |
 |      Return this buffer as a Python bytes object. Memory is copied.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Buffer:
 |
 |  address
 |      The buffer's address, as an integer.
 |
 |      The returned address may point to CPU or device memory.
 |      Use `is_cpu()` to disambiguate.
 |
 |  device
 |      The device where the buffer resides.
 |
 |      Returns
 |      -------
 |      Device
 |
 |  device_type
 |      The device type where the buffer resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the buffer is CPU-accessible.
 |
 |  is_mutable
 |      Whether the buffer is mutable.
 |
 |  memory_manager
 |      The memory manager associated with the buffer.
 |
 |      Returns
 |      -------
 |      MemoryManager
 |
 |  parent
 |
 |  size
 |      The buffer size in bytes.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Buffer:
 |
 |  __hash__ = None

Python Library Documentation: class RunEndEncodedArray in module pyarrow.lib

class RunEndEncodedArray(Array)
 |  Concrete class for Arrow run-end encoded arrays.
 |
 |  Method resolution order:
 |      RunEndEncodedArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  find_physical_length(self)
 |      RunEndEncodedArray.find_physical_length(self)
 |
 |      Find the physical length of this REE array.
 |
 |      The physical length of an REE is the number of physical values (and
 |      run-ends) necessary to represent the logical range of values from offset
 |      to length.
 |
 |      This function uses binary-search, so it has a O(log N) cost.
 |
 |  find_physical_offset(self)
 |      RunEndEncodedArray.find_physical_offset(self)
 |
 |      Find the physical offset of this REE array.
 |
 |      This is the offset of the run that contains the value of the first
 |      logical element of this array considering its offset.
 |
 |      This function uses binary-search, so it has a O(log N) cost.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_arrays(run_ends, values, type=None)
 |      RunEndEncodedArray.from_arrays(run_ends, values, type=None)
 |
 |      Construct RunEndEncodedArray from run_ends and values arrays.
 |
 |      Parameters
 |      ----------
 |      run_ends : Array (int16, int32, or int64 type)
 |          The run_ends array.
 |      values : Array (any type)
 |          The values array.
 |      type : pyarrow.DataType, optional
 |          The run_end_encoded(run_end_type, value_type) array type.
 |
 |      Returns
 |      -------
 |      RunEndEncodedArray
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      RunEndEncodedArray.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct a RunEndEncodedArray from all the parameters that make up an
 |      Array.
 |
 |      RunEndEncodedArrays do not have buffers, only children arrays, but this
 |      implementation is needed to satisfy the Array interface.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The run_end_encoded(run_end_type, value_type) type.
 |      length : int
 |          The logical length of the run-end encoded array. Expected to match
 |          the last value of the run_ends array (children[0]) minus the offset.
 |      buffers : List[Buffer]
 |          Empty List or [None].
 |      null_count : int, default -1
 |          The number of null entries in the array. Run-end encoded arrays
 |          are specified to not have valid bits and null_count always equals 0.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array]
 |          Nested type children containing the run_ends and values arrays.
 |
 |      Returns
 |      -------
 |      RunEndEncodedArray
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  run_ends
 |      An array holding the logical indexes of each run-end.
 |
 |      The physical offset to the array is applied.
 |
 |  values
 |      An array holding the values of each run.
 |
 |      The physical offset to the array is applied.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class RunEndEncodedScalar in module pyarrow.lib

class RunEndEncodedScalar(Scalar)
 |  Concrete class for RunEndEncoded scalars.
 |
 |  Method resolution order:
 |      RunEndEncodedScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      RunEndEncodedScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return underlying value as a Python object.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value
 |      Return underlying value as a scalar.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class RunEndEncodedType in module pyarrow.lib

class RunEndEncodedType(DataType)
 |  Concrete class for run-end encoded types.
 |
 |  Method resolution order:
 |      RunEndEncodedType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      RunEndEncodedType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  run_end_type
 |
 |  value_type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class RuntimeInfo in module importlib._bootstrap

class RuntimeInfo(builtins.tuple)
 |  RuntimeInfo(simd_level, detected_simd_level)
 |
 |  RuntimeInfo(simd_level, detected_simd_level)
 |
 |  Method resolution order:
 |      RuntimeInfo
 |      builtins.tuple
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getnewargs__(self) from collections.RuntimeInfo
 |      Return self as a plain tuple.  Used by copy and pickle.
 |
 |  __repr__(self) from collections.RuntimeInfo
 |      Return a nicely formatted representation string
 |
 |  _asdict(self) from collections.RuntimeInfo
 |      Return a new dict which maps field names to their values.
 |
 |  _replace(self, /, **kwds) from collections.RuntimeInfo
 |      Return a new RuntimeInfo object replacing specified fields with new values
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  _make(iterable) from collections.RuntimeInfo
 |      Make a new RuntimeInfo object from a sequence or iterable
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(_cls, simd_level, detected_simd_level) from namedtuple_RuntimeInfo.RuntimeInfo
 |      Create new instance of RuntimeInfo(simd_level, detected_simd_level)
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  simd_level
 |      Alias for field number 0
 |
 |  detected_simd_level
 |      Alias for field number 1
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __match_args__ = ('simd_level', 'detected_simd_level')
 |
 |  _field_defaults = {}
 |
 |  _fields = ('simd_level', 'detected_simd_level')
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.tuple:
 |
 |  __add__(self, value, /)
 |      Return self+value.
 |
 |  __contains__(self, key, /)
 |      Return bool(key in self).
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(self, value, /)
 |      Return self*value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  count(self, value, /)
 |      Return number of occurrences of value.
 |
 |  index(self, value, start=0, stop=9223372036854775807, /)
 |      Return first index of value.
 |
 |      Raises ValueError if the value is not present.
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from builtins.tuple:
 |
 |  __class_getitem__(...)
 |      See PEP 585

Python Library Documentation: class Scalar in module pyarrow.lib

class Scalar(_Weakrefable)
 |  Scalar()
 |
 |  The base class for scalars.
 |
 |  Notes
 |  -----
 |  This class supports Python's standard operators
 |  for element-wise operations, i.e. arithmetic (`+`, `-`, `/`, `%`, `**`),
 |  bitwise (`&`, `|`, `^`, `>>`, `<<`) and others.
 |  They can be used directly instead of calling underlying
 |  `pyarrow.compute` functions explicitly.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |  >>> pa.scalar(42) + pa.scalar(17)
 |  <pyarrow.Int64Scalar: 59>
 |
 |  >>> pa.scalar(6) ** 3
 |  <pyarrow.Int64Scalar: 216>
 |
 |  >>> arr = pa.array([1, 2, 3], type=pa.int8())
 |  >>> val = pa.scalar(42)
 |  >>> val - arr
 |  <pyarrow.lib.Int64Array object at ...>
 |  [
 |    41,
 |    40,
 |    39
 |  ]
 |
 |  Method resolution order:
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python representation.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class Schema in module pyarrow.lib

class Schema(_Weakrefable)
 |  Schema()
 |
 |  A named collection of types a.k.a schema. A schema defines the
 |  column names and types in a record batch or table data structure.
 |  They also contain metadata about the columns. For example, schemas
 |  converted from Pandas contain metadata about their original Pandas
 |  types so they can be converted back to the same types.
 |
 |  Warnings
 |  --------
 |  Do not call this class's constructor directly. Instead use
 |  :func:`pyarrow.schema` factory function which makes a new Arrow
 |  Schema object.
 |
 |  Examples
 |  --------
 |  Create a new Arrow Schema object:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.schema([
 |  ...     ('some_int', pa.int32()),
 |  ...     ('some_string', pa.string())
 |  ... ])
 |  some_int: int32
 |  some_string: string
 |
 |  Create Arrow Schema with metadata:
 |
 |  >>> pa.schema([
 |  ...     pa.field('n_legs', pa.int64()),
 |  ...     pa.field('animals', pa.string())],
 |  ...     metadata={"n_legs": "Number of legs per animal"})
 |  n_legs: int64
 |  animals: string
 |  -- schema metadata --
 |  n_legs: 'Number of legs per animal'
 |
 |  Method resolution order:
 |      Schema
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_c_schema__(self)
 |      Schema.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      Schema.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __sizeof__(self)
 |      Schema.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  add_metadata(self, metadata)
 |      Schema.add_metadata(self, metadata)
 |
 |      DEPRECATED
 |
 |      Parameters
 |      ----------
 |      metadata : dict
 |          Keys and values must be string-like / coercible to bytes
 |
 |  append(self, field)
 |      Schema.append(self, Field field)
 |
 |      Append a field at the end of the schema.
 |
 |      In contrast to Python's ``list.append()`` it does return a new
 |      object, leaving the original Schema unmodified.
 |
 |      Parameters
 |      ----------
 |      field : Field
 |
 |      Returns
 |      -------
 |      schema: Schema
 |          New object with appended field.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())])
 |
 |      Append a field 'extra' at the end of the schema:
 |
 |      >>> schema_new = schema.append(pa.field('extra', pa.bool_()))
 |      >>> schema_new
 |      n_legs: int64
 |      animals: string
 |      extra: bool
 |
 |      Original schema is unmodified:
 |
 |      >>> schema
 |      n_legs: int64
 |      animals: string
 |
 |  empty_table(self)
 |      Schema.empty_table(self)
 |
 |      Provide an empty table according to the schema.
 |
 |      Returns
 |      -------
 |      table: pyarrow.Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())])
 |
 |      Create an empty table with schema's fields:
 |
 |      >>> schema.empty_table()
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[]]
 |      animals: [[]]
 |
 |  equals(self, other, check_metadata=False)
 |      Schema.equals(self, Schema other, bool check_metadata=False)
 |
 |      Test if this schema is equal to the other
 |
 |      Parameters
 |      ----------
 |      other :  pyarrow.Schema
 |      check_metadata : bool, default False
 |          Key/value metadata must be equal too
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema1 = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |      >>> schema2 = pa.schema([
 |      ...     ('some_int', pa.int32()),
 |      ...     ('some_string', pa.string())
 |      ... ])
 |
 |      Test two equal schemas:
 |
 |      >>> schema1.equals(schema1)
 |      True
 |
 |      Test two unequal schemas:
 |
 |      >>> schema1.equals(schema2)
 |      False
 |
 |  field(self, i)
 |      Schema.field(self, i)
 |
 |      Select a field by its column name or numeric index.
 |
 |      Parameters
 |      ----------
 |      i : int or string
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())])
 |
 |      Select the second field:
 |
 |      >>> schema.field(1)
 |      pyarrow.Field<animals: string>
 |
 |      Select the field of the column named 'n_legs':
 |
 |      >>> schema.field('n_legs')
 |      pyarrow.Field<n_legs: int64>
 |
 |  field_by_name(self, name)
 |      Schema.field_by_name(self, name)
 |
 |      DEPRECATED
 |
 |      Parameters
 |      ----------
 |      name : str
 |
 |      Returns
 |      -------
 |      field: pyarrow.Field
 |
 |  get_all_field_indices(self, name)
 |      Schema.get_all_field_indices(self, name)
 |
 |      Return sorted list of indices for the fields with the given name.
 |
 |      Parameters
 |      ----------
 |      name : str
 |          The name of the field to look up.
 |
 |      Returns
 |      -------
 |      indices : List[int]
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string()),
 |      ...     pa.field('animals', pa.bool_())])
 |
 |      Get the indexes of the fields named 'animals':
 |
 |      >>> schema.get_all_field_indices("animals")
 |      [1, 2]
 |
 |  get_field_index(self, name)
 |      Schema.get_field_index(self, name)
 |
 |      Return index of the unique field with the given name.
 |
 |      Parameters
 |      ----------
 |      name : str
 |          The name of the field to look up.
 |
 |      Returns
 |      -------
 |      index : int
 |          The index of the field with the given name; -1 if the
 |          name isn't found or there are several fields with the given
 |          name.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())])
 |
 |      Get the index of the field named 'animals':
 |
 |      >>> schema.get_field_index("animals")
 |      1
 |
 |      Index in case of several fields with the given name:
 |
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string()),
 |      ...     pa.field('animals', pa.bool_())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |      >>> schema.get_field_index("animals")
 |      -1
 |
 |  insert(self, i, field)
 |      Schema.insert(self, int i, Field field)
 |
 |      Add a field at position i to the schema.
 |
 |      Parameters
 |      ----------
 |      i : int
 |      field : Field
 |
 |      Returns
 |      -------
 |      schema: Schema
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())])
 |
 |      Insert a new field on the second position:
 |
 |      >>> schema.insert(1, pa.field('extra', pa.bool_()))
 |      n_legs: int64
 |      extra: bool
 |      animals: string
 |
 |  remove(self, i)
 |      Schema.remove(self, int i)
 |
 |      Remove the field at index i from the schema.
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      schema: Schema
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())])
 |
 |      Remove the second field of the schema:
 |
 |      >>> schema.remove(1)
 |      n_legs: int64
 |
 |  remove_metadata(self)
 |      Schema.remove_metadata(self)
 |
 |      Create new schema without metadata, if any
 |
 |      Returns
 |      -------
 |      schema : pyarrow.Schema
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |      >>> schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |      Create a new schema with removing the metadata from the original:
 |
 |      >>> schema.remove_metadata()
 |      n_legs: int64
 |      animals: string
 |
 |  serialize(self, memory_pool=None)
 |      Schema.serialize(self, memory_pool=None)
 |
 |      Write Schema to Buffer as encapsulated IPC message
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Uses default memory pool if not specified
 |
 |      Returns
 |      -------
 |      serialized : Buffer
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())])
 |
 |      Write schema to Buffer:
 |
 |      >>> schema.serialize()
 |      <pyarrow.Buffer address=0x... size=... is_cpu=True is_mutable=True>
 |
 |  set(self, i, field)
 |      Schema.set(self, int i, Field field)
 |
 |      Replace a field at position i in the schema.
 |
 |      Parameters
 |      ----------
 |      i : int
 |      field : Field
 |
 |      Returns
 |      -------
 |      schema: Schema
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())])
 |
 |      Replace the second field of the schema with a new field 'extra':
 |
 |      >>> schema.set(1, pa.field('replaced', pa.bool_()))
 |      n_legs: int64
 |      replaced: bool
 |
 |  to_string(self, truncate_metadata=True, show_field_metadata=True, show_schema_metadata=True, element_size_limit=100)
 |      Schema.to_string(self, truncate_metadata=True, show_field_metadata=True, show_schema_metadata=True, element_size_limit=100)
 |
 |      Return human-readable representation of Schema
 |
 |      Parameters
 |      ----------
 |      truncate_metadata : boolean, default True
 |          Limit metadata key/value display to a single line of ~80 characters
 |          or less
 |      show_field_metadata : boolean, default True
 |          Display Field-level KeyValueMetadata
 |      show_schema_metadata : boolean, default True
 |          Display Schema-level KeyValueMetadata
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |      Returns
 |      -------
 |      str : the formatted output
 |
 |  with_metadata(self, metadata)
 |      Schema.with_metadata(self, metadata)
 |
 |      Add metadata as dict of string keys and values to Schema
 |
 |      Parameters
 |      ----------
 |      metadata : dict
 |          Keys and values must be string-like / coercible to bytes
 |
 |      Returns
 |      -------
 |      schema : pyarrow.Schema
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())])
 |
 |      Add metadata to existing schema field:
 |
 |      >>> schema.with_metadata({"n_legs": "Number of legs per animal"})
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  from_pandas(df, preserve_index=None)
 |      Schema.from_pandas(cls, df, preserve_index=None)
 |
 |      Returns implied schema from DataFrame
 |
 |      Parameters
 |      ----------
 |      df : pandas.DataFrame
 |
 |      preserve_index : bool, optional
 |          Whether to store the index as an additional field in the resulting
 |          ``Schema``. The default of None will store the index as a field,
 |          except for RangeIndex which is stored as metadata only. Use
 |          ``preserve_index=True`` to force it to be stored as a field.
 |
 |      Returns
 |      -------
 |      pyarrow.Schema
 |
 |      Examples
 |      --------
 |      >>> import pandas as pd
 |      >>> import pyarrow as pa
 |      >>> df = pd.DataFrame({
 |      ...     'int': [1, 2],
 |      ...     'str': ['a', 'b']
 |      ... })
 |
 |      Create an Arrow Schema from the schema of a pandas DataFrame:
 |
 |      >>> pa.Schema.from_pandas(df)
 |      int: int64
 |      str: large_string
 |      -- schema metadata --
 |      pandas: '{"index_columns": [{"kind": "range", "name": null, ...
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  metadata
 |      The schema's metadata (if any is set).
 |
 |      Returns
 |      -------
 |      metadata: dict or None
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |
 |      Get the metadata of the schema's fields:
 |
 |      >>> schema.metadata
 |      {b'n_legs': b'Number of legs per animal'}
 |
 |  names
 |      The schema's field names.
 |
 |      Returns
 |      -------
 |      list of str
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())])
 |
 |      Get the names of the schema's fields:
 |
 |      >>> schema.names
 |      ['n_legs', 'animals']
 |
 |  pandas_metadata
 |      Return deserialized-from-JSON pandas metadata field (if it exists)
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
 |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> schema = pa.Table.from_pandas(df).schema
 |
 |      Select pandas metadata field from Arrow Schema:
 |
 |      >>> schema.pandas_metadata
 |      {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, 'stop': 4, 'step': 1}], ...
 |
 |  types
 |      The schema's field types.
 |
 |      Returns
 |      -------
 |      list of DataType
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())])
 |
 |      Get the types of the schema's fields:
 |
 |      >>> schema.types
 |      [DataType(int64), DataType(string)]
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class SparseCOOTensor in module pyarrow.lib

class SparseCOOTensor(_Weakrefable)
 |  SparseCOOTensor()
 |
 |  A sparse COO (COOrdinate) tensor.
 |
 |  COO format stores a sparse tensor as a collection of (indices, values)
 |  pairs. The indices specify the coordinates of non-zero elements, and
 |  the values contain the actual data at those coordinates.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |  >>> import numpy as np
 |  >>> dense_tensor = np.array([[0, 1, 0], [2, 0, 3]], dtype=np.float32)
 |  >>> sparse_coo = pa.SparseCOOTensor.from_dense_numpy(dense_tensor)
 |  >>> sparse_coo
 |  <pyarrow.SparseCOOTensor>
 |  type: float
 |  shape: (2, 3)
 |
 |  Method resolution order:
 |      SparseCOOTensor
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      SparseCOOTensor.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      SparseCOOTensor.__setstate_cython__(self, __pyx_state)
 |
 |  dim_name(self, i)
 |      SparseCOOTensor.dim_name(self, i)
 |
 |      Returns the name of the i-th tensor dimension.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          The physical index of the tensor dimension.
 |
 |      Returns
 |      -------
 |      str
 |
 |  equals(self, other)
 |      SparseCOOTensor.equals(self, SparseCOOTensor other)
 |
 |      Return true if sparse tensors contains exactly equal data.
 |
 |      Parameters
 |      ----------
 |      other : SparseCOOTensor
 |          The other tensor to compare for equality.
 |
 |  to_numpy(self)
 |      SparseCOOTensor.to_numpy(self)
 |
 |      Convert arrow::SparseCOOTensor to numpy.ndarrays with zero copy.
 |
 |  to_pydata_sparse(self)
 |      SparseCOOTensor.to_pydata_sparse(self)
 |
 |      Convert arrow::SparseCOOTensor to pydata/sparse.COO.
 |
 |  to_scipy(self)
 |      SparseCOOTensor.to_scipy(self)
 |
 |      Convert arrow::SparseCOOTensor to scipy.sparse.coo_array.
 |
 |  to_tensor(self)
 |      SparseCOOTensor.to_tensor(self)
 |
 |      Convert arrow::SparseCOOTensor to arrow::Tensor.
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  from_dense_numpy(obj, dim_names=None)
 |      SparseCOOTensor.from_dense_numpy(cls, obj, dim_names=None)
 |
 |      Convert numpy.ndarray to arrow::SparseCOOTensor
 |
 |      Parameters
 |      ----------
 |      obj : numpy.ndarray
 |          Data used to populate the rows.
 |      dim_names : list[str], optional
 |          Names of the dimensions.
 |
 |      Returns
 |      -------
 |      pyarrow.SparseCOOTensor
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_numpy(data, coords, shape, dim_names=None)
 |      SparseCOOTensor.from_numpy(data, coords, shape, dim_names=None)
 |
 |      Create arrow::SparseCOOTensor from numpy.ndarrays
 |
 |      Parameters
 |      ----------
 |      data : numpy.ndarray
 |          Data used to populate the rows.
 |      coords : numpy.ndarray
 |          Coordinates of the data.
 |      shape : tuple
 |          Shape of the tensor.
 |      dim_names : list, optional
 |          Names of the dimensions.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> data = np.array([1.0, 2.0, 3.0], dtype=np.float32)
 |      >>> coords = np.array([[0, 1], [1, 0], [1, 2]], dtype=np.int64)
 |      >>> sparse_coo = pa.SparseCOOTensor.from_numpy(data, coords, shape=(2, 3))
 |      >>> sparse_coo
 |      <pyarrow.SparseCOOTensor>
 |      type: float
 |      shape: (2, 3)
 |
 |  from_pydata_sparse(obj, dim_names=None)
 |      SparseCOOTensor.from_pydata_sparse(obj, dim_names=None)
 |
 |      Convert pydata/sparse.COO to arrow::SparseCOOTensor.
 |
 |      Parameters
 |      ----------
 |      obj : pydata.sparse.COO
 |          The sparse multidimensional array that should be converted.
 |      dim_names : list, optional
 |          Names of the dimensions.
 |
 |  from_scipy(obj, dim_names=None)
 |      SparseCOOTensor.from_scipy(obj, dim_names=None)
 |
 |      Convert scipy.sparse.coo_array or scipy.sparse.coo_matrix to arrow::SparseCOOTensor
 |
 |      Parameters
 |      ----------
 |      obj : scipy.sparse.coo_array or scipy.sparse.coo_matrix
 |          The scipy array or matrix that should be converted.
 |      dim_names : list, optional
 |          Names of the dimensions.
 |
 |  from_tensor(obj)
 |      SparseCOOTensor.from_tensor(obj)
 |
 |      Convert arrow::Tensor to arrow::SparseCOOTensor.
 |
 |      Parameters
 |      ----------
 |      obj : Tensor
 |          The tensor that should be converted.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  dim_names
 |
 |  has_canonical_format
 |
 |  is_mutable
 |
 |  ndim
 |
 |  non_zero_length
 |
 |  shape
 |
 |  size
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class SparseCSCMatrix in module pyarrow.lib

class SparseCSCMatrix(_Weakrefable)
 |  SparseCSCMatrix()
 |
 |  A sparse CSC (Compressed Sparse Column) matrix.
 |
 |  CSC format stores a sparse matrix by compressing the column information.
 |  It uses three arrays: data (non-zero values), indices (row indices),
 |  and indptr (column pointers that indicate where each column starts
 |  in the data array). CSC is the transpose of CSR format.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |  >>> import numpy as np
 |  >>> dense_matrix = np.array([[1, 0, 2], [0, 0, 3]], dtype=np.float64)
 |  >>> sparse_csc = pa.SparseCSCMatrix.from_dense_numpy(dense_matrix)
 |  >>> sparse_csc
 |  <pyarrow.SparseCSCMatrix>
 |  type: double
 |  shape: (2, 3)
 |
 |  Method resolution order:
 |      SparseCSCMatrix
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      SparseCSCMatrix.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      SparseCSCMatrix.__setstate_cython__(self, __pyx_state)
 |
 |  dim_name(self, i)
 |      SparseCSCMatrix.dim_name(self, i)
 |
 |      Returns the name of the i-th tensor dimension.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          The physical index of the tensor dimension.
 |
 |      Returns
 |      -------
 |      str
 |
 |  equals(self, other)
 |      SparseCSCMatrix.equals(self, SparseCSCMatrix other)
 |
 |      Return true if sparse tensors contains exactly equal data
 |
 |      Parameters
 |      ----------
 |      other : SparseCSCMatrix
 |          The other tensor to compare for equality.
 |
 |  to_numpy(self)
 |      SparseCSCMatrix.to_numpy(self)
 |
 |      Convert arrow::SparseCSCMatrix to numpy.ndarrays with zero copy
 |
 |  to_scipy(self)
 |      SparseCSCMatrix.to_scipy(self)
 |
 |      Convert arrow::SparseCSCMatrix to scipy.sparse.csc_array
 |
 |  to_tensor(self)
 |      SparseCSCMatrix.to_tensor(self)
 |
 |      Convert arrow::SparseCSCMatrix to arrow::Tensor
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  from_dense_numpy(obj, dim_names=None)
 |      SparseCSCMatrix.from_dense_numpy(cls, obj, dim_names=None)
 |
 |      Convert numpy.ndarray to arrow::SparseCSCMatrix
 |
 |      Parameters
 |      ----------
 |      obj : numpy.ndarray
 |          Data used to populate the rows.
 |      dim_names : list[str], optional
 |          Names of the dimensions.
 |
 |      Returns
 |      -------
 |      pyarrow.SparseCSCMatrix
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_numpy(data, indptr, indices, shape, dim_names=None)
 |      SparseCSCMatrix.from_numpy(data, indptr, indices, shape, dim_names=None)
 |
 |      Create arrow::SparseCSCMatrix from numpy.ndarrays
 |
 |      Parameters
 |      ----------
 |      data : numpy.ndarray
 |          Data used to populate the sparse matrix.
 |      indptr : numpy.ndarray
 |          Range of the rows,
 |          The i-th row spans from `indptr[i]` to `indptr[i+1]` in the data.
 |      indices : numpy.ndarray
 |          Column indices of the corresponding non-zero values.
 |      shape : tuple
 |          Shape of the matrix.
 |      dim_names : list, optional
 |          Names of the dimensions.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> data = np.array([1.0, 3.0, 2.0], dtype=np.float64)
 |      >>> indptr = np.array([0, 1, 2, 3], dtype=np.int64)
 |      >>> indices = np.array([0, 1, 0], dtype=np.int64)
 |      >>> sparse_csc = pa.SparseCSCMatrix.from_numpy(
 |      ...     data, indptr, indices, shape=(2, 3))
 |      >>> sparse_csc
 |      <pyarrow.SparseCSCMatrix>
 |      type: double
 |      shape: (2, 3)
 |
 |  from_scipy(obj, dim_names=None)
 |      SparseCSCMatrix.from_scipy(obj, dim_names=None)
 |
 |      Convert scipy.sparse.csc_array or scipy.sparse.csc_matrix to arrow::SparseCSCMatrix
 |
 |      Parameters
 |      ----------
 |      obj : scipy.sparse.csc_array or scipy.sparse.csc_matrix
 |          The scipy matrix that should be converted.
 |      dim_names : list, optional
 |          Names of the dimensions.
 |
 |  from_tensor(obj)
 |      SparseCSCMatrix.from_tensor(obj)
 |
 |      Convert arrow::Tensor to arrow::SparseCSCMatrix
 |
 |      Parameters
 |      ----------
 |      obj : Tensor
 |          The dense tensor that should be converted.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  dim_names
 |
 |  is_mutable
 |
 |  ndim
 |
 |  non_zero_length
 |
 |  shape
 |
 |  size
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class SparseCSFTensor in module pyarrow.lib

class SparseCSFTensor(_Weakrefable)
 |  SparseCSFTensor()
 |
 |  A sparse CSF tensor.
 |
 |  CSF is a generalization of compressed sparse row (CSR) index.
 |
 |  CSF index recursively compresses each dimension of a tensor into a set
 |  of prefix trees. Each path from a root to leaf forms one tensor
 |  non-zero index. CSF is implemented with two arrays of buffers and one
 |  arrays of integers.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |  >>> import numpy as np
 |  >>> # Create a 3D sparse tensor
 |  >>> dense_tensor = np.zeros((2, 3, 2), dtype=np.float32)
 |  >>> dense_tensor[0, 1, 0] = 1.0
 |  >>> dense_tensor[1, 2, 1] = 2.0
 |  >>> sparse_csf = pa.SparseCSFTensor.from_dense_numpy(dense_tensor)
 |  >>> sparse_csf
 |  <pyarrow.SparseCSFTensor>
 |  type: float
 |  shape: (2, 3, 2)
 |
 |  Method resolution order:
 |      SparseCSFTensor
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      SparseCSFTensor.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      SparseCSFTensor.__setstate_cython__(self, __pyx_state)
 |
 |  dim_name(self, i)
 |      SparseCSFTensor.dim_name(self, i)
 |
 |      Returns the name of the i-th tensor dimension.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          The physical index of the tensor dimension.
 |
 |      Returns
 |      -------
 |      str
 |
 |  equals(self, other)
 |      SparseCSFTensor.equals(self, SparseCSFTensor other)
 |
 |      Return true if sparse tensors contains exactly equal data
 |
 |      Parameters
 |      ----------
 |      other : SparseCSFTensor
 |          The other tensor to compare for equality.
 |
 |  to_numpy(self)
 |      SparseCSFTensor.to_numpy(self)
 |
 |      Convert arrow::SparseCSFTensor to numpy.ndarrays with zero copy
 |
 |  to_tensor(self)
 |      SparseCSFTensor.to_tensor(self)
 |
 |      Convert arrow::SparseCSFTensor to arrow::Tensor
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  from_dense_numpy(obj, dim_names=None)
 |      SparseCSFTensor.from_dense_numpy(cls, obj, dim_names=None)
 |
 |      Convert numpy.ndarray to arrow::SparseCSFTensor
 |
 |      Parameters
 |      ----------
 |      obj : numpy.ndarray
 |          Data used to populate the rows.
 |      dim_names : list[str], optional
 |          Names of the dimensions.
 |
 |      Returns
 |      -------
 |      pyarrow.SparseCSFTensor
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_numpy(data, indptr, indices, shape, axis_order=None, dim_names=None)
 |      SparseCSFTensor.from_numpy(data, indptr, indices, shape, axis_order=None, dim_names=None)
 |
 |      Create arrow::SparseCSFTensor from numpy.ndarrays
 |
 |      Parameters
 |      ----------
 |      data : numpy.ndarray
 |          Data used to populate the sparse tensor.
 |      indptr : numpy.ndarray
 |          The sparsity structure.
 |          Each two consecutive dimensions in a tensor correspond to
 |          a buffer in indices.
 |          A pair of consecutive values at `indptr[dim][i]`
 |          `indptr[dim][i + 1]` signify a range of nodes in
 |          `indices[dim + 1]` who are children of `indices[dim][i]` node.
 |      indices : numpy.ndarray
 |          Stores values of nodes.
 |          Each tensor dimension corresponds to a buffer in indptr.
 |      shape : tuple
 |          Shape of the matrix.
 |      axis_order : list, optional
 |          the sequence in which dimensions were traversed to
 |          produce the prefix tree.
 |      dim_names : list, optional
 |          Names of the dimensions.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> data = np.array([1.0, 2.0], dtype=np.float32)
 |      >>> indptr = [
 |      ...     np.array([0, 1, 2], dtype=np.int64),
 |      ...     np.array([0, 1, 2], dtype=np.int64),
 |      ... ]
 |      >>> indices = [
 |      ...     np.array([0, 1], dtype=np.int64),
 |      ...     np.array([1, 2], dtype=np.int64),
 |      ...     np.array([0, 1], dtype=np.int64),
 |      ... ]
 |      >>> sparse_csf = pa.SparseCSFTensor.from_numpy(
 |      ...     data, indptr, indices, shape=(2, 3, 2))
 |      >>> sparse_csf
 |      <pyarrow.SparseCSFTensor>
 |      type: float
 |      shape: (2, 3, 2)
 |
 |  from_tensor(obj)
 |      SparseCSFTensor.from_tensor(obj)
 |
 |      Convert arrow::Tensor to arrow::SparseCSFTensor
 |
 |      Parameters
 |      ----------
 |      obj : Tensor
 |          The dense tensor that should be converted.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  dim_names
 |
 |  is_mutable
 |
 |  ndim
 |
 |  non_zero_length
 |
 |  shape
 |
 |  size
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class SparseCSRMatrix in module pyarrow.lib

class SparseCSRMatrix(_Weakrefable)
 |  SparseCSRMatrix()
 |
 |  A sparse CSR (Compressed Sparse Row) matrix.
 |
 |  CSR format stores a sparse matrix by compressing the row information.
 |  It uses three arrays: data (non-zero values), indices (column indices),
 |  and indptr (row pointers that indicate where each row starts in the
 |  data array).
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |  >>> import numpy as np
 |  >>> dense_matrix = np.array([[1, 0, 2], [0, 0, 3]], dtype=np.float64)
 |  >>> sparse_csr = pa.SparseCSRMatrix.from_dense_numpy(dense_matrix)
 |  >>> sparse_csr
 |  <pyarrow.SparseCSRMatrix>
 |  type: double
 |  shape: (2, 3)
 |
 |  Method resolution order:
 |      SparseCSRMatrix
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      SparseCSRMatrix.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      SparseCSRMatrix.__setstate_cython__(self, __pyx_state)
 |
 |  dim_name(self, i)
 |      SparseCSRMatrix.dim_name(self, i)
 |
 |      Returns the name of the i-th tensor dimension.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          The physical index of the tensor dimension.
 |
 |      Returns
 |      -------
 |      str
 |
 |  equals(self, other)
 |      SparseCSRMatrix.equals(self, SparseCSRMatrix other)
 |
 |      Return true if sparse tensors contains exactly equal data.
 |
 |      Parameters
 |      ----------
 |      other : SparseCSRMatrix
 |          The other tensor to compare for equality.
 |
 |  to_numpy(self)
 |      SparseCSRMatrix.to_numpy(self)
 |
 |      Convert arrow::SparseCSRMatrix to numpy.ndarrays with zero copy.
 |
 |  to_scipy(self)
 |      SparseCSRMatrix.to_scipy(self)
 |
 |      Convert arrow::SparseCSRMatrix to scipy.sparse.csr_array.
 |
 |  to_tensor(self)
 |      SparseCSRMatrix.to_tensor(self)
 |
 |      Convert arrow::SparseCSRMatrix to arrow::Tensor.
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  from_dense_numpy(obj, dim_names=None)
 |      SparseCSRMatrix.from_dense_numpy(cls, obj, dim_names=None)
 |
 |      Convert numpy.ndarray to arrow::SparseCSRMatrix
 |
 |      Parameters
 |      ----------
 |      obj : numpy.ndarray
 |          The dense numpy array that should be converted.
 |      dim_names : list, optional
 |          The names of the dimensions.
 |
 |      Returns
 |      -------
 |      pyarrow.SparseCSRMatrix
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_numpy(data, indptr, indices, shape, dim_names=None)
 |      SparseCSRMatrix.from_numpy(data, indptr, indices, shape, dim_names=None)
 |
 |      Create arrow::SparseCSRMatrix from numpy.ndarrays.
 |
 |      Parameters
 |      ----------
 |      data : numpy.ndarray
 |          Data used to populate the sparse matrix.
 |      indptr : numpy.ndarray
 |          Range of the rows,
 |          The i-th row spans from `indptr[i]` to `indptr[i+1]` in the data.
 |      indices : numpy.ndarray
 |          Column indices of the corresponding non-zero values.
 |      shape : tuple
 |          Shape of the matrix.
 |      dim_names : list, optional
 |          Names of the dimensions.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> data = np.array([1.0, 2.0, 3.0], dtype=np.float64)
 |      >>> indptr = np.array([0, 2, 3], dtype=np.int64)
 |      >>> indices = np.array([0, 2, 1], dtype=np.int64)
 |      >>> sparse_csr = pa.SparseCSRMatrix.from_numpy(
 |      ...     data, indptr, indices, shape=(2, 3))
 |      >>> sparse_csr
 |      <pyarrow.SparseCSRMatrix>
 |      type: double
 |      shape: (2, 3)
 |
 |  from_scipy(obj, dim_names=None)
 |      SparseCSRMatrix.from_scipy(obj, dim_names=None)
 |
 |      Convert scipy.sparse.csr_array or scipy.sparse.csr_matrix to arrow::SparseCSRMatrix.
 |
 |      Parameters
 |      ----------
 |      obj : scipy.sparse.csr_array or scipy.sparse.csr_matrix
 |          The scipy matrix that should be converted.
 |      dim_names : list, optional
 |          Names of the dimensions.
 |
 |  from_tensor(obj)
 |      SparseCSRMatrix.from_tensor(obj)
 |
 |      Convert arrow::Tensor to arrow::SparseCSRMatrix.
 |
 |      Parameters
 |      ----------
 |      obj : Tensor
 |          The dense tensor that should be converted.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  dim_names
 |
 |  is_mutable
 |
 |  ndim
 |
 |  non_zero_length
 |
 |  shape
 |
 |  size
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class SparseUnionType in module pyarrow.lib

class SparseUnionType(UnionType)
 |  Concrete class for sparse union types.
 |
 |  Examples
 |  --------
 |  Create an instance of a sparse UnionType using ``pa.union``:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())],
 |  ...          mode=pa.lib.UnionMode_SPARSE),
 |  (SparseUnionType(sparse_union<a: fixed_size_binary[10]=0, b: string=1>),)
 |
 |  Create an instance of a sparse UnionType using ``pa.sparse_union``:
 |
 |  >>> pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |  SparseUnionType(sparse_union<a: fixed_size_binary[10]=0, b: string=1>)
 |
 |  Method resolution order:
 |      SparseUnionType
 |      UnionType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from UnionType:
 |
 |  __getitem__(...)
 |      UnionType.__getitem__(self, i)
 |
 |      Return a child field by its index.
 |
 |      Alias of ``field``.
 |
 |  __iter__(...)
 |      UnionType.__iter__(self)
 |
 |      Iterate over union members, in order.
 |
 |  __len__(...)
 |      UnionType.__len__(self)
 |
 |      Like num_fields().
 |
 |  __reduce__(self)
 |      UnionType.__reduce__(self)
 |
 |  field(self, i)
 |      UnionType.field(self, i) -> Field
 |
 |      Return a child field by its numeric index.
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |      >>> union[0]
 |      pyarrow.Field<a: fixed_size_binary[10]>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from UnionType:
 |
 |  mode
 |      The mode of the union ("dense" or "sparse").
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |      >>> union.mode
 |      'sparse'
 |
 |  type_codes
 |      The type code to indicate each data type in this union.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |      >>> union.type_codes
 |      [0, 1]
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class StringArray in module pyarrow.lib

class StringArray(Array)
 |  Concrete class for Arrow arrays of string (or utf8) data type.
 |
 |  Method resolution order:
 |      StringArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_buffers(length, value_offsets, data, null_bitmap=None, null_count=-1, offset=0)
 |      StringArray.from_buffers(int length, Buffer value_offsets, Buffer data, Buffer null_bitmap=None, int null_count=-1, int offset=0)
 |
 |      Construct a StringArray from value_offsets and data buffers.
 |      If there are nulls in the data, also a null_bitmap and the matching
 |      null_count must be passed.
 |
 |      Parameters
 |      ----------
 |      length : int
 |      value_offsets : Buffer
 |      data : Buffer
 |      null_bitmap : Buffer, optional
 |      null_count : int, default 0
 |      offset : int, default 0
 |
 |      Returns
 |      -------
 |      string_array : StringArray
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class StringScalar in module pyarrow.lib

class StringScalar(BinaryScalar)
 |  Concrete class for string-like (utf8) scalars.
 |
 |  Method resolution order:
 |      StringScalar
 |      BinaryScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      StringScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python string.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BinaryScalar:
 |
 |  __buffer__(self, flags, /)
 |      Return a buffer object that exposes the underlying memory of the object.
 |
 |  __bytes__(self)
 |      BinaryScalar.__bytes__(self)
 |
 |  as_buffer(self)
 |      BinaryScalar.as_buffer(self)
 |
 |      Return a view over this value as a Buffer object.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class StringViewArray in module pyarrow.lib

class StringViewArray(Array)
 |  Concrete class for Arrow arrays of string (or utf8) view data type.
 |
 |  Method resolution order:
 |      StringViewArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class StringViewScalar in module pyarrow.lib

class StringViewScalar(StringScalar)
 |  Method resolution order:
 |      StringViewScalar
 |      StringScalar
 |      BinaryScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from StringScalar:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      StringScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python string.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BinaryScalar:
 |
 |  __buffer__(self, flags, /)
 |      Return a buffer object that exposes the underlying memory of the object.
 |
 |  __bytes__(self)
 |      BinaryScalar.__bytes__(self)
 |
 |  as_buffer(self)
 |      BinaryScalar.as_buffer(self)
 |
 |      Return a view over this value as a Buffer object.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class StructArray in module pyarrow.lib

class StructArray(Array)
 |  Concrete class for Arrow arrays of a struct data type.
 |
 |  Method resolution order:
 |      StructArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  field(self, index)
 |      StructArray.field(self, index)
 |
 |      Retrieves the child array belonging to field.
 |
 |      Parameters
 |      ----------
 |      index : Union[int, str]
 |          Index / position or name of the field.
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  flatten(self, memory_pool=None)
 |      StructArray.flatten(self, MemoryPool memory_pool=None)
 |
 |      Return one individual array for each field in the struct.
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          For memory allocations, if required, otherwise use default pool.
 |
 |      Returns
 |      -------
 |      result : List[Array]
 |
 |  sort(self, order='ascending', by=None, **kwargs)
 |      StructArray.sort(self, order='ascending', by=None, **kwargs)
 |
 |      Sort the StructArray
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      by : str or None, default None
 |          If to sort the array by one of its fields
 |          or by the whole array.
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : StructArray
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_arrays(arrays, names=None, fields=None, mask=None, memory_pool=None, type=None)
 |      StructArray.from_arrays(arrays, names=None, fields=None, mask=None, memory_pool=None, type=None)
 |
 |      Construct StructArray from collection of arrays representing
 |      each field in the struct.
 |
 |      Either field names, field instances or a struct type must be passed.
 |
 |      Parameters
 |      ----------
 |      arrays : sequence of Array
 |      names : List[str] (optional)
 |          Field names for each struct child.
 |      fields : List[Field] (optional)
 |          Field instances for each struct child.
 |      mask : pyarrow.Array[bool] (optional)
 |          Indicate which values are null (True) or not null (False).
 |      memory_pool : MemoryPool (optional)
 |          For memory allocations, if required, otherwise uses default pool.
 |      type : pyarrow.StructType (optional)
 |          Struct type for name and type of each child.
 |
 |      Returns
 |      -------
 |      result : StructArray
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class StructScalar in module pyarrow.lib

class StructScalar(Scalar, collections.abc.Mapping)
 |  Concrete class for struct scalars.
 |
 |  Method resolution order:
 |      StructScalar
 |      Scalar
 |      _Weakrefable
 |      collections.abc.Mapping
 |      collections.abc.Collection
 |      collections.abc.Sized
 |      collections.abc.Iterable
 |      collections.abc.Container
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __contains__(self, key, /)
 |      Return bool(key in self).
 |
 |  __getitem__(...)
 |      StructScalar.__getitem__(self, key)
 |
 |      Return the child value for the given field.
 |
 |      Parameters
 |      ----------
 |      index : Union[int, str]
 |          Index / position or name of the field.
 |
 |      Returns
 |      -------
 |      result : Scalar
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      StructScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python dict.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  items(self)
 |      StructScalar.items(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from collections.abc.Mapping:
 |
 |  get(self, key, default=None)
 |      D.get(k[,d]) -> D[k] if k in D, else d.  d defaults to None.
 |
 |  keys(self)
 |      D.keys() -> a set-like object providing a view on D's keys
 |
 |  values(self)
 |      D.values() -> an object providing a view on D's values
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from collections.abc.Mapping:
 |
 |  __abstractmethods__ = frozenset({'__getitem__', '__iter__', '__len__'}...
 |
 |  __reversed__ = None
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Collection:
 |
 |  __subclasshook__(C)
 |      Abstract classes can override this to customize issubclass().
 |
 |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
 |      It should return True, False or NotImplemented.  If it returns
 |      NotImplemented, the normal algorithm is used.  Otherwise, it
 |      overrides the normal algorithm (and the outcome is cached).
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from collections.abc.Iterable:
 |
 |  __class_getitem__ = GenericAlias(...)
 |      Represent a PEP 585 generic type
 |
 |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

Python Library Documentation: class StructType in module pyarrow.lib

class StructType(DataType)
 |  Concrete class for struct data types.
 |
 |  ``StructType`` supports direct indexing using ``[...]`` (implemented via
 |  ``__getitem__``) to access its fields.
 |  It will return the struct field with the given index or name.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |
 |  Accessing fields using direct indexing:
 |
 |  >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |  >>> struct_type[0]
 |  pyarrow.Field<x: int32>
 |  >>> struct_type['y']
 |  pyarrow.Field<y: string>
 |
 |  Accessing fields using ``field()``:
 |
 |  >>> struct_type.field(1)
 |  pyarrow.Field<y: string>
 |  >>> struct_type.field('x')
 |  pyarrow.Field<x: int32>
 |
 |  # Creating a schema from the struct type's fields:
 |  >>> pa.schema(list(struct_type))
 |  x: int32
 |  y: string
 |
 |  Method resolution order:
 |      StructType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getitem__(...)
 |      StructType.__getitem__(self, i)
 |
 |      Return the struct field with the given index or name.
 |
 |      Alias of ``field``.
 |
 |  __iter__(...)
 |      StructType.__iter__(self)
 |
 |      Iterate over struct fields, in order.
 |
 |  __len__(...)
 |      StructType.__len__(self)
 |
 |      Like num_fields().
 |
 |  __reduce__(self)
 |      StructType.__reduce__(self)
 |
 |  field(self, i)
 |      StructType.field(self, i) -> Field
 |
 |      Select a field by its column name or numeric index.
 |
 |      Parameters
 |      ----------
 |      i : int or str
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |      Examples
 |      --------
 |
 |      >>> import pyarrow as pa
 |      >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |
 |      Select the second field:
 |
 |      >>> struct_type.field(1)
 |      pyarrow.Field<y: string>
 |
 |      Select the field named 'x':
 |
 |      >>> struct_type.field('x')
 |      pyarrow.Field<x: int32>
 |
 |  get_all_field_indices(self, name)
 |      StructType.get_all_field_indices(self, name)
 |
 |      Return sorted list of indices for the fields with the given name.
 |
 |      Parameters
 |      ----------
 |      name : str
 |          The name of the field to look up.
 |
 |      Returns
 |      -------
 |      indices : List[int]
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct_type.get_all_field_indices('x')
 |      [0]
 |
 |  get_field_index(self, name)
 |      StructType.get_field_index(self, name)
 |
 |      Return index of the unique field with the given name.
 |
 |      Parameters
 |      ----------
 |      name : str
 |          The name of the field to look up.
 |
 |      Returns
 |      -------
 |      index : int
 |          The index of the field with the given name; -1 if the
 |          name isn't found or there are several fields with the given
 |          name.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |
 |      Index of the field with a name 'y':
 |
 |      >>> struct_type.get_field_index('y')
 |      1
 |
 |      Index of the field that does not exist:
 |
 |      >>> struct_type.get_field_index('z')
 |      -1
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  fields
 |      Lists all fields within the StructType.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> struct_type = pa.struct([('a', pa.int64()), ('b', pa.float64()), ('c', pa.string())])
 |      >>> struct_type.fields
 |      [pyarrow.Field<a: int64>, pyarrow.Field<b: double>, pyarrow.Field<c: string>]
 |
 |  names
 |      Lists the field names.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> struct_type = pa.struct([('a', pa.int64()), ('b', pa.float64()), ('c', pa.string())])
 |      >>> struct_type.names
 |      ['a', 'b', 'c']
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class Table in module pyarrow.lib

class Table(_Tabular)
 |  A collection of top-level named, equal length Arrow arrays.
 |
 |  Warnings
 |  --------
 |  Do not call this class's constructor directly, use one of the ``from_*``
 |  methods instead.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |  >>> n_legs = pa.array([2, 4, 5, 100])
 |  >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |  >>> names = ["n_legs", "animals"]
 |
 |  Construct a Table from arrays:
 |
 |  >>> pa.Table.from_arrays([n_legs, animals], names=names)
 |  pyarrow.Table
 |  n_legs: int64
 |  animals: string
 |  ----
 |  n_legs: [[2,4,5,100]]
 |  animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |  Construct a Table from a RecordBatch:
 |
 |  >>> batch = pa.record_batch([n_legs, animals], names=names)
 |  >>> pa.Table.from_batches([batch])
 |  pyarrow.Table
 |  n_legs: int64
 |  animals: string
 |  ----
 |  n_legs: [[2,4,5,100]]
 |  animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |  Construct a Table from pandas DataFrame:
 |
 |  >>> import pandas as pd
 |  >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
 |  ...                    'n_legs': [2, 4, 5, 100],
 |  ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |  >>> pa.Table.from_pandas(df)
 |  pyarrow.Table
 |  year: int64
 |  n_legs: int64
 |  animals: large_string
 |  ----
 |  year: [[2020,2022,2019,2021]]
 |  n_legs: [[2,4,5,100]]
 |  animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |  Construct a Table from a dictionary of arrays:
 |
 |  >>> pydict = {'n_legs': n_legs, 'animals': animals}
 |  >>> pa.Table.from_pydict(pydict)
 |  pyarrow.Table
 |  n_legs: int64
 |  animals: string
 |  ----
 |  n_legs: [[2,4,5,100]]
 |  animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |  >>> pa.Table.from_pydict(pydict).schema
 |  n_legs: int64
 |  animals: string
 |
 |  Construct a Table from a dictionary of arrays with metadata:
 |
 |  >>> my_metadata={"n_legs": "Number of legs per animal"}
 |  >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema
 |  n_legs: int64
 |  animals: string
 |  -- schema metadata --
 |  n_legs: 'Number of legs per animal'
 |
 |  Construct a Table from a list of rows:
 |
 |  >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, {'year': 2021, 'animals': 'Centipede'}]
 |  >>> pa.Table.from_pylist(pylist)
 |  pyarrow.Table
 |  n_legs: int64
 |  animals: string
 |  ----
 |  n_legs: [[2,null]]
 |  animals: [["Flamingo","Centipede"]]
 |
 |  Construct a Table from a list of rows with pyarrow schema:
 |
 |  >>> my_schema = pa.schema([
 |  ...     pa.field('year', pa.int64()),
 |  ...     pa.field('n_legs', pa.int64()),
 |  ...     pa.field('animals', pa.string())],
 |  ...     metadata={"year": "Year of entry"})
 |  >>> pa.Table.from_pylist(pylist, schema=my_schema).schema
 |  year: int64
 |  n_legs: int64
 |  animals: string
 |  -- schema metadata --
 |  year: 'Year of entry'
 |
 |  Construct a Table with :func:`pyarrow.table`:
 |
 |  >>> pa.table([n_legs, animals], names=names)
 |  pyarrow.Table
 |  n_legs: int64
 |  animals: string
 |  ----
 |  n_legs: [[2,4,5,100]]
 |  animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |  Method resolution order:
 |      Table
 |      _Tabular
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_c_stream__(self, requested_schema=None)
 |      Table.__arrow_c_stream__(self, requested_schema=None)
 |
 |      Export the table as an Arrow C stream PyCapsule.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule, default None
 |          The schema to which the stream should be casted, passed as a
 |          PyCapsule containing a C ArrowSchema representation of the
 |          requested schema.
 |          Currently, this is not supported and will raise a
 |          NotImplementedError if the schema doesn't match the current schema.
 |
 |      Returns
 |      -------
 |      PyCapsule
 |
 |  __reduce__(self)
 |      Table.__reduce__(self)
 |
 |  __sizeof__(self)
 |      Table.__sizeof__(self)
 |
 |  add_column(self, i, field_, column)
 |      Table.add_column(self, int i, field_, column)
 |
 |      Add column to Table at position.
 |
 |      A new table is returned with the column added, the original table
 |      object is left unchanged.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          Index to place the column at.
 |      field_ : str or Field
 |          If a string is passed then the type is deduced from the column
 |          data.
 |      column : Array, list of Array, or values coercible to arrays
 |          Column data.
 |
 |      Returns
 |      -------
 |      Table
 |          New table with the passed column added.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |
 |      Add column:
 |
 |      >>> year = [2021, 2022, 2019, 2021]
 |      >>> table.add_column(0,"year", [year])
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2021,2022,2019,2021]]
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |      Original table is left unchanged:
 |
 |      >>> table
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |  cast(self, target_schema, safe=None, options=None)
 |      Table.cast(self, Schema target_schema, safe=None, options=None)
 |
 |      Cast table values to another schema.
 |
 |      Parameters
 |      ----------
 |      target_schema : Schema
 |          Schema to cast to, the names and order of fields must match.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table.schema
 |      n_legs: int64
 |      animals: string
 |
 |      Define new schema and cast table values:
 |
 |      >>> my_schema = pa.schema([
 |      ...     pa.field('n_legs', pa.duration('s')),
 |      ...     pa.field('animals', pa.string())]
 |      ...     )
 |      >>> table.cast(target_schema=my_schema)
 |      pyarrow.Table
 |      n_legs: duration[s]
 |      animals: string
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |  combine_chunks(self, memory_pool=None)
 |      Table.combine_chunks(self, MemoryPool memory_pool=None)
 |
 |      Make a new table by combining the chunks this table has.
 |
 |      All the underlying chunks in the ChunkedArray of each column are
 |      concatenated into zero or one chunk.
 |
 |      To avoid buffer overflow, binary columns may be combined into
 |      multiple chunks. Chunks will have the maximum possible length.
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          For memory allocations, if required, otherwise use default pool.
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]])
 |      >>> names = ["n_legs", "animals"]
 |      >>> table = pa.table([n_legs, animals], names=names)
 |      >>> table
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,2,4],[4,5,100]]
 |      animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]]
 |      >>> table.combine_chunks()
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,2,4,4,5,100]]
 |      animals: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]]
 |
 |  drop(self, columns)
 |      Table.drop(self, columns)
 |
 |      Drop one or more columns and return a new table.
 |
 |      Alias of Table.drop_columns, but kept for backwards compatibility.
 |
 |      Parameters
 |      ----------
 |      columns : str or list[str]
 |          Field name(s) referencing existing column(s).
 |
 |      Returns
 |      -------
 |      Table
 |          New table without the column(s).
 |
 |  equals(self, other, check_metadata=False)
 |      Table.equals(self, Table other, bool check_metadata=False)
 |
 |      Check if contents of two tables are equal.
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Table
 |          Table to compare against.
 |      check_metadata : bool, default False
 |          Whether schema metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> names=["n_legs", "animals"]
 |      >>> table = pa.Table.from_arrays([n_legs, animals], names=names)
 |      >>> table_0 = pa.Table.from_arrays([])
 |      >>> table_1 = pa.Table.from_arrays([n_legs, animals],
 |      ...                                 names=names,
 |      ...                                 metadata={"n_legs": "Number of legs per animal"})
 |      >>> table.equals(table)
 |      True
 |      >>> table.equals(table_0)
 |      False
 |      >>> table.equals(table_1)
 |      True
 |      >>> table.equals(table_1, check_metadata=True)
 |      False
 |
 |  flatten(self, memory_pool=None)
 |      Table.flatten(self, MemoryPool memory_pool=None)
 |
 |      Flatten this Table.
 |
 |      Each column with a struct type is flattened
 |      into one column per struct field.  Other columns are left unchanged.
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          For memory allocations, if required, otherwise use default pool
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
 |      ...                    {'year': 2022, 'n_legs': 4}])
 |      >>> month = pa.array([4, 6])
 |      >>> table = pa.Table.from_arrays([struct,month],
 |      ...                              names = ["a", "month"])
 |      >>> table
 |      pyarrow.Table
 |      a: struct<n_legs: int64, animals: string, year: int64>
 |        child 0, n_legs: int64
 |        child 1, animals: string
 |        child 2, year: int64
 |      month: int64
 |      ----
 |      a: [
 |        -- is_valid: all not null
 |        -- child 0 type: int64
 |      [2,4]
 |        -- child 1 type: string
 |      ["Parrot",null]
 |        -- child 2 type: int64
 |      [null,2022]]
 |      month: [[4,6]]
 |
 |      Flatten the columns with struct field:
 |
 |      >>> table.flatten()
 |      pyarrow.Table
 |      a.n_legs: int64
 |      a.animals: string
 |      a.year: int64
 |      month: int64
 |      ----
 |      a.n_legs: [[2,4]]
 |      a.animals: [["Parrot",null]]
 |      a.year: [[null,2022]]
 |      month: [[4,6]]
 |
 |  get_total_buffer_size(self)
 |      Table.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the table.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
 |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> table.get_total_buffer_size()
 |      76
 |
 |  group_by(self, keys, use_threads=True)
 |      Table.group_by(self, keys, use_threads=True)
 |
 |      Declare a grouping over the columns of the table.
 |
 |      Resulting grouping can then be used to perform aggregations
 |      with a subsequent ``aggregate()`` method.
 |
 |      Parameters
 |      ----------
 |      keys : str or list[str]
 |          Name of the columns that should be used as the grouping key.
 |      use_threads : bool, default True
 |          Whether to use multithreading or not. When set to True (the
 |          default), no stable ordering of the output is guaranteed.
 |
 |      Returns
 |      -------
 |      TableGroupBy
 |
 |      See Also
 |      --------
 |      TableGroupBy.aggregate
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],
 |      ...                   'n_legs': [2, 2, 4, 4, 5, 100],
 |      ...                   'animal': ["Flamingo", "Parrot", "Dog", "Horse",
 |      ...                              "Brittle stars", "Centipede"]})
 |      >>> table.group_by('year').aggregate([('n_legs', 'sum')])
 |      pyarrow.Table
 |      year: int64
 |      n_legs_sum: int64
 |      ----
 |      year: [[2020,2022,2021,2019]]
 |      n_legs_sum: [[2,6,104,5]]
 |
 |  join(self, right_table, keys, right_keys=None, join_type='left outer', left_suffix=None, right_suffix=None, coalesce_keys=True, use_threads=True, filter_expression=None)
 |      Table.join(self, right_table, keys, right_keys=None, join_type='left outer', left_suffix=None, right_suffix=None, coalesce_keys=True, use_threads=True, filter_expression=None)
 |
 |      Perform a join between this table and another one.
 |
 |      Result of the join will be a new Table, where further
 |      operations can be applied.
 |
 |      Parameters
 |      ----------
 |      right_table : Table
 |          The table to join to the current one, acting as the right table
 |          in the join operation.
 |      keys : str or list[str]
 |          The columns from current table that should be used as keys
 |          of the join operation left side.
 |      right_keys : str or list[str], default None
 |          The columns from the right_table that should be used as keys
 |          on the join operation right side.
 |          When ``None`` use the same key names as the left table.
 |      join_type : str, default "left outer"
 |          The kind of join that should be performed, one of
 |          ("left semi", "right semi", "left anti", "right anti",
 |          "inner", "left outer", "right outer", "full outer")
 |      left_suffix : str, default None
 |          Which suffix to add to left column names. This prevents confusion
 |          when the columns in left and right tables have colliding names.
 |      right_suffix : str, default None
 |          Which suffix to add to the right column names. This prevents confusion
 |          when the columns in left and right tables have colliding names.
 |      coalesce_keys : bool, default True
 |          If the duplicated keys should be omitted from one of the sides
 |          in the join result.
 |      use_threads : bool, default True
 |          Whether to use multithreading or not.
 |      filter_expression : pyarrow.compute.Expression
 |          Residual filter which is applied to matching row.
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pyarrow.compute as pc
 |      >>> t1 = pa.table({'id': [1, 2, 3],
 |      ...                'year': [2020, 2022, 2019]})
 |      >>> t2 = pa.table({'id': [3, 4],
 |      ...                'n_legs': [5, 100],
 |      ...                'animal': ["Brittle stars", "Centipede"]})
 |
 |      Left outer join:
 |
 |      >>> t1.join(t2, 'id').combine_chunks().sort_by('year')
 |      pyarrow.Table
 |      id: int64
 |      year: int64
 |      n_legs: int64
 |      animal: string
 |      ----
 |      id: [[3,1,2]]
 |      year: [[2019,2020,2022]]
 |      n_legs: [[5,null,null]]
 |      animal: [["Brittle stars",null,null]]
 |
 |      Full outer join:
 |
 |      >>> t1.join(t2, 'id', join_type="full outer").combine_chunks().sort_by('year')
 |      pyarrow.Table
 |      id: int64
 |      year: int64
 |      n_legs: int64
 |      animal: string
 |      ----
 |      id: [[3,1,2,4]]
 |      year: [[2019,2020,2022,null]]
 |      n_legs: [[5,null,null,100]]
 |      animal: [["Brittle stars",null,null,"Centipede"]]
 |
 |      Right outer join:
 |
 |      >>> t1.join(t2, 'id', join_type="right outer").combine_chunks().sort_by('year')
 |      pyarrow.Table
 |      year: int64
 |      id: int64
 |      n_legs: int64
 |      animal: string
 |      ----
 |      year: [[2019,null]]
 |      id: [[3,4]]
 |      n_legs: [[5,100]]
 |      animal: [["Brittle stars","Centipede"]]
 |
 |      Right anti join:
 |
 |      >>> t1.join(t2, 'id', join_type="right anti")
 |      pyarrow.Table
 |      id: int64
 |      n_legs: int64
 |      animal: string
 |      ----
 |      id: [[4]]
 |      n_legs: [[100]]
 |      animal: [["Centipede"]]
 |
 |      Inner join with intended mismatch filter expression:
 |
 |      >>> t1.join(t2, 'id', join_type="inner", filter_expression=pc.equal(pc.field("n_legs"), 100))
 |      pyarrow.Table
 |      id: int64
 |      year: int64
 |      n_legs: int64
 |      animal: string
 |      ----
 |      id: []
 |      year: []
 |      n_legs: []
 |      animal: []
 |
 |  join_asof(self, right_table, on, by, tolerance, right_on=None, right_by=None)
 |      Table.join_asof(self, right_table, on, by, tolerance, right_on=None, right_by=None)
 |
 |      Perform an asof join between this table and another one.
 |
 |      This is similar to a left-join except that we match on nearest key rather
 |      than equal keys. Both tables must be sorted by the key. This type of join
 |      is most useful for time series data that are not perfectly aligned.
 |
 |      Optionally match on equivalent keys with "by" before searching with "on".
 |
 |      Result of the join will be a new Table, where further
 |      operations can be applied.
 |
 |      Parameters
 |      ----------
 |      right_table : Table
 |          The table to join to the current one, acting as the right table
 |          in the join operation.
 |      on : str
 |          The column from current table that should be used as the "on" key
 |          of the join operation left side.
 |
 |          An inexact match is used on the "on" key, i.e. a row is considered a
 |          match if and only if ``right.on - left.on`` is in the range
 |          ``[min(0, tolerance), max(0, tolerance)]``.
 |
 |          The input dataset must be sorted by the "on" key. Must be a single
 |          field of a common type.
 |
 |          Currently, the "on" key must be an integer, date, or timestamp type.
 |      by : str or list[str]
 |          The columns from current table that should be used as the keys
 |          of the join operation left side. The join operation is then done
 |          only for the matches in these columns.
 |      tolerance : int
 |          The tolerance for inexact "on" key matching. A right row is considered
 |          a match with a left row if ``right.on - left.on`` is in the range
 |          ``[min(0, tolerance), max(0, tolerance)]``. ``tolerance`` may be:
 |
 |          - negative, in which case a past-as-of-join occurs
 |            (match iff ``tolerance <= right.on - left.on <= 0``);
 |          - or positive, in which case a future-as-of-join occurs
 |            (match iff ``0 <= right.on - left.on <= tolerance``);
 |          - or zero, in which case an exact-as-of-join occurs
 |            (match iff ``right.on == left.on``).
 |
 |          The tolerance is interpreted in the same units as the "on" key.
 |      right_on : str or list[str], default None
 |          The columns from the right_table that should be used as the on key
 |          on the join operation right side.
 |          When ``None`` use the same key name as the left table.
 |      right_by : str or list[str], default None
 |          The columns from the right_table that should be used as keys
 |          on the join operation right side.
 |          When ``None`` use the same key names as the left table.
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t1 = pa.table({'id': [1, 3, 2, 3, 3],
 |      ...                'year': [2020, 2021, 2022, 2022, 2023]})
 |      >>> t2 = pa.table({'id': [3, 4],
 |      ...                'year': [2020, 2021],
 |      ...                'n_legs': [5, 100],
 |      ...                'animal': ["Brittle stars", "Centipede"]})
 |
 |      >>> t1.join_asof(t2, on='year', by='id', tolerance=-2)
 |      pyarrow.Table
 |      id: int64
 |      year: int64
 |      n_legs: int64
 |      animal: string
 |      ----
 |      id: [[1,3,2,3,3]]
 |      year: [[2020,2021,2022,2022,2023]]
 |      n_legs: [[null,5,null,5,null]]
 |      animal: [[null,"Brittle stars",null,"Brittle stars",null]]
 |
 |  remove_column(self, i)
 |      Table.remove_column(self, int i)
 |
 |      Create new Table with the indicated column removed.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          Index of column to remove.
 |
 |      Returns
 |      -------
 |      Table
 |          New table without the column.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table.remove_column(1)
 |      pyarrow.Table
 |      n_legs: int64
 |      ----
 |      n_legs: [[2,4,5,100]]
 |
 |  rename_columns(self, names)
 |      Table.rename_columns(self, names)
 |
 |      Create new table with columns renamed to provided names.
 |
 |      Parameters
 |      ----------
 |      names : list[str] or dict[str, str]
 |          List of new column names or mapping of old column names to new column names.
 |
 |          If a mapping of old to new column names is passed, then all columns which are
 |          found to match a provided old column name will be renamed to the new column name.
 |          If any column names are not found in the mapping, a KeyError will be raised.
 |
 |      Raises
 |      ------
 |      KeyError
 |          If any of the column names passed in the names mapping do not exist.
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> new_names = ["n", "name"]
 |      >>> table.rename_columns(new_names)
 |      pyarrow.Table
 |      n: int64
 |      name: string
 |      ----
 |      n: [[2,4,5,100]]
 |      name: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |      >>> new_names = {"n_legs": "n", "animals": "name"}
 |      >>> table.rename_columns(new_names)
 |      pyarrow.Table
 |      n: int64
 |      name: string
 |      ----
 |      n: [[2,4,5,100]]
 |      name: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |  replace_schema_metadata(self, metadata=None)
 |      Table.replace_schema_metadata(self, metadata=None)
 |
 |      Create shallow copy of table by replacing schema
 |      key-value metadata with the indicated new metadata (which may be None),
 |      which deletes any existing metadata.
 |
 |      Parameters
 |      ----------
 |      metadata : dict, default None
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
 |      ...                    'n_legs': [2, 4, 5, 100],
 |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table = pa.Table.from_pandas(df)
 |
 |      Constructing a Table with pyarrow schema and metadata:
 |
 |      >>> my_schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |      >>> table= pa.table(df, my_schema)
 |      >>> table.schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |      pandas: ...
 |
 |      Create a shallow copy of a Table with deleted schema metadata:
 |
 |      >>> table.replace_schema_metadata().schema
 |      n_legs: int64
 |      animals: string
 |
 |      Create a shallow copy of a Table with new schema metadata:
 |
 |      >>> metadata={"animals": "Which animal"}
 |      >>> table.replace_schema_metadata(metadata = metadata).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      animals: 'Which animal'
 |
 |  select(self, columns)
 |      Table.select(self, columns)
 |
 |      Select columns of the Table.
 |
 |      Returns a new Table with the specified columns, and metadata
 |      preserved.
 |
 |      Parameters
 |      ----------
 |      columns : list-like
 |          The column names or integer indices to select.
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
 |      ...                   'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table.select([0,1])
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      ----
 |      year: [[2020,2022,2019,2021]]
 |      n_legs: [[2,4,5,100]]
 |      >>> table.select(["year"])
 |      pyarrow.Table
 |      year: int64
 |      ----
 |      year: [[2020,2022,2019,2021]]
 |
 |  set_column(self, i, field_, column)
 |      Table.set_column(self, int i, field_, column)
 |
 |      Replace column in Table at position.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          Index to place the column at.
 |      field_ : str or Field
 |          If a string is passed then the type is deduced from the column
 |          data.
 |      column : Array, list of Array, or values coercible to arrays
 |          Column data.
 |
 |      Returns
 |      -------
 |      Table
 |          New table with the passed column set.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |
 |      Replace a column:
 |
 |      >>> year = [2021, 2022, 2019, 2021]
 |      >>> table.set_column(1,'year', [year])
 |      pyarrow.Table
 |      n_legs: int64
 |      year: int64
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      year: [[2021,2022,2019,2021]]
 |
 |  slice(self, offset=0, length=None)
 |      Table.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this Table.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of table to slice.
 |      length : int, default None
 |          Length of slice (default is until end of table starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
 |      ...                   'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table.slice(length=3)
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2020,2022,2019]]
 |      n_legs: [[2,4,5]]
 |      animals: [["Flamingo","Horse","Brittle stars"]]
 |      >>> table.slice(offset=2)
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2019,2021]]
 |      n_legs: [[5,100]]
 |      animals: [["Brittle stars","Centipede"]]
 |      >>> table.slice(offset=2, length=1)
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2019]]
 |      n_legs: [[5]]
 |      animals: [["Brittle stars"]]
 |
 |  to_batches(self, max_chunksize=None)
 |      Table.to_batches(self, max_chunksize=None)
 |
 |      Convert Table to a list of RecordBatch objects.
 |
 |      Note that this method is zero-copy, it merely exposes the same data
 |      under a different API.
 |
 |      Parameters
 |      ----------
 |      max_chunksize : int, default None
 |          Maximum number of rows for each RecordBatch chunk. Individual chunks
 |          may be smaller depending on the chunk layout of individual columns.
 |
 |      Returns
 |      -------
 |      list[RecordBatch]
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
 |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table = pa.Table.from_pandas(df)
 |
 |      Convert a Table to a RecordBatch:
 |
 |      >>> table.to_batches()[0].to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |
 |      Convert a Table to a list of RecordBatches:
 |
 |      >>> table.to_batches(max_chunksize=2)[0].to_pandas()
 |         n_legs   animals
 |      0       2  Flamingo
 |      1       4     Horse
 |      >>> table.to_batches(max_chunksize=2)[1].to_pandas()
 |         n_legs        animals
 |      0       5  Brittle stars
 |      1     100      Centipede
 |
 |  to_reader(self, max_chunksize=None)
 |      Table.to_reader(self, max_chunksize=None)
 |
 |      Convert the Table to a RecordBatchReader.
 |
 |      Note that this method is zero-copy, it merely exposes the same data
 |      under a different API.
 |
 |      Parameters
 |      ----------
 |      max_chunksize : int, default None
 |          Maximum number of rows for each RecordBatch chunk. Individual chunks
 |          may be smaller depending on the chunk layout of individual columns.
 |
 |      Returns
 |      -------
 |      RecordBatchReader
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |
 |      Convert a Table to a RecordBatchReader:
 |
 |      >>> table.to_reader()
 |      <pyarrow.lib.RecordBatchReader object at ...>
 |
 |      >>> reader = table.to_reader()
 |      >>> reader.schema
 |      n_legs: int64
 |      animals: string
 |      >>> reader.read_all()
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |  to_struct_array(self, max_chunksize=None)
 |      Table.to_struct_array(self, max_chunksize=None)
 |
 |      Convert to a chunked array of struct type.
 |
 |      Parameters
 |      ----------
 |      max_chunksize : int, default None
 |          Maximum number of rows for ChunkedArray chunks. Individual chunks
 |          may be smaller depending on the chunk layout of individual columns.
 |
 |      Returns
 |      -------
 |      ChunkedArray
 |
 |  unify_dictionaries(self, memory_pool=None)
 |      Table.unify_dictionaries(self, MemoryPool memory_pool=None)
 |
 |      Unify dictionaries across all chunks.
 |
 |      This method returns an equivalent table, but where all chunks of
 |      each column share the same dictionary values.  Dictionary indices
 |      are transposed accordingly.
 |
 |      Columns without dictionaries are returned unchanged.
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          For memory allocations, if required, otherwise use default pool
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode()
 |      >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode()
 |      >>> c_arr = pa.chunked_array([arr_1, arr_2])
 |      >>> table = pa.table([c_arr], names=["animals"])
 |      >>> table
 |      pyarrow.Table
 |      animals: dictionary<values=string, indices=int32, ordered=0>
 |      ----
 |      animals: [  -- dictionary:
 |      ["Flamingo","Parrot","Dog"]  -- indices:
 |      [0,1,2],  -- dictionary:
 |      ["Horse","Brittle stars","Centipede"]  -- indices:
 |      [0,1,2]]
 |
 |      Unify dictionaries across both chunks:
 |
 |      >>> table.unify_dictionaries()
 |      pyarrow.Table
 |      animals: dictionary<values=string, indices=int32, ordered=0>
 |      ----
 |      animals: [  -- dictionary:
 |      ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]  -- indices:
 |      [0,1,2],  -- dictionary:
 |      ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]  -- indices:
 |      [3,4,5]]
 |
 |  validate(self, *, full=False)
 |      Table.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  from_pandas(df, schema=None, preserve_index=None, nthreads=None, columns=None, safe=True)
 |      Table.from_pandas(cls, df, Schema schema=None, preserve_index=None, nthreads=None, columns=None, bool safe=True)
 |
 |      Convert pandas.DataFrame to an Arrow Table.
 |
 |      The column types in the resulting Arrow Table are inferred from the
 |      dtypes of the pandas.Series in the DataFrame. In the case of non-object
 |      Series, the NumPy dtype is translated to its Arrow equivalent. In the
 |      case of `object`, we need to guess the datatype by looking at the
 |      Python objects in this Series.
 |
 |      Be aware that Series of the `object` dtype don't carry enough
 |      information to always lead to a meaningful Arrow type. In the case that
 |      we cannot infer a type, e.g. because the DataFrame is of length 0 or
 |      the Series only contains None/nan objects, the type is set to
 |      null. This behavior can be avoided by constructing an explicit schema
 |      and passing it to this function.
 |
 |      Parameters
 |      ----------
 |      df : pandas.DataFrame
 |      schema : pyarrow.Schema, optional
 |          The expected schema of the Arrow Table. This can be used to
 |          indicate the type of columns if we cannot infer it automatically.
 |          If passed, the output will have exactly this schema. Columns
 |          specified in the schema that are not found in the DataFrame columns
 |          or its index will raise an error. Additional columns or index
 |          levels in the DataFrame which are not specified in the schema will
 |          be ignored.
 |      preserve_index : bool, optional
 |          Whether to store the index as an additional column in the resulting
 |          ``Table``. The default of None will store the index as a column,
 |          except for RangeIndex which is stored as metadata only. Use
 |          ``preserve_index=True`` to force it to be stored as a column.
 |      nthreads : int, default None
 |          If greater than 1, convert columns to Arrow in parallel using
 |          indicated number of threads. By default, this follows
 |          :func:`pyarrow.cpu_count` (may use up to system CPU count threads).
 |      columns : list, optional
 |         List of column to be converted. If None, use all columns.
 |      safe : bool, default True
 |         Check for overflows or other unsafe conversions.
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
 |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> pa.Table.from_pandas(df)
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: large_string
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_arrays(arrays, names=None, schema=None, metadata=None)
 |      Table.from_arrays(arrays, names=None, schema=None, metadata=None)
 |
 |      Construct a Table from Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      arrays : list of pyarrow.Array or pyarrow.ChunkedArray
 |          Equal-length arrays that should form the table.
 |      names : list of str, optional
 |          Names for the table columns. If not passed, schema must be passed.
 |      schema : Schema, default None
 |          Schema for the created table. If not passed, names must be passed.
 |      metadata : dict or Mapping, default None
 |          Optional metadata for the schema (if inferred).
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> names = ["n_legs", "animals"]
 |
 |      Construct a Table from arrays:
 |
 |      >>> pa.Table.from_arrays([n_legs, animals], names=names)
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |      Construct a Table from arrays with metadata:
 |
 |      >>> my_metadata={"n_legs": "Number of legs per animal"}
 |      >>> pa.Table.from_arrays([n_legs, animals],
 |      ...                       names=names,
 |      ...                       metadata=my_metadata)
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |      >>> pa.Table.from_arrays([n_legs, animals],
 |      ...                       names=names,
 |      ...                       metadata=my_metadata).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |      Construct a Table from arrays with pyarrow schema:
 |
 |      >>> my_schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())],
 |      ...     metadata={"animals": "Name of the animal species"})
 |      >>> pa.Table.from_arrays([n_legs, animals],
 |      ...                       schema=my_schema)
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |      >>> pa.Table.from_arrays([n_legs, animals],
 |      ...                       schema=my_schema).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      animals: 'Name of the animal species'
 |
 |  from_batches(batches, schema=None)
 |      Table.from_batches(batches, Schema schema=None)
 |
 |      Construct a Table from a sequence or iterator of Arrow RecordBatches.
 |
 |      Parameters
 |      ----------
 |      batches : sequence or iterator of RecordBatch
 |          Sequence of RecordBatch to be converted, all schemas must be equal.
 |      schema : Schema, default None
 |          If not passed, will be inferred from the first RecordBatch.
 |
 |      Returns
 |      -------
 |      Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> names = ["n_legs", "animals"]
 |      >>> batch = pa.record_batch([n_legs, animals], names=names)
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |
 |      Construct a Table from a RecordBatch:
 |
 |      >>> pa.Table.from_batches([batch])
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |      Construct a Table from a sequence of RecordBatches:
 |
 |      >>> pa.Table.from_batches([batch, batch])
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,4,5,100],[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Flamingo","Horse","Brittle stars","Centipede"]]
 |
 |  from_struct_array(struct_array)
 |      Table.from_struct_array(struct_array)
 |
 |      Construct a Table from a StructArray.
 |
 |      Each field in the StructArray will become a column in the resulting
 |      ``Table``.
 |
 |      Parameters
 |      ----------
 |      struct_array : StructArray or ChunkedArray
 |          Array to construct the table from.
 |
 |      Returns
 |      -------
 |      pyarrow.Table
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
 |      ...                    {'year': 2022, 'n_legs': 4, 'animals': 'Goat'}])
 |      >>> pa.Table.from_struct_array(struct).to_pandas()
 |         n_legs animals    year
 |      0       2  Parrot     NaN
 |      1       4    Goat  2022.0
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  is_cpu
 |      Whether all ChunkedArrays are CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the table.
 |
 |      In other words, the sum of bytes from all buffer ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will only be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
 |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> table.nbytes
 |      72
 |
 |  num_columns
 |      Number of columns in this table.
 |
 |      Returns
 |      -------
 |      int
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
 |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> table = pa.Table.from_pandas(df)
 |      >>> table.num_columns
 |      2
 |
 |  num_rows
 |      Number of rows in this table.
 |
 |      Due to the definition of a table, all columns have the same number of
 |      rows.
 |
 |      Returns
 |      -------
 |      int
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
 |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> table = pa.Table.from_pandas(df)
 |      >>> table.num_rows
 |      4
 |
 |  schema
 |      Schema of the table and its columns.
 |
 |      Returns
 |      -------
 |      Schema
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table.schema
 |      n_legs: int64
 |      animals: string
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _Tabular:
 |
 |  __array__(self, dtype=None, copy=None)
 |      _Tabular.__array__(self, dtype=None, copy=None)
 |
 |  __dataframe__(self, nan_as_null: 'bool' = False, allow_copy: 'bool' = True)
 |      _Tabular.__dataframe__(self, bool nan_as_null: bool = False, bool allow_copy: bool = True)
 |
 |      Return the dataframe interchange object implementing the interchange protocol.
 |
 |      Parameters
 |      ----------
 |      nan_as_null : bool, default False
 |          Whether to tell the DataFrame to overwrite null values in the data
 |          with ``NaN`` (or ``NaT``).
 |      allow_copy : bool, default True
 |          Whether to allow memory copying when exporting. If set to False
 |          it would cause non-zero-copy exports to fail.
 |
 |      Returns
 |      -------
 |      DataFrame interchange object
 |          The object which consuming library can use to ingress the dataframe.
 |
 |      Notes
 |      -----
 |      Details on the interchange protocol:
 |      https://data-apis.org/dataframe-protocol/latest/index.html
 |      `nan_as_null` currently has no effect; once support for nullable extension
 |      dtypes is added, this value should be propagated to columns.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      _Tabular.__getitem__(self, key)
 |
 |      Slice or return column at given index or column name
 |
 |      Parameters
 |      ----------
 |      key : integer, str, or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      Array (from RecordBatch) or ChunkedArray (from Table) for column input.
 |      RecordBatch or Table for slice input.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce_cython__(self)
 |      _Tabular.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _Tabular.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _Tabular.__setstate_cython__(self, __pyx_state)
 |
 |  append_column(self, field_, column)
 |      _Tabular.append_column(self, field_, column)
 |
 |      Append column at end of columns.
 |
 |      Parameters
 |      ----------
 |      field_ : str or Field
 |          If a string is passed then the type is deduced from the column
 |          data.
 |      column : Array or value coercible to array
 |          Column data.
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |          New table or record batch with the passed column added.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |
 |      Append column at the end:
 |
 |      >>> year = [2021, 2022, 2019, 2021]
 |      >>> table.append_column('year', [year])
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      year: int64
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |      year: [[2021,2022,2019,2021]]
 |
 |  column(self, i)
 |      _Tabular.column(self, i)
 |
 |      Select single column from Table or RecordBatch.
 |
 |      Parameters
 |      ----------
 |      i : int or string
 |          The index or name of the column to retrieve.
 |
 |      Returns
 |      -------
 |      column : Array (for RecordBatch) or ChunkedArray (for Table)
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
 |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table = pa.Table.from_pandas(df)
 |
 |      Select a column by numeric index:
 |
 |      >>> table.column(0)
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          2,
 |          4,
 |          5,
 |          100
 |        ]
 |      ]
 |
 |      Select a column by its name:
 |
 |      >>> table.column("animals")
 |      <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          "Flamingo",
 |          "Horse",
 |          "Brittle stars",
 |          "Centipede"
 |        ]
 |      ]
 |
 |  drop_columns(self, columns)
 |      _Tabular.drop_columns(self, columns)
 |
 |      Drop one or more columns and return a new Table or RecordBatch.
 |
 |      Parameters
 |      ----------
 |      columns : str or list[str]
 |          Field name(s) referencing existing column(s).
 |
 |      Raises
 |      ------
 |      KeyError
 |          If any of the passed column names do not exist.
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |          A tabular object without the column(s).
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
 |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table = pa.Table.from_pandas(df)
 |
 |      Drop one column:
 |
 |      >>> table.drop_columns("animals")
 |      pyarrow.Table
 |      n_legs: int64
 |      ----
 |      n_legs: [[2,4,5,100]]
 |
 |      Drop one or more columns:
 |
 |      >>> table.drop_columns(["n_legs", "animals"])
 |      pyarrow.Table
 |      ...
 |      ----
 |
 |  drop_null(self)
 |      _Tabular.drop_null(self)
 |
 |      Remove rows that contain missing values from a Table or RecordBatch.
 |
 |      See :func:`pyarrow.compute.drop_null` for full usage.
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |          A tabular object with the same schema, with rows containing
 |          no missing values.
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'year': [None, 2022, 2019, 2021],
 |      ...                   'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> table.drop_null()
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2022,2021]]
 |      n_legs: [[4,100]]
 |      animals: [["Horse","Centipede"]]
 |
 |  field(self, i)
 |      _Tabular.field(self, i)
 |
 |      Select a schema field by its column name or numeric index.
 |
 |      Parameters
 |      ----------
 |      i : int or string
 |          The index or name of the field to retrieve.
 |
 |      Returns
 |      -------
 |      Field
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table.field(0)
 |      pyarrow.Field<n_legs: int64>
 |      >>> table.field(1)
 |      pyarrow.Field<animals: string>
 |
 |  filter(self, mask, null_selection_behavior='drop')
 |      _Tabular.filter(self, mask, null_selection_behavior='drop')
 |
 |      Select rows from the table or record batch based on a boolean mask.
 |
 |      The Table can be filtered based on a mask, which will be passed to
 |      :func:`pyarrow.compute.filter` to perform the filtering, or it can
 |      be filtered through a boolean :class:`.Expression`
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like or .Expression
 |          The boolean mask or the :class:`.Expression` to filter the table with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled, does nothing if
 |          an :class:`.Expression` is used.
 |
 |      Returns
 |      -------
 |      filtered : Table or RecordBatch
 |          A tabular object of the same schema, with only the rows selected
 |          by applied filtering
 |
 |      Examples
 |      --------
 |      Using a Table (works similarly for RecordBatch):
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
 |      ...                   'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |
 |      Define an expression and select rows:
 |
 |      >>> import pyarrow.compute as pc
 |      >>> expr = pc.field("year") <= 2020
 |      >>> table.filter(expr)
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2020,2019]]
 |      n_legs: [[2,5]]
 |      animals: [["Flamingo","Brittle stars"]]
 |
 |      Define a mask and select rows:
 |
 |      >>> mask=[True, True, False, None]
 |      >>> table.filter(mask)
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2020,2022]]
 |      n_legs: [[2,4]]
 |      animals: [["Flamingo","Horse"]]
 |      >>> table.filter(mask, null_selection_behavior='emit_null')
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2020,2022,null]]
 |      n_legs: [[2,4,null]]
 |      animals: [["Flamingo","Horse",null]]
 |
 |  itercolumns(self)
 |      _Tabular.itercolumns(self)
 |
 |      Iterator over all columns in their numerical order.
 |
 |      Yields
 |      ------
 |      Array (for RecordBatch) or ChunkedArray (for Table)
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
 |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> for i in table.itercolumns():
 |      ...     print(i.null_count)
 |      ...
 |      2
 |      1
 |
 |  sort_by(self, sorting, **kwargs)
 |      _Tabular.sort_by(self, sorting, **kwargs)
 |
 |      Sort the Table or RecordBatch by one or multiple columns.
 |
 |      Parameters
 |      ----------
 |      sorting : str or list[tuple(name, order)]
 |          Name of the column to use to sort (ascending), or
 |          a list of multiple sorting conditions where
 |          each entry is a tuple with column name
 |          and sorting order ("ascending" or "descending")
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |          A new tabular object sorted according to the sort keys.
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],
 |      ...                   'n_legs': [2, 2, 4, 4, 5, 100],
 |      ...                   'animal': ["Flamingo", "Parrot", "Dog", "Horse",
 |      ...                   "Brittle stars", "Centipede"]})
 |      >>> table.sort_by('animal')
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animal: string
 |      ----
 |      year: [[2019,2021,2021,2020,2022,2022]]
 |      n_legs: [[5,100,4,2,4,2]]
 |      animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]]
 |
 |  take(self, indices)
 |      _Tabular.take(self, indices)
 |
 |      Select rows from a Table or RecordBatch.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the tabular object whose rows will be returned.
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |          A tabular object with the same schema, containing the taken rows.
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
 |      ...                   'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table.take([1,3])
 |      pyarrow.Table
 |      year: int64
 |      n_legs: int64
 |      animals: string
 |      ----
 |      year: [[2022,2021]]
 |      n_legs: [[4,100]]
 |      animals: [["Horse","Centipede"]]
 |
 |  to_pydict(self, *, maps_as_pydicts=None)
 |      _Tabular.to_pydict(self, *, maps_as_pydicts=None)
 |
 |      Convert the Table or RecordBatch to a dict or OrderedDict.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      dict
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
 |      >>> table = pa.Table.from_arrays([n_legs, animals], names=["n_legs", "animals"])
 |      >>> table.to_pydict()
 |      {'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']}
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      _Tabular.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert the Table or RecordBatch to a list of rows / dictionaries.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      list
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> data = [[2, 4, 5, 100],
 |      ...         ["Flamingo", "Horse", "Brittle stars", "Centipede"]]
 |      >>> table = pa.table(data, names=["n_legs", "animals"])
 |      >>> table.to_pylist()
 |      [{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ...
 |
 |  to_string(self, *, show_metadata=False, preview_cols=0)
 |      _Tabular.to_string(self, *, show_metadata=False, preview_cols=0)
 |
 |      Return human-readable string representation of Table or RecordBatch.
 |
 |      Parameters
 |      ----------
 |      show_metadata : bool, default False
 |          Display Field-level and Schema-level KeyValueMetadata.
 |      preview_cols : int, default 0
 |          Display values of the columns for the first N columns.
 |
 |      Returns
 |      -------
 |      str
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from _Tabular:
 |
 |  from_pydict(mapping, schema=None, metadata=None)
 |      _Tabular.from_pydict(cls, mapping, schema=None, metadata=None)
 |
 |      Construct a Table or RecordBatch from Arrow arrays or columns.
 |
 |      Parameters
 |      ----------
 |      mapping : dict or Mapping
 |          A mapping of strings to Arrays or Python lists.
 |      schema : Schema, default None
 |          If not passed, will be inferred from the Mapping values.
 |      metadata : dict or Mapping, default None
 |          Optional metadata for the schema (if inferred).
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> pydict = {'n_legs': n_legs, 'animals': animals}
 |
 |      Construct a Table from a dictionary of arrays:
 |
 |      >>> pa.Table.from_pydict(pydict)
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,4,5,100]]
 |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
 |      >>> pa.Table.from_pydict(pydict).schema
 |      n_legs: int64
 |      animals: string
 |
 |      Construct a Table from a dictionary of arrays with metadata:
 |
 |      >>> my_metadata={"n_legs": "Number of legs per animal"}
 |      >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |      Construct a Table from a dictionary of arrays with pyarrow schema:
 |
 |      >>> my_schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |      >>> pa.Table.from_pydict(pydict, schema=my_schema).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |  from_pylist(mapping, schema=None, metadata=None)
 |      _Tabular.from_pylist(cls, mapping, schema=None, metadata=None)
 |
 |      Construct a Table or RecordBatch from list of rows / dictionaries.
 |
 |      Parameters
 |      ----------
 |      mapping : list of dicts of rows
 |          A mapping of strings to row values.
 |      schema : Schema, default None
 |          If not passed, will be inferred from the first row of the
 |          mapping values.
 |      metadata : dict or Mapping, default None
 |          Optional metadata for the schema (if inferred).
 |
 |      Returns
 |      -------
 |      Table or RecordBatch
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'},
 |      ...           {'n_legs': 4, 'animals': 'Dog'}]
 |
 |      Construct a Table from a list of rows:
 |
 |      >>> pa.Table.from_pylist(pylist)
 |      pyarrow.Table
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [[2,4]]
 |      animals: [["Flamingo","Dog"]]
 |
 |      Construct a Table from a list of rows with metadata:
 |
 |      >>> my_metadata={"n_legs": "Number of legs per animal"}
 |      >>> pa.Table.from_pylist(pylist, metadata=my_metadata).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |      Construct a Table from a list of rows with pyarrow schema:
 |
 |      >>> my_schema = pa.schema([
 |      ...     pa.field('n_legs', pa.int64()),
 |      ...     pa.field('animals', pa.string())],
 |      ...     metadata={"n_legs": "Number of legs per animal"})
 |      >>> pa.Table.from_pylist(pylist, schema=my_schema).schema
 |      n_legs: int64
 |      animals: string
 |      -- schema metadata --
 |      n_legs: 'Number of legs per animal'
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from _Tabular:
 |
 |  column_names
 |      Names of the Table or RecordBatch columns.
 |
 |      Returns
 |      -------
 |      list of str
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
 |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
 |      >>> table.column_names
 |      ['n_legs', 'animals']
 |
 |  columns
 |      List of all columns in numerical order.
 |
 |      Returns
 |      -------
 |      columns : list of Array (for RecordBatch) or list of ChunkedArray (for Table)
 |
 |      Examples
 |      --------
 |      Table (works similarly for RecordBatch)
 |
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |      >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
 |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> table = pa.Table.from_pandas(df)
 |      >>> table.columns
 |      [<pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          null,
 |          4,
 |          5,
 |          null
 |        ]
 |      ], <pyarrow.lib.ChunkedArray object at ...>
 |      [
 |        [
 |          "Flamingo",
 |          "Horse",
 |          null,
 |          "Centipede"
 |        ]
 |      ]]
 |
 |  shape
 |      Dimensions of the table or record batch: (#rows, #columns).
 |
 |      Returns
 |      -------
 |      (int, int)
 |          Number of rows and number of columns.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
 |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
 |      >>> table.shape
 |      (4, 2)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from _Tabular:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class TableGroupBy in module pyarrow.lib

class TableGroupBy(builtins.object)
 |  TableGroupBy(table, keys, use_threads=True)
 |
 |  A grouping of columns in a table on which to perform aggregations.
 |
 |  Parameters
 |  ----------
 |  table : pyarrow.Table
 |      Input table to execute the aggregation on.
 |  keys : str or list[str]
 |      Name of the grouped columns.
 |  use_threads : bool, default True
 |      Whether to use multithreading or not. When set to True (the default),
 |      no stable ordering of the output is guaranteed.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |  >>> t = pa.table([
 |  ...       pa.array(["a", "a", "b", "b", "c"]),
 |  ...       pa.array([1, 2, 3, 4, 5]),
 |  ... ], names=["keys", "values"])
 |
 |  Grouping of columns:
 |
 |  >>> pa.TableGroupBy(t,"keys")
 |  <pyarrow.lib.TableGroupBy object at ...>
 |
 |  Perform aggregations:
 |
 |  >>> pa.TableGroupBy(t,"keys").aggregate([("values", "sum")])
 |  pyarrow.Table
 |  keys: string
 |  values_sum: int64
 |  ----
 |  keys: [["a","b","c"]]
 |  values_sum: [[3,7,5]]
 |
 |  Methods defined here:
 |
 |  __init__(self, table, keys, use_threads=True)
 |      TableGroupBy.__init__(self, table, keys, use_threads=True)
 |
 |  aggregate(self, aggregations)
 |      TableGroupBy.aggregate(self, aggregations)
 |
 |      Perform an aggregation over the grouped columns of the table.
 |
 |      Parameters
 |      ----------
 |      aggregations : list[tuple(str, str)] or list[tuple(str, str, FunctionOptions)]
 |          List of tuples, where each tuple is one aggregation specification
 |          and consists of: aggregation column name followed
 |          by function name and optionally aggregation function option.
 |          Pass empty list to get a single row for each group.
 |          The column name can be a string, an empty list or a list of
 |          column names, for unary, nullary and n-ary aggregation functions
 |          respectively.
 |
 |          For the list of function names and respective aggregation
 |          function options see :ref:`py-grouped-aggrs`.
 |
 |      Returns
 |      -------
 |      Table
 |          Results of the aggregation functions.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.table([
 |      ...       pa.array(["a", "a", "b", "b", "c"]),
 |      ...       pa.array([1, 2, 3, 4, 5]),
 |      ... ], names=["keys", "values"])
 |
 |      Sum the column "values" over the grouped column "keys":
 |
 |      >>> t.group_by("keys").aggregate([("values", "sum")])
 |      pyarrow.Table
 |      keys: string
 |      values_sum: int64
 |      ----
 |      keys: [["a","b","c"]]
 |      values_sum: [[3,7,5]]
 |
 |      Count the rows over the grouped column "keys":
 |
 |      >>> t.group_by("keys").aggregate([([], "count_all")])
 |      pyarrow.Table
 |      keys: string
 |      count_all: int64
 |      ----
 |      keys: [["a","b","c"]]
 |      count_all: [[2,2,1]]
 |
 |      Do multiple aggregations:
 |
 |      >>> t.group_by("keys").aggregate([
 |      ...    ("values", "sum"),
 |      ...    ("keys", "count")
 |      ... ])
 |      pyarrow.Table
 |      keys: string
 |      values_sum: int64
 |      keys_count: int64
 |      ----
 |      keys: [["a","b","c"]]
 |      values_sum: [[3,7,5]]
 |      keys_count: [[2,2,1]]
 |
 |      Count the number of non-null values for column "values"
 |      over the grouped column "keys":
 |
 |      >>> import pyarrow.compute as pc
 |      >>> t.group_by(["keys"]).aggregate([
 |      ...    ("values", "count", pc.CountOptions(mode="only_valid"))
 |      ... ])
 |      pyarrow.Table
 |      keys: string
 |      values_count: int64
 |      ----
 |      keys: [["a","b","c"]]
 |      values_count: [[2,2,1]]
 |
 |      Get a single row for each group in column "keys":
 |
 |      >>> t.group_by("keys").aggregate([])
 |      pyarrow.Table
 |      keys: string
 |      ----
 |      keys: [["a","b","c"]]
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  __dict__
 |      dictionary for instance variables
 |
 |  __weakref__
 |      list of weak references to the object

Python Library Documentation: class Tensor in module pyarrow.lib

class Tensor(_Weakrefable)
 |  Tensor()
 |
 |  A n-dimensional array a.k.a Tensor.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |  >>> import numpy as np
 |  >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |  >>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |  <pyarrow.Tensor>
 |  type: int32
 |  shape: (2, 3)
 |  strides: (12, 4)
 |
 |  Method resolution order:
 |      Tensor
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __buffer__(self, flags, /)
 |      Return a buffer object that exposes the underlying memory of the object.
 |
 |  __dlpack__(self, stream=None)
 |      Tensor.__dlpack__(self, stream=None)
 |
 |      Export a Tensor as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the tensor, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Tensor.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this tensor resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      Tensor.__reduce_cython__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __setstate__ = __setstate_cython__(...)
 |      Tensor.__setstate_cython__(self, __pyx_state)
 |
 |  dim_name(self, i)
 |      Tensor.dim_name(self, i)
 |
 |      Returns the name of the i-th tensor dimension.
 |
 |      Parameters
 |      ----------
 |      i : int
 |          The physical index of the tensor dimension.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |      >>> tensor.dim_name(0)
 |      'dim1'
 |      >>> tensor.dim_name(1)
 |      'dim2'
 |
 |  equals(self, other)
 |      Tensor.equals(self, Tensor other)
 |
 |      Return true if the tensors contains exactly equal data.
 |
 |      Parameters
 |      ----------
 |      other : Tensor
 |          The other tensor to compare for equality.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |      >>> y = np.array([[2, 2, 4], [4, 5, 10]], np.int32)
 |      >>> tensor2 = pa.Tensor.from_numpy(y, dim_names=["a","b"])
 |      >>> tensor.equals(tensor)
 |      True
 |      >>> tensor.equals(tensor2)
 |      False
 |
 |  to_numpy(self)
 |      Tensor.to_numpy(self)
 |
 |      Convert arrow::Tensor to numpy.ndarray with zero copy
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |      >>> tensor.to_numpy()
 |      array([[  2,   2,   4],
 |             [  4,   5, 100]], dtype=int32)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_numpy(obj, dim_names=None)
 |      Tensor.from_numpy(obj, dim_names=None)
 |
 |      Create a Tensor from a numpy array.
 |
 |      Parameters
 |      ----------
 |      obj : numpy.ndarray
 |          The source numpy array
 |      dim_names : list, optional
 |          Names of each dimension of the Tensor.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |      >>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |      <pyarrow.Tensor>
 |      type: int32
 |      shape: (2, 3)
 |      strides: (12, 4)
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  dim_names
 |      Names of this tensor dimensions.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |      >>> tensor.dim_names
 |      ['dim1', 'dim2']
 |
 |  is_contiguous
 |      Is this tensor contiguous in memory.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |      >>> tensor.is_contiguous
 |      True
 |
 |  is_mutable
 |      Is this tensor mutable or immutable.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |      >>> tensor.is_mutable
 |      True
 |
 |  ndim
 |      The dimension (n) of this tensor.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |      >>> tensor.ndim
 |      2
 |
 |  shape
 |      The shape of this tensor.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |      >>> tensor.shape
 |      (2, 3)
 |
 |  size
 |      The size of this tensor.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |      >>> tensor.size
 |      6
 |
 |  strides
 |      Strides of this tensor.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import numpy as np
 |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
 |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
 |      >>> tensor.strides
 |      (12, 4)
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __hash__ = None
 |
 |  __pyx_vtable__ = <capsule object NULL>

Python Library Documentation: class Time32Array in module pyarrow.lib

class Time32Array(NumericArray)
 |  Concrete class for Arrow arrays of time32 data type.
 |
 |  Method resolution order:
 |      Time32Array
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Time32Scalar in module pyarrow.lib

class Time32Scalar(Scalar)
 |  Concrete class for time32 scalars.
 |
 |  Method resolution order:
 |      Time32Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Time32Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python datetime.timedelta instance.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Time32Type in module pyarrow.lib

class Time32Type(DataType)
 |  Concrete class for time32 data types.
 |
 |  Supported time unit resolutions are 's' [second]
 |  and 'ms' [millisecond].
 |
 |  Examples
 |  --------
 |  Create an instance of time32 type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.time32('ms')
 |  Time32Type(time32[ms])
 |
 |  Method resolution order:
 |      Time32Type
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  unit
 |      The time unit ('s' or 'ms').
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.time32('ms')
 |      >>> t.unit
 |      'ms'
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      DataType.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class Time64Array in module pyarrow.lib

class Time64Array(NumericArray)
 |  Concrete class for Arrow arrays of time64 data type.
 |
 |  Method resolution order:
 |      Time64Array
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class Time64Scalar in module pyarrow.lib

class Time64Scalar(Scalar)
 |  Concrete class for time64 scalars.
 |
 |  Method resolution order:
 |      Time64Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      Time64Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python datetime.timedelta instance.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class Time64Type in module pyarrow.lib

class Time64Type(DataType)
 |  Concrete class for time64 data types.
 |
 |  Supported time unit resolutions are 'us' [microsecond]
 |  and 'ns' [nanosecond].
 |
 |  Examples
 |  --------
 |  Create an instance of time64 type:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.time64('us')
 |  Time64Type(time64[us])
 |
 |  Method resolution order:
 |      Time64Type
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  unit
 |      The time unit ('us' or 'ns').
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.time64('us')
 |      >>> t.unit
 |      'us'
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      DataType.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class TimestampArray in module pyarrow.lib

class TimestampArray(NumericArray)
 |  Concrete class for Arrow arrays of timestamp data type.
 |
 |  Method resolution order:
 |      TimestampArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class TimestampScalar in module pyarrow.lib

class TimestampScalar(Scalar)
 |  Concrete class for timestamp scalars.
 |
 |  Method resolution order:
 |      TimestampScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __repr__(...)
 |      TimestampScalar.__repr__(self)
 |
 |      Return the representation of TimestampScalar using `strftime` to avoid
 |      original repr datetime values being out of range.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      TimestampScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Pandas Timestamp instance (if units are
 |      nanoseconds and pandas is available), otherwise as a Python
 |      datetime.datetime instance.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  value
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class TimestampType in module pyarrow.lib

class TimestampType(DataType)
 |  Concrete class for timestamp data types.
 |
 |  Examples
 |  --------
 |  >>> import pyarrow as pa
 |
 |  Create an instance of timestamp type:
 |
 |  >>> pa.timestamp('us')
 |  TimestampType(timestamp[us])
 |
 |  Create an instance of timestamp type with timezone:
 |
 |  >>> pa.timestamp('s', tz='UTC')
 |  TimestampType(timestamp[s, tz=UTC])
 |
 |  Method resolution order:
 |      TimestampType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __reduce__(self)
 |      TimestampType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  tz
 |      The timestamp time zone, if any, or None.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.timestamp('s', tz='UTC')
 |      >>> t.tz
 |      'UTC'
 |
 |  unit
 |      The timestamp unit ('s', 'ms', 'us' or 'ns').
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> t = pa.timestamp('us')
 |      >>> t.unit
 |      'us'
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class TransformInputStream in module pyarrow.lib

class TransformInputStream(NativeFile)
 |  TransformInputStream(NativeFile stream, transform_func)
 |
 |  Transform an input stream.
 |
 |  Parameters
 |  ----------
 |  stream : NativeFile
 |      The stream to transform.
 |  transform_func : callable
 |      The transformation to apply.
 |
 |  Method resolution order:
 |      TransformInputStream
 |      NativeFile
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __reduce__ = __reduce_cython__(...)
 |      TransformInputStream.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      TransformInputStream.__setstate_cython__(self, __pyx_state)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from NativeFile:
 |
 |  __enter__(self)
 |      NativeFile.__enter__(self)
 |
 |  __exit__(self, exc_type, exc_value, tb)
 |      NativeFile.__exit__(self, exc_type, exc_value, tb)
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __next__(...)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  close(self)
 |      NativeFile.close(self)
 |
 |  download(self, stream_or_path, buffer_size=None)
 |      NativeFile.download(self, stream_or_path, buffer_size=None)
 |
 |      Read this file completely to a local path or destination stream.
 |
 |      This method first seeks to the beginning of the file.
 |
 |      Parameters
 |      ----------
 |      stream_or_path : str or file-like object
 |          If a string, a local file path to write to; otherwise,
 |          should be a writable stream.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  fileno(self)
 |      NativeFile.fileno(self)
 |
 |      NOT IMPLEMENTED
 |
 |  flush(self)
 |      NativeFile.flush(self)
 |
 |      Flush the stream, if applicable.
 |
 |      An error is raised if stream is not writable.
 |
 |  get_stream(self, file_offset, nbytes)
 |      NativeFile.get_stream(self, file_offset, nbytes)
 |
 |      Return an input stream that reads a file segment independent of the
 |      state of the file.
 |
 |      Allows reading portions of a random access file as an input stream
 |      without interfering with each other.
 |
 |      Parameters
 |      ----------
 |      file_offset : int
 |      nbytes : int
 |
 |      Returns
 |      -------
 |      stream : NativeFile
 |
 |  isatty(self)
 |      NativeFile.isatty(self)
 |
 |  metadata(self)
 |      NativeFile.metadata(self)
 |
 |      Return file metadata
 |
 |  read(self, nbytes=None)
 |      NativeFile.read(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      If *nbytes* is None, then the entire remaining file contents are read.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read1(self, nbytes=None)
 |      NativeFile.read1(self, nbytes=None)
 |
 |      Read and return up to n bytes.
 |
 |      Unlike read(), if *nbytes* is None then a chunk is read, not the
 |      entire file.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, default None
 |          The maximum number of bytes to read.
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_at(self, nbytes, offset)
 |      NativeFile.read_at(self, nbytes, offset)
 |
 |      Read indicated number of bytes at offset from the file
 |
 |      Parameters
 |      ----------
 |      nbytes : int
 |      offset : int
 |
 |      Returns
 |      -------
 |      data : bytes
 |
 |  read_buffer(self, nbytes=None)
 |      NativeFile.read_buffer(self, nbytes=None)
 |
 |      Read from buffer.
 |
 |      Parameters
 |      ----------
 |      nbytes : int, optional
 |          maximum number of bytes read
 |
 |  readable(self)
 |      NativeFile.readable(self)
 |
 |  readall(self)
 |      NativeFile.readall(self)
 |
 |  readinto(self, b)
 |      NativeFile.readinto(self, b)
 |
 |      Read into the supplied buffer
 |
 |      Parameters
 |      ----------
 |      b : buffer-like object
 |          A writable buffer object (such as a bytearray).
 |
 |      Returns
 |      -------
 |      written : int
 |          number of bytes written
 |
 |  readline(self, size=None)
 |      NativeFile.readline(self, size=None)
 |
 |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
 |
 |      If size is specified, read at most size bytes.
 |
 |      Line terminator is always b"\n".
 |
 |      Parameters
 |      ----------
 |      size : int
 |          maximum number of bytes read
 |
 |  readlines(self, hint=None)
 |      NativeFile.readlines(self, hint=None)
 |
 |      NOT IMPLEMENTED. Read lines of the file
 |
 |      Parameters
 |      ----------
 |      hint : int
 |          maximum number of bytes read until we stop
 |
 |  seek(self, position, whence=0)
 |      NativeFile.seek(self, int64_t position, int whence=0)
 |
 |      Change current file stream position
 |
 |      Parameters
 |      ----------
 |      position : int
 |          Byte offset, interpreted relative to value of whence argument
 |      whence : int, default 0
 |          Point of reference for seek offset
 |
 |      Notes
 |      -----
 |      Values of whence:
 |      * 0 -- start of stream (the default); offset should be zero or positive
 |      * 1 -- current stream position; offset may be negative
 |      * 2 -- end of stream; offset is usually negative
 |
 |      Returns
 |      -------
 |      int
 |          The new absolute stream position.
 |
 |  seekable(self)
 |      NativeFile.seekable(self)
 |
 |  size(self)
 |      NativeFile.size(self)
 |
 |      Return file size
 |
 |  tell(self)
 |      NativeFile.tell(self)
 |
 |      Return current stream position
 |
 |  truncate(self)
 |      NativeFile.truncate(self)
 |
 |      NOT IMPLEMENTED
 |
 |  upload(self, stream, buffer_size=None)
 |      NativeFile.upload(self, stream, buffer_size=None)
 |
 |      Write from a source stream to this file.
 |
 |      Parameters
 |      ----------
 |      stream : file-like object
 |          Source stream to pipe to this file.
 |      buffer_size : int, optional
 |          The buffer size to use for data transfers.
 |
 |  writable(self)
 |      NativeFile.writable(self)
 |
 |  write(self, data)
 |      NativeFile.write(self, data)
 |
 |      Write data to the file.
 |
 |      Parameters
 |      ----------
 |      data : bytes-like object or exporter of buffer protocol
 |
 |      Returns
 |      -------
 |      int
 |          nbytes: number of bytes written
 |
 |  writelines(self, lines)
 |      NativeFile.writelines(self, lines)
 |
 |      Write lines to the file.
 |
 |      Parameters
 |      ----------
 |      lines : iterable
 |          Iterable of bytes-like objects or exporters of buffer protocol
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from NativeFile:
 |
 |  closed
 |
 |  mode
 |      The file mode. Currently instances of NativeFile may support:
 |
 |      * rb: binary read
 |      * wb: binary write
 |      * rb+: binary read and write
 |      * ab: binary append

Python Library Documentation: class UInt16Array in module pyarrow.lib

class UInt16Array(IntegerArray)
 |  Concrete class for Arrow arrays of uint16 data type.
 |
 |  Method resolution order:
 |      UInt16Array
 |      IntegerArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class UInt16Scalar in module pyarrow.lib

class UInt16Scalar(Scalar)
 |  Concrete class for uint16 scalars.
 |
 |  Method resolution order:
 |      UInt16Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __index__(self, /)
 |      Return self converted to an integer, if self is suitable for use as an index into a list.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      UInt16Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python int.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class UInt32Array in module pyarrow.lib

class UInt32Array(IntegerArray)
 |  Concrete class for Arrow arrays of uint32 data type.
 |
 |  Method resolution order:
 |      UInt32Array
 |      IntegerArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class UInt32Scalar in module pyarrow.lib

class UInt32Scalar(Scalar)
 |  Concrete class for uint32 scalars.
 |
 |  Method resolution order:
 |      UInt32Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __index__(self, /)
 |      Return self converted to an integer, if self is suitable for use as an index into a list.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      UInt32Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python int.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class UInt64Array in module pyarrow.lib

class UInt64Array(IntegerArray)
 |  Concrete class for Arrow arrays of uint64 data type.
 |
 |  Method resolution order:
 |      UInt64Array
 |      IntegerArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class UInt64Scalar in module pyarrow.lib

class UInt64Scalar(Scalar)
 |  Concrete class for uint64 scalars.
 |
 |  Method resolution order:
 |      UInt64Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __index__(self, /)
 |      Return self converted to an integer, if self is suitable for use as an index into a list.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      UInt64Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python int.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class UInt8Array in module pyarrow.lib

class UInt8Array(IntegerArray)
 |  Concrete class for Arrow arrays of uint8 data type.
 |
 |  Method resolution order:
 |      UInt8Array
 |      IntegerArray
 |      NumericArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class UInt8Scalar in module pyarrow.lib

class UInt8Scalar(Scalar)
 |  Concrete class for uint8 scalars.
 |
 |  Method resolution order:
 |      UInt8Scalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __index__(self, /)
 |      Return self converted to an integer, if self is suitable for use as an index into a list.
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      UInt8Scalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this value as a Python int.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class UnionArray in module pyarrow.lib

class UnionArray(Array)
 |  Concrete class for Arrow arrays of a Union data type.
 |
 |  Method resolution order:
 |      UnionArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  child(self, pos)
 |      UnionArray.child(self, int pos)
 |
 |      DEPRECATED, use field() instead.
 |
 |      Parameters
 |      ----------
 |      pos : int
 |          The physical index of the union child field (not its type code).
 |
 |      Returns
 |      -------
 |      field : pyarrow.Field
 |          The given child field.
 |
 |  field(self, pos)
 |      UnionArray.field(self, int pos)
 |
 |      Return the given child field as an individual array.
 |
 |      For sparse unions, the returned array has its offset, length,
 |      and null count adjusted.
 |
 |      For dense unions, the returned array is unchanged.
 |
 |      Parameters
 |      ----------
 |      pos : int
 |          The physical index of the union child field (not its type code).
 |
 |      Returns
 |      -------
 |      field : Array
 |          The given child field.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_dense(types, value_offsets, children, field_names=None, type_codes=None)
 |      UnionArray.from_dense(Array types, Array value_offsets, list children, list field_names=None, list type_codes=None)
 |
 |      Construct dense UnionArray from arrays of int8 types, int32 offsets and
 |      children arrays
 |
 |      Parameters
 |      ----------
 |      types : Array (int8 type)
 |      value_offsets : Array (int32 type)
 |      children : list
 |      field_names : list
 |      type_codes : list
 |
 |      Returns
 |      -------
 |      union_array : UnionArray
 |
 |  from_sparse(types, children, field_names=None, type_codes=None)
 |      UnionArray.from_sparse(Array types, list children, list field_names=None, list type_codes=None)
 |
 |      Construct sparse UnionArray from arrays of int8 types and children
 |      arrays
 |
 |      Parameters
 |      ----------
 |      types : Array (int8 type)
 |      children : list
 |      field_names : list
 |      type_codes : list
 |
 |      Returns
 |      -------
 |      union_array : UnionArray
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  offsets
 |      Get the value offsets array (dense arrays only).
 |
 |      Does not account for any slice offset.
 |
 |  type_codes
 |      Get the type codes array.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class UnionScalar in module pyarrow.lib

class UnionScalar(Scalar)
 |  Concrete class for Union scalars.
 |
 |  Method resolution order:
 |      UnionScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      UnionScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return underlying value as a Python object.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  type_code
 |      Return the union type code for this scalar.
 |
 |  value
 |      Return underlying value as a scalar.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class UnionType in module pyarrow.lib

class UnionType(DataType)
 |  Base class for union data types.
 |
 |  Examples
 |  --------
 |  Create an instance of a dense UnionType using ``pa.union``:
 |
 |  >>> import pyarrow as pa
 |  >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())],
 |  ...          mode=pa.lib.UnionMode_DENSE),
 |  (DenseUnionType(dense_union<a: fixed_size_binary[10]=0, b: string=1>),)
 |
 |  Create an instance of a dense UnionType using ``pa.dense_union``:
 |
 |  >>> pa.dense_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |  DenseUnionType(dense_union<a: fixed_size_binary[10]=0, b: string=1>)
 |
 |  Create an instance of a sparse UnionType using ``pa.union``:
 |
 |  >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())],
 |  ...          mode=pa.lib.UnionMode_SPARSE),
 |  (SparseUnionType(sparse_union<a: fixed_size_binary[10]=0, b: string=1>),)
 |
 |  Create an instance of a sparse UnionType using ``pa.sparse_union``:
 |
 |  >>> pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |  SparseUnionType(sparse_union<a: fixed_size_binary[10]=0, b: string=1>)
 |
 |  Method resolution order:
 |      UnionType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getitem__(...)
 |      UnionType.__getitem__(self, i)
 |
 |      Return a child field by its index.
 |
 |      Alias of ``field``.
 |
 |  __iter__(...)
 |      UnionType.__iter__(self)
 |
 |      Iterate over union members, in order.
 |
 |  __len__(...)
 |      UnionType.__len__(self)
 |
 |      Like num_fields().
 |
 |  __reduce__(self)
 |      UnionType.__reduce__(self)
 |
 |  field(self, i)
 |      UnionType.field(self, i) -> Field
 |
 |      Return a child field by its numeric index.
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |      >>> union[0]
 |      pyarrow.Field<a: fixed_size_binary[10]>
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  mode
 |      The mode of the union ("dense" or "sparse").
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |      >>> union.mode
 |      'sparse'
 |
 |  type_codes
 |      The type code to indicate each data type in this union.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
 |      >>> union.type_codes
 |      [0, 1]
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  bit_width
 |      Bit width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().bit_width
 |      64
 |
 |  byte_width
 |      Byte width for fixed width type.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().byte_width
 |      8
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class UnknownExtensionType in module pyarrow.lib

class UnknownExtensionType(ExtensionType)
 |  UnknownExtensionType(DataType storage_type, serialized)
 |
 |  A concrete class for Python-defined extension types that refer to
 |  an unknown Python implementation.
 |
 |  Parameters
 |  ----------
 |  storage_type : DataType
 |      The storage type for which the extension is built.
 |  serialized : bytes
 |      The serialised output.
 |
 |  Method resolution order:
 |      UnknownExtensionType
 |      ExtensionType
 |      BaseExtensionType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_ext_serialize__(self)
 |      UnknownExtensionType.__arrow_ext_serialize__(self)
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  __arrow_ext_deserialize__(storage_type, serialized)
 |      UnknownExtensionType.__arrow_ext_deserialize__(cls, storage_type, serialized)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from ExtensionType:
 |
 |  __arrow_ext_class__(self)
 |      ExtensionType.__arrow_ext_class__(self)
 |
 |      Return an extension array class to be used for building or
 |      deserializing arrays with this extension type.
 |
 |      This method should return a subclass of the ExtensionArray class. By
 |      default, if not specialized in the extension implementation, an
 |      extension type array will be a built-in ExtensionArray instance.
 |
 |  __arrow_ext_scalar_class__(self)
 |      ExtensionType.__arrow_ext_scalar_class__(self)
 |
 |      Return an extension scalar class for building scalars with this
 |      extension type.
 |
 |      This method should return subclass of the ExtensionScalar class. By
 |      default, if not specialized in the extension implementation, an
 |      extension type scalar will be a built-in ExtensionScalar instance.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __reduce__(self)
 |      ExtensionType.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from ExtensionType:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseExtensionType:
 |
 |  wrap_array(self, storage)
 |      BaseExtensionType.wrap_array(self, storage)
 |
 |      Wrap the given storage array as an extension array.
 |
 |      Parameters
 |      ----------
 |      storage : Array or ChunkedArray
 |
 |      Returns
 |      -------
 |      array : Array or ChunkedArray
 |          Extension array wrapping the storage array
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from BaseExtensionType:
 |
 |  bit_width
 |      The bit width of the extension type.
 |
 |  byte_width
 |      The byte width of the extension type.
 |
 |  extension_name
 |      The extension type name.
 |
 |  storage_type
 |      The underlying storage type.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class UuidArray in module pyarrow.lib

class UuidArray(ExtensionArray)
 |  Concrete class for Arrow arrays of UUID data type.
 |
 |  Method resolution order:
 |      UuidArray
 |      ExtensionArray
 |      Array
 |      _PandasConvertible
 |      _Weakrefable
 |      builtins.object
 |
 |  Data descriptors defined here:
 |
 |  __dict__
 |      dictionary for instance variables
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from ExtensionArray:
 |
 |  __new__(*args, **kwargs) class method of pyarrow.lib.ExtensionArray
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_storage(typ, storage)
 |      ExtensionArray.from_storage(BaseExtensionType typ, Array storage)
 |
 |      Construct ExtensionArray from type and storage array.
 |
 |      Parameters
 |      ----------
 |      typ : DataType
 |          The extension type for the result array.
 |      storage : Array
 |          The underlying storage for the result array.
 |
 |      Returns
 |      -------
 |      ext_array : ExtensionArray
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ExtensionArray:
 |
 |  storage
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from ExtensionArray:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Array:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __array__(self, dtype=None, copy=None)
 |      Array.__array__(self, dtype=None, copy=None)
 |
 |  __arrow_c_array__(self, requested_schema=None)
 |      Array.__arrow_c_array__(self, requested_schema=None)
 |
 |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
 |          respectively.
 |
 |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
 |
 |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
 |      of the object.
 |
 |      Parameters
 |      ----------
 |      requested_schema : PyCapsule | None
 |          A PyCapsule containing a C ArrowSchema representation of a requested
 |          schema. PyArrow will attempt to cast the array to this data type.
 |          If None, the array will be returned as-is, with a type matching the
 |          one returned by :meth:`__arrow_c_schema__()`.
 |      kwargs
 |          Currently no additional keyword arguments are supported, but
 |          this method will accept any keyword with a value of ``None``
 |          for compatibility with future keywords.
 |
 |      Returns
 |      -------
 |      Tuple[PyCapsule, PyCapsule]
 |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
 |          respectively.
 |
 |  __dlpack__(self, stream=None)
 |      Array.__dlpack__(self, stream=None)
 |
 |      Export a primitive array as a DLPack capsule.
 |
 |      Parameters
 |      ----------
 |      stream : int, optional
 |          A Python integer representing a pointer to a stream. Currently not supported.
 |          Stream is provided by the consumer to the producer to instruct the producer
 |          to ensure that operations can safely be performed on the array.
 |
 |      Returns
 |      -------
 |      capsule : PyCapsule
 |          A DLPack capsule for the array, pointing to a DLManagedTensor.
 |
 |  __dlpack_device__(self)
 |      Array.__dlpack_device__(self)
 |
 |      Return the DLPack device tuple this arrays resides on.
 |
 |      Returns
 |      -------
 |      tuple : Tuple[int, int]
 |          Tuple with index specifying the type of the device (where
 |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
 |          device which is 0 by default for CPU.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getitem__(...)
 |      Array.__getitem__(self, key)
 |
 |      Slice or return value at given index
 |
 |      Parameters
 |      ----------
 |      key : integer or slice
 |          Slices with step not equal to 1 (or None) will produce a copy
 |          rather than a zero-copy view
 |
 |      Returns
 |      -------
 |      value : Scalar (index) or Array (slice)
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Array.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __sizeof__(self)
 |      Array.__sizeof__(self)
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  buffers(self)
 |      Array.buffers(self)
 |
 |      Return a list of Buffer objects pointing to this array's physical
 |      storage.
 |
 |      To correctly interpret these buffers, you need to also apply the offset
 |      multiplied with the size of the stored data type.
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast array values to another data type
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast array to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      cast : Array
 |
 |  copy_to(self, destination)
 |      Array.copy_to(self, destination)
 |
 |      Construct a copy of the array with all buffers on destination
 |      device.
 |
 |      This method recursively copies the array's buffers and those of its
 |      children onto the destination MemoryManager device and returns the
 |      new Array.
 |
 |      Parameters
 |      ----------
 |      destination : pyarrow.MemoryManager or pyarrow.Device
 |          The destination device to copy the array to.
 |
 |      Returns
 |      -------
 |      Array
 |
 |  dictionary_encode(self, null_encoding='mask')
 |      Array.dictionary_encode(self, null_encoding='mask')
 |
 |      Compute dictionary-encoded representation of array.
 |
 |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
 |
 |      Parameters
 |      ----------
 |      null_encoding : str, default "mask"
 |          How to handle null entries.
 |
 |      Returns
 |      -------
 |      encoded : DictionaryArray
 |          A dictionary-encoded version of this array.
 |
 |  diff(self, other)
 |      Array.diff(self, Array other)
 |
 |      Compare contents of this array against another one.
 |
 |      Return a string containing the result of diffing this array
 |      (on the left side) against the other array (on the right side).
 |
 |      Parameters
 |      ----------
 |      other : Array
 |          The other array to compare this array with.
 |
 |      Returns
 |      -------
 |      diff : str
 |          A human-readable printout of the differences.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> left = pa.array(["one", "two", "three"])
 |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
 |      >>> print(left.diff(right)) # doctest: +SKIP
 |
 |      @@ -0, +0 @@
 |      -"one"
 |      @@ -2, +1 @@
 |      +null
 |      +"two-and-a-half"
 |
 |  drop_null(self)
 |      Array.drop_null(self)
 |
 |      Remove missing values from an array.
 |
 |  equals(self, other)
 |      Array.equals(self, Array other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Array
 |
 |      Returns
 |      -------
 |      bool
 |
 |  fill_null(self, fill_value)
 |      Array.fill_null(self, fill_value)
 |
 |      See :func:`pyarrow.compute.fill_null` for usage.
 |
 |      Parameters
 |      ----------
 |      fill_value : any
 |          The replacement value for null entries.
 |
 |      Returns
 |      -------
 |      result : Array
 |          A new array with nulls replaced by the given value.
 |
 |  filter(self, mask, *, null_selection_behavior='drop')
 |      Array.filter(self, mask, *, null_selection_behavior='drop')
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.filter` for full usage.
 |
 |      Parameters
 |      ----------
 |      mask : Array or array-like
 |          The boolean mask to filter the array with.
 |      null_selection_behavior : str, default "drop"
 |          How nulls in the mask should be handled.
 |
 |      Returns
 |      -------
 |      filtered : Array
 |          An array of the same type, with only the elements selected by
 |          the boolean mask.
 |
 |  get_total_buffer_size(self)
 |      Array.get_total_buffer_size(self)
 |
 |      The sum of bytes in each buffer referenced by the array.
 |
 |      An array may only reference a portion of a buffer.
 |      This method will overestimate in this case and return the
 |      byte size of the entire buffer.
 |
 |      If a buffer is referenced multiple times then it will
 |      only be counted once.
 |
 |  index(self, value, start=None, end=None, *, memory_pool=None)
 |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
 |
 |      Find the first index of a value.
 |
 |      See :func:`pyarrow.compute.index` for full usage.
 |
 |      Parameters
 |      ----------
 |      value : Scalar or object
 |          The value to look for in the array.
 |      start : int, optional
 |          The start index where to look for `value`.
 |      end : int, optional
 |          The end index where to look for `value`.
 |      memory_pool : MemoryPool, optional
 |          A memory pool for potential memory allocations.
 |
 |      Returns
 |      -------
 |      index : Int64Scalar
 |          The index of the value in the array (-1 if not found).
 |
 |  is_nan(self)
 |      Array.is_nan(self)
 |
 |      Return BooleanArray indicating the NaN values.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_null(self, *, nan_is_null=False)
 |      Array.is_null(self, *, nan_is_null=False)
 |
 |      Return BooleanArray indicating the null values.
 |
 |      Parameters
 |      ----------
 |      nan_is_null : bool (optional, default False)
 |          Whether floating-point NaN values should also be considered null.
 |
 |      Returns
 |      -------
 |      array : boolean Array
 |
 |  is_valid(self)
 |      Array.is_valid(self)
 |
 |      Return BooleanArray indicating the non-null values.
 |
 |  slice(self, offset=0, length=None)
 |      Array.slice(self, offset=0, length=None)
 |
 |      Compute zero-copy slice of this array.
 |
 |      Parameters
 |      ----------
 |      offset : int, default 0
 |          Offset from start of array to slice.
 |      length : int, default None
 |          Length of slice (default is until end of Array starting from
 |          offset).
 |
 |      Returns
 |      -------
 |      sliced : Array
 |          An array with the same datatype, containing the sliced values.
 |
 |  sort(self, order='ascending', **kwargs)
 |      Array.sort(self, order='ascending', **kwargs)
 |
 |      Sort the Array
 |
 |      Parameters
 |      ----------
 |      order : str, default "ascending"
 |          Which order to sort values in.
 |          Accepted values are "ascending", "descending".
 |      **kwargs : dict, optional
 |          Additional sorting options.
 |          As allowed by :class:`SortOptions`
 |
 |      Returns
 |      -------
 |      result : Array
 |
 |  sum(self, **kwargs)
 |      Array.sum(self, **kwargs)
 |
 |      Sum the values in a numerical array.
 |
 |      See :func:`pyarrow.compute.sum` for full usage.
 |
 |      Parameters
 |      ----------
 |      **kwargs : dict, optional
 |          Options to pass to :func:`pyarrow.compute.sum`.
 |
 |      Returns
 |      -------
 |      sum : Scalar
 |          A scalar containing the sum value.
 |
 |  take(self, indices)
 |      Array.take(self, indices)
 |
 |      Select values from an array.
 |
 |      See :func:`pyarrow.compute.take` for full usage.
 |
 |      Parameters
 |      ----------
 |      indices : Array or array-like
 |          The indices in the array whose values will be returned.
 |
 |      Returns
 |      -------
 |      taken : Array
 |          An array with the same datatype, containing the taken values.
 |
 |  to_numpy(self, zero_copy_only=True, writable=False)
 |      Array.to_numpy(self, zero_copy_only=True, writable=False)
 |
 |      Return a NumPy view or copy of this array.
 |
 |      By default, tries to return a view of this array. This is only
 |      supported for primitive arrays with the same memory layout as NumPy
 |      (i.e. integers, floating point, ..) and without any nulls.
 |
 |      For the extension arrays, this method simply delegates to the
 |      underlying storage array.
 |
 |      Parameters
 |      ----------
 |      zero_copy_only : bool, default True
 |          If True, an exception will be raised if the conversion to a numpy
 |          array would require copying the underlying data (e.g. in presence
 |          of nulls, or for non-primitive types).
 |      writable : bool, default False
 |          For numpy arrays created with zero copy (view on the Arrow data),
 |          the resulting array is not writable (Arrow data is immutable).
 |          By setting this to True, a copy of the array is made to ensure
 |          it is writable.
 |
 |      Returns
 |      -------
 |      array : numpy.ndarray
 |
 |  to_pylist(self, *, maps_as_pydicts=None)
 |      Array.to_pylist(self, *, maps_as_pydicts=None)
 |
 |      Convert to a list of native Python objects.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |
 |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
 |          The last seen value of a duplicate key will be in the Python dictionary.
 |          If 'strict', this instead results in an exception being raised when detected.
 |
 |      Returns
 |      -------
 |      lst : list
 |
 |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
 |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
 |
 |      Render a "pretty-printed" string representation of the Array.
 |
 |      Note: for data on a non-CPU device, the full array is copied to CPU
 |      memory.
 |
 |      Parameters
 |      ----------
 |      indent : int, default 2
 |          How much to indent the internal items in the string to
 |          the right, by default ``2``.
 |      top_level_indent : int, default 0
 |          How much to indent right the entire content of the array,
 |          by default ``0``.
 |      window : int
 |          How many primitive items to preview at the begin and end
 |          of the array when the array is bigger than the window.
 |          The other items will be ellipsed.
 |      container_window : int
 |          How many container items (such as a list in a list array)
 |          to preview at the begin and end of the array when the array
 |          is bigger than the window.
 |      skip_new_lines : bool
 |          If the array should be rendered as a single line of text
 |          or if each element should be on its own line.
 |      element_size_limit : int, default 100
 |          Maximum number of characters of a single element before it is truncated.
 |
 |  tolist(self)
 |      Array.tolist(self)
 |
 |      Alias of to_pylist for compatibility with NumPy.
 |
 |  unique(self)
 |      Array.unique(self)
 |
 |      Compute distinct elements in array.
 |
 |      Returns
 |      -------
 |      unique : Array
 |          An array of the same data type, with deduplicated elements.
 |
 |  validate(self, *, full=False)
 |      Array.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  value_counts(self)
 |      Array.value_counts(self)
 |
 |      Compute counts of unique elements in array.
 |
 |      Returns
 |      -------
 |      StructArray
 |          An array of  <input type "Values", int64 "Counts"> structs
 |
 |  view(self, target_type)
 |      Array.view(self, target_type)
 |
 |      Return zero-copy "view" of array as another data type.
 |
 |      The data types must have compatible columnar buffer layouts
 |
 |      Parameters
 |      ----------
 |      target_type : DataType
 |          Type to construct view as.
 |
 |      Returns
 |      -------
 |      view : Array
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from Array:
 |
 |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
 |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
 |
 |      Construct an Array from a sequence of buffers.
 |
 |      The concrete type returned depends on the datatype.
 |
 |      Parameters
 |      ----------
 |      type : DataType
 |          The value type of the array.
 |      length : int
 |          The number of values in the array.
 |      buffers : List[Buffer | None]
 |          The buffers backing this array.
 |      null_count : int, default -1
 |          The number of null entries in the array. Negative value means that
 |          the null count is not known.
 |      offset : int, default 0
 |          The array's logical offset (in values, not in bytes) from the
 |          start of each buffer.
 |      children : List[Array], default None
 |          Nested type children with length matching type.num_fields.
 |
 |      Returns
 |      -------
 |      array : Array
 |
 |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
 |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
 |
 |      Convert pandas.Series to an Arrow Array.
 |
 |      This method uses Pandas semantics about what values indicate
 |      nulls. See pyarrow.array for more general conversion from arrays or
 |      sequences to Arrow arrays.
 |
 |      Parameters
 |      ----------
 |      obj : ndarray, pandas.Series, array-like
 |      mask : array (boolean), optional
 |          Indicate which values are null (True) or not null (False).
 |      type : pyarrow.DataType
 |          Explicit type to attempt to coerce to, otherwise will be inferred
 |          from the data.
 |      safe : bool, default True
 |          Check for overflows or other unsafe conversions.
 |      memory_pool : pyarrow.MemoryPool, optional
 |          If not passed, will allocate memory from the currently-set default
 |          memory pool.
 |
 |      Notes
 |      -----
 |      Localized timestamps will currently be returned as UTC (pandas's native
 |      representation). Timezone-naive data will be implicitly interpreted as
 |      UTC.
 |
 |      Returns
 |      -------
 |      array : pyarrow.Array or pyarrow.ChunkedArray
 |          ChunkedArray is returned if object data overflows binary buffer.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Array:
 |
 |  device_type
 |      The device type where the array resides.
 |
 |      Returns
 |      -------
 |      DeviceAllocationType
 |
 |  is_cpu
 |      Whether the array is CPU-accessible.
 |
 |  nbytes
 |      Total number of bytes consumed by the elements of the array.
 |
 |      In other words, the sum of bytes from all buffer
 |      ranges referenced.
 |
 |      Unlike `get_total_buffer_size` this method will account for array
 |      offsets.
 |
 |      If buffers are shared between arrays then the shared
 |      portion will be counted multiple times.
 |
 |      The dictionary of dictionary arrays will always be counted in their
 |      entirety even if the array only references a portion of the dictionary.
 |
 |  null_count
 |
 |  offset
 |      A relative position into another array's data.
 |
 |      The purpose is to enable zero-copy slicing. This value defaults to zero
 |      but must be applied on all operations with the physical storage
 |      buffers.
 |
 |  statistics
 |      Statistics of the array.
 |
 |  type
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from Array:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from _PandasConvertible:
 |
 |  __reduce_cython__(self)
 |      _PandasConvertible.__reduce_cython__(self)
 |
 |  __setstate__ = __setstate_cython__(...)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  __setstate_cython__(self, __pyx_state)
 |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
 |
 |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
 |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
 |
 |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
 |
 |      Parameters
 |      ----------
 |      memory_pool : MemoryPool, default None
 |          Arrow MemoryPool to use for allocations. Uses the default memory
 |          pool if not passed.
 |      categories : list, default empty
 |          List of fields that should be returned as pandas.Categorical. Only
 |          applies to table-like data structures.
 |      strings_to_categorical : bool, default False
 |          Encode string (UTF8) and binary types to pandas.Categorical.
 |      zero_copy_only : bool, default False
 |          Raise an ArrowException if this function call would require copying
 |          the underlying data.
 |      integer_object_nulls : bool, default False
 |          Cast integers with nulls to objects
 |      date_as_object : bool, default True
 |          Cast dates to objects. If False, convert to datetime64 dtype with
 |          the equivalent time unit (if supported). Note: in pandas version
 |          < 2.0, only datetime64[ns] conversion is supported.
 |      timestamp_as_object : bool, default False
 |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
 |          useful in pandas version 1.x if you have timestamps that don't fit
 |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
 |          Non-nanosecond timestamps are supported in pandas version 2.0.
 |          If False, all timestamps are converted to datetime64 dtype.
 |      use_threads : bool, default True
 |          Whether to parallelize the conversion using multiple threads.
 |      deduplicate_objects : bool, default True
 |          Do not create multiple copies Python objects when created, to save
 |          on memory use. Conversion will be slower.
 |      ignore_metadata : bool, default False
 |          If True, do not use the 'pandas' metadata to reconstruct the
 |          DataFrame index, if present
 |      safe : bool, default True
 |          For certain data types, a cast is needed in order to store the
 |          data in a pandas DataFrame or Series (e.g. timestamps are always
 |          stored as nanoseconds in pandas). This option controls whether it
 |          is a safe cast or not.
 |      split_blocks : bool, default False
 |          If True, generate one internal "block" for each column when
 |          creating a pandas.DataFrame from a RecordBatch or Table. While this
 |          can temporarily reduce memory note that various pandas operations
 |          can trigger "consolidation" which may balloon memory use.
 |      self_destruct : bool, default False
 |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
 |          memory while converting the Arrow object to pandas. If you use the
 |          object after calling to_pandas with this option it will crash your
 |          program.
 |
 |          Note that you may not see always memory usage improvements. For
 |          example, if multiple columns share an underlying allocation,
 |          memory can't be freed until all columns are converted.
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          The default behavior (`None`), is to convert Arrow Map arrays to
 |          Python association lists (list-of-tuples) in the same order as the
 |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
 |
 |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
 |          This can change the ordering of (key, value) pairs, and will
 |          deduplicate multiple keys, resulting in a possible loss of data.
 |
 |          If 'lossy', this key deduplication results in a warning printed
 |          when detected. If 'strict', this instead results in an exception
 |          being raised when detected.
 |      types_mapper : function, default None
 |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
 |          This can be used to override the default pandas type for conversion
 |          of built-in pyarrow types or in absence of pandas_metadata in the
 |          Table schema. The function receives a pyarrow DataType and is
 |          expected to return a pandas ExtensionDtype or ``None`` if the
 |          default conversion should be used for that type. If you have
 |          a dictionary mapping, you can pass ``dict.get`` as function.
 |      coerce_temporal_nanoseconds : bool, default False
 |          Only applicable to pandas version >= 2.0.
 |          A legacy option to coerce date32, date64, duration, and timestamp
 |          time units to nanoseconds when converting to pandas. This is the
 |          default behavior in pandas version 1.x. Set this option to True if
 |          you'd like to use this coercion when using pandas version >= 2.0
 |          for backwards compatibility (not recommended otherwise).
 |
 |      Returns
 |      -------
 |      pandas.Series or pandas.DataFrame depending on type of object
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> import pandas as pd
 |
 |      Convert a Table to pandas DataFrame:
 |
 |      >>> table = pa.table([
 |      ...    pa.array([2, 4, 5, 100]),
 |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      ...    ], names=['n_legs', 'animals'])
 |      >>> table.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(table.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a RecordBatch to pandas DataFrame:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.array([2, 4, 5, 100])
 |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
 |      >>> batch = pa.record_batch([n_legs, animals],
 |      ...                         names=["n_legs", "animals"])
 |      >>> batch
 |      pyarrow.RecordBatch
 |      n_legs: int64
 |      animals: string
 |      ----
 |      n_legs: [2,4,5,100]
 |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
 |      >>> batch.to_pandas()
 |         n_legs        animals
 |      0       2       Flamingo
 |      1       4          Horse
 |      2       5  Brittle stars
 |      3     100      Centipede
 |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
 |      True
 |
 |      Convert a Chunked Array to pandas Series:
 |
 |      >>> import pyarrow as pa
 |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
 |      >>> n_legs.to_pandas()
 |      0      2
 |      1      2
 |      2      4
 |      3      4
 |      4      5
 |      5    100
 |      dtype: int64
 |      >>> isinstance(n_legs.to_pandas(), pd.Series)
 |      True

Python Library Documentation: class UuidScalar in module pyarrow.lib

class UuidScalar(ExtensionScalar)
 |  Concrete class for Uuid extension scalar.
 |
 |  Method resolution order:
 |      UuidScalar
 |      ExtensionScalar
 |      Scalar
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  as_py(self, *, maps_as_pydicts=None)
 |      UuidScalar.as_py(self, *, maps_as_pydicts=None)
 |
 |      Return this scalar as a Python UUID.
 |
 |      Parameters
 |      ----------
 |      maps_as_pydicts : str, optional, default `None`
 |          Valid values are `None`, 'lossy', or 'strict'.
 |          This parameter is ignored for non-nested Scalars.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  __dict__
 |      dictionary for instance variables
 |
 |  ----------------------------------------------------------------------
 |  Static methods inherited from ExtensionScalar:
 |
 |  __new__(*args, **kwargs) class method of pyarrow.lib.ExtensionScalar
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  from_storage(typ, value)
 |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
 |
 |      Construct ExtensionScalar from type and storage value.
 |
 |      Parameters
 |      ----------
 |      typ : DataType
 |          The extension type for the result scalar.
 |      value : object
 |          The storage value for the result scalar.
 |
 |      Returns
 |      -------
 |      ext_scalar : ExtensionScalar
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from ExtensionScalar:
 |
 |  value
 |      Return storage value as a scalar.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from ExtensionScalar:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Scalar:
 |
 |  __abs__(self, /)
 |      abs(self)
 |
 |  __add__(...)
 |
 |  __and__(...)
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lshift__(...)
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(...)
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __neg__(self, /)
 |      -self
 |
 |  __or__(...)
 |      Return self|value.
 |
 |  __pow__(self, value, mod=None, /)
 |      Return pow(self, value, mod).
 |
 |  __radd__(self, value, /)
 |      Return value+self.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __reduce__(self)
 |      Scalar.__reduce__(self)
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __rlshift__(self, value, /)
 |      Return value<<self.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rpow__(self, value, mod=None, /)
 |      Return pow(value, self, mod).
 |
 |  __rrshift__(self, value, /)
 |      Return value>>self.
 |
 |  __rshift__(...)
 |
 |  __rsub__(self, value, /)
 |      Return value-self.
 |
 |  __rtruediv__(self, value, /)
 |      Return value/self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  __sub__(...)
 |
 |  __truediv__(...)
 |
 |  __xor__(...)
 |
 |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
 |
 |      Cast scalar value to another data type.
 |
 |      See :func:`pyarrow.compute.cast` for usage.
 |
 |      Parameters
 |      ----------
 |      target_type : DataType, default None
 |          Type to cast scalar to.
 |      safe : boolean, default True
 |          Whether to check for conversion errors such as overflow.
 |      options : CastOptions, default None
 |          Additional checks pass by CastOptions
 |      memory_pool : MemoryPool, optional
 |          memory pool to use for allocations during function execution.
 |
 |      Returns
 |      -------
 |      scalar : A Scalar of the given target data type.
 |
 |  equals(self, other)
 |      Scalar.equals(self, Scalar other)
 |
 |      Parameters
 |      ----------
 |      other : pyarrow.Scalar
 |
 |      Returns
 |      -------
 |      bool
 |
 |  validate(self, *, full=False)
 |      Scalar.validate(self, *, full=False)
 |
 |      Perform validation checks.  An exception is raised if validation fails.
 |
 |      By default only cheap validation checks are run.  Pass `full=True`
 |      for thorough validation checks (potentially O(n)).
 |
 |      Parameters
 |      ----------
 |      full : bool, default False
 |          If True, run expensive checks, otherwise cheap checks only.
 |
 |      Raises
 |      ------
 |      ArrowInvalid
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Scalar:
 |
 |  is_valid
 |      Holds a valid (non-null) value.
 |
 |  type
 |      Data type of the Scalar object.

Python Library Documentation: class UuidType in module pyarrow.lib

class UuidType(BaseExtensionType)
 |  Concrete class for UUID extension type.
 |
 |  Method resolution order:
 |      UuidType
 |      BaseExtensionType
 |      DataType
 |      _Weakrefable
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __arrow_ext_class__(self)
 |      UuidType.__arrow_ext_class__(self)
 |
 |  __arrow_ext_scalar_class__(self)
 |      UuidType.__arrow_ext_scalar_class__(self)
 |
 |  __reduce__(self)
 |      UuidType.__reduce__(self)
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __pyx_vtable__ = <capsule object NULL>
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from BaseExtensionType:
 |
 |  wrap_array(self, storage)
 |      BaseExtensionType.wrap_array(self, storage)
 |
 |      Wrap the given storage array as an extension array.
 |
 |      Parameters
 |      ----------
 |      storage : Array or ChunkedArray
 |
 |      Returns
 |      -------
 |      array : Array or ChunkedArray
 |          Extension array wrapping the storage array
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from BaseExtensionType:
 |
 |  bit_width
 |      The bit width of the extension type.
 |
 |  byte_width
 |      The byte width of the extension type.
 |
 |  extension_name
 |      The extension type name.
 |
 |  storage_type
 |      The underlying storage type.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from DataType:
 |
 |  __arrow_c_schema__(self)
 |      DataType.__arrow_c_schema__(self)
 |
 |      Export to a ArrowSchema PyCapsule
 |
 |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __init__(self, /, *args, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __str__(self, /)
 |      Return str(self).
 |
 |  equals(self, other, *, check_metadata=False)
 |      DataType.equals(self, other, *, check_metadata=False)
 |
 |      Return true if type is equivalent to passed value.
 |
 |      Parameters
 |      ----------
 |      other : DataType or string convertible to DataType
 |      check_metadata : bool
 |          Whether nested Field metadata equality should be checked as well.
 |
 |      Returns
 |      -------
 |      is_equal : bool
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().equals(pa.string())
 |      False
 |      >>> pa.int64().equals(pa.int64())
 |      True
 |
 |  field(self, i)
 |      DataType.field(self, i) -> Field
 |
 |      Parameters
 |      ----------
 |      i : int
 |
 |      Returns
 |      -------
 |      pyarrow.Field
 |
 |  to_pandas_dtype(self)
 |      DataType.to_pandas_dtype(self)
 |
 |      Return the equivalent NumPy / Pandas dtype.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().to_pandas_dtype()
 |      <class 'numpy.int64'>
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from DataType:
 |
 |  has_variadic_buffers
 |      If True, the number of expected buffers is only
 |      lower-bounded by num_buffers.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().has_variadic_buffers
 |      False
 |      >>> pa.string_view().has_variadic_buffers
 |      True
 |
 |  id
 |
 |  num_buffers
 |      Number of data buffers required to construct Array type
 |      excluding children.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64().num_buffers
 |      2
 |      >>> pa.string().num_buffers
 |      3
 |
 |  num_fields
 |      The number of child fields.
 |
 |      Examples
 |      --------
 |      >>> import pyarrow as pa
 |      >>> pa.int64()
 |      DataType(int64)
 |      >>> pa.int64().num_fields
 |      0
 |      >>> pa.list_(pa.string())
 |      ListType(list<item: string>)
 |      >>> pa.list_(pa.string()).num_fields
 |      1
 |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
 |      >>> struct.num_fields
 |      2

Python Library Documentation: class VersionInfo in module importlib._bootstrap

class VersionInfo(builtins.tuple)
 |  VersionInfo(major, minor, patch)
 |
 |  VersionInfo(major, minor, patch)
 |
 |  Method resolution order:
 |      VersionInfo
 |      builtins.tuple
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getnewargs__(self) from collections.VersionInfo
 |      Return self as a plain tuple.  Used by copy and pickle.
 |
 |  __repr__(self) from collections.VersionInfo
 |      Return a nicely formatted representation string
 |
 |  _asdict(self) from collections.VersionInfo
 |      Return a new dict which maps field names to their values.
 |
 |  _replace(self, /, **kwds) from collections.VersionInfo
 |      Return a new VersionInfo object replacing specified fields with new values
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  _make(iterable) from collections.VersionInfo
 |      Make a new VersionInfo object from a sequence or iterable
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(_cls, major, minor, patch) from namedtuple_VersionInfo.VersionInfo
 |      Create new instance of VersionInfo(major, minor, patch)
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  major
 |      Alias for field number 0
 |
 |  minor
 |      Alias for field number 1
 |
 |  patch
 |      Alias for field number 2
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __match_args__ = ('major', 'minor', 'patch')
 |
 |  _field_defaults = {}
 |
 |  _fields = ('major', 'minor', 'patch')
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.tuple:
 |
 |  __add__(self, value, /)
 |      Return self+value.
 |
 |  __contains__(self, key, /)
 |      Return bool(key in self).
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(self, value, /)
 |      Return self*value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  count(self, value, /)
 |      Return number of occurrences of value.
 |
 |  index(self, value, start=0, stop=9223372036854775807, /)
 |      Return first index of value.
 |
 |      Raises ValueError if the value is not present.
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from builtins.tuple:
 |
 |  __class_getitem__(...)
 |      See PEP 585

Python Library Documentation: cython_function_or_method in module pyarrow.lib

allocate_buffer(size, memory_pool=None, resizable=False)
    allocate_buffer(int64_t size, MemoryPool memory_pool=None, resizable=False)

    Allocate a mutable buffer.

    Parameters
    ----------
    size : int
        Number of bytes to allocate (plus internal padding)
    memory_pool : MemoryPool, optional
        The pool to allocate memory from.
        If not given, the default memory pool is used.
    resizable : bool, default False
        If true, the returned buffer is resizable.

    Returns
    -------
    buffer : Buffer or ResizableBuffer

Python Library Documentation: cython_function_or_method in module pyarrow.lib

arange(start, stop, step=1, *, memory_pool=None)
    arange(int64_t start, int64_t stop, int64_t step=1, *, memory_pool=None)

    Create an array of evenly spaced values within a given interval.

    This function is similar to Python's `range` function.
    The resulting array will contain values starting from `start` up to but not
    including `stop`, with a step size of `step`.

    Parameters
    ----------
    start : int
        The starting value for the sequence. The returned array will include this value.
    stop : int
        The stopping value for the sequence. The returned array will not include this value.
    step : int, default 1
        The spacing between values.
    memory_pool : MemoryPool, optional
        A memory pool to use for memory allocations.

    Raises
    ------
    ArrowInvalid
        If `step` is zero.

    Returns
    -------
    arange : Array

Python Library Documentation: cython_function_or_method in module pyarrow.lib

array(obj, type=None, mask=None, size=None, from_pandas=None, safe=True, memory_pool=None)
    array(obj, type=None, mask=None, size=None, from_pandas=None, bool safe=True, MemoryPool memory_pool=None)

    Create pyarrow.Array instance from a Python object.

    Parameters
    ----------
    obj : sequence, iterable, ndarray, pandas.Series, Arrow-compatible array
        If both type and size are specified may be a single use iterable. If
        not strongly-typed, Arrow type will be inferred for resulting array.
        Any Arrow-compatible array that implements the Arrow PyCapsule Protocol
        (has an ``__arrow_c_array__`` or ``__arrow_c_device_array__`` method)
        can be passed as well.
    type : pyarrow.DataType
        Explicit type to attempt to coerce to, otherwise will be inferred from
        the data.
    mask : array[bool], optional
        Indicate which values are null (True) or not null (False).
    size : int64, optional
        Size of the elements. If the input is larger than size bail at this
        length. For iterators, if size is larger than the input iterator this
        will be treated as a "max size", but will involve an initial allocation
        of size followed by a resize to the actual size (so if you know the
        exact size specifying it correctly will give you better performance).
    from_pandas : bool, default None
        Use pandas's semantics for inferring nulls from values in
        ndarray-like data. If passed, the mask tasks precedence, but
        if a value is unmasked (not-null), but still null according to
        pandas semantics, then it is null. Defaults to False if not
        passed explicitly by user, or True if a pandas object is
        passed in.
    safe : bool, default True
        Check for overflows or other unsafe conversions.
    memory_pool : pyarrow.MemoryPool, optional
        If not passed, will allocate memory from the currently-set default
        memory pool.

    Returns
    -------
    array : pyarrow.Array or pyarrow.ChunkedArray
        A ChunkedArray instead of an Array is returned if:

        - the object data overflowed binary storage.
        - the object's ``__arrow_array__`` protocol method returned a chunked
          array.

    Notes
    -----
    Timezone will be preserved in the returned array for timezone-aware data,
    else no timezone will be returned for naive timestamps.
    Internally, UTC values are stored for timezone-aware data with the
    timezone set in the data type.

    Pandas's DateOffsets and dateutil.relativedelta.relativedelta are by
    default converted as MonthDayNanoIntervalArray. relativedelta leapdays
    are ignored as are all absolute fields on both objects. datetime.timedelta
    can also be converted to MonthDayNanoIntervalArray but this requires
    passing MonthDayNanoIntervalType explicitly.

    Converting to dictionary array will promote to a wider integer type for
    indices if the number of distinct values cannot be represented, even if
    the index type was explicitly set. This means that if there are more than
    127 values the returned dictionary array's index type will be at least
    pa.int16() even if pa.int8() was passed to the function. Note that an
    explicit index type will not be demoted even if it is wider than required.

    This class supports Python's standard operators
    for element-wise operations, i.e. arithmetic (`+`, `-`, `/`, `%`, `**`),
    bitwise (`&`, `|`, `^`, `>>`, `<<`) and others.
    They can be used directly instead of calling underlying
    `pyarrow.compute` functions explicitly.

    Examples
    --------
    >>> import pandas as pd
    >>> import pyarrow as pa
    >>> pa.array(pd.Series([1, 2]))
    <pyarrow.lib.Int64Array object at ...>
    [
      1,
      2
    ]

    >>> pa.array(["a", "b", "a"], type=pa.dictionary(pa.int8(), pa.string()))
    <pyarrow.lib.DictionaryArray object at ...>
    ...
    -- dictionary:
      [
        "a",
        "b"
      ]
    -- indices:
      [
        0,
        1,
        0
      ]

    >>> import numpy as np
    >>> pa.array(pd.Series([1, 2]), mask=np.array([0, 1], dtype=bool))
    <pyarrow.lib.Int64Array object at ...>
    [
      1,
      null
    ]

    >>> arr = pa.array(range(1024), type=pa.dictionary(pa.int8(), pa.int64()))
    >>> arr.type.index_type
    DataType(int16)

    >>> arr1 = pa.array([1, 2, 3], type=pa.int8())
    >>> arr2 = pa.array([4, 5, 6], type=pa.int8())
    >>> arr1 + arr2
    <pyarrow.lib.Int8Array object at ...>
    [
      5,
      7,
      9
    ]

    >>> val = pa.scalar(42)
    >>> val - arr1
    <pyarrow.lib.Int64Array object at ...>
    [
      41,
      40,
      39
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

binary(length=-1)
    binary(int length=-1)

    Create variable-length or fixed size binary type.

    Parameters
    ----------
    length : int, optional, default -1
        If length == -1 then return a variable length binary type. If length is
        greater than or equal to 0 then return a fixed size binary type of
        width `length`.

    Examples
    --------
    Create an instance of a variable-length binary type:

    >>> import pyarrow as pa
    >>> pa.binary()
    DataType(binary)

    and use the variable-length binary type to create an array:

    >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary())
    <pyarrow.lib.BinaryArray object at ...>
    [
      666F6F,
      626172,
      62617A
    ]

    Create an instance of a fixed-size binary type:

    >>> pa.binary(3)
    FixedSizeBinaryType(fixed_size_binary[3])

    and use the fixed-length binary type to create an array:

    >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary(3))
    <pyarrow.lib.FixedSizeBinaryArray object at ...>
    [
      666F6F,
      626172,
      62617A
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

binary_view()
    binary_view()

    Create a variable-length binary view type.

    Examples
    --------
    Create an instance of a string type:

    >>> import pyarrow as pa
    >>> pa.binary_view()
    DataType(binary_view)

Python Library Documentation: cython_function_or_method in module pyarrow.lib

bool8()
    bool8()

    Create instance of bool8 extension type.

    Examples
    --------
    Create an instance of bool8 extension type:

    >>> import pyarrow as pa
    >>> type = pa.bool8()
    >>> type
    Bool8Type(extension<arrow.bool8>)

    Inspect the data type:

    >>> type.storage_type
    DataType(int8)

    Create a table with a bool8 array:

    >>> arr = [-1, 0, 1, 2, None]
    >>> storage = pa.array(arr, pa.int8())
    >>> other = pa.ExtensionArray.from_storage(type, storage)
    >>> pa.table([other], names=["unknown_col"])
    pyarrow.Table
    unknown_col: extension<arrow.bool8>
    ----
    unknown_col: [[-1,0,1,2,null]]

    Returns
    -------
    type : Bool8Type

Python Library Documentation: cython_function_or_method in module pyarrow.lib

bool_()
    bool_()

    Create instance of boolean type.

    Examples
    --------
    Create an instance of a boolean type:

    >>> import pyarrow as pa
    >>> pa.bool_()
    DataType(bool)
    >>> print(pa.bool_())
    bool

    Create a ``Field`` type with a boolean type
    and a name:

    >>> pa.field('bool_field', pa.bool_())
    pyarrow.Field<bool_field: bool>

Python Library Documentation: BuildInfo in module importlib._bootstrap object

class BuildInfo(builtins.tuple)
 |  BuildInfo(build_type, cpp_build_info)
 |
 |  BuildInfo(build_type, cpp_build_info)
 |
 |  Method resolution order:
 |      BuildInfo
 |      builtins.tuple
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getnewargs__(self) from collections.BuildInfo
 |      Return self as a plain tuple.  Used by copy and pickle.
 |
 |  __repr__(self) from collections.BuildInfo
 |      Return a nicely formatted representation string
 |
 |  _asdict(self) from collections.BuildInfo
 |      Return a new dict which maps field names to their values.
 |
 |  _replace(self, /, **kwds) from collections.BuildInfo
 |      Return a new BuildInfo object replacing specified fields with new values
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  _make(iterable) from collections.BuildInfo
 |      Make a new BuildInfo object from a sequence or iterable
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(_cls, build_type, cpp_build_info) from namedtuple_BuildInfo.BuildInfo
 |      Create new instance of BuildInfo(build_type, cpp_build_info)
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  build_type
 |      Alias for field number 0
 |
 |  cpp_build_info
 |      Alias for field number 1
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __match_args__ = ('build_type', 'cpp_build_info')
 |
 |  _field_defaults = {}
 |
 |  _fields = ('build_type', 'cpp_build_info')
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.tuple:
 |
 |  __add__(self, value, /)
 |      Return self+value.
 |
 |  __contains__(self, key, /)
 |      Return bool(key in self).
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(self, value, /)
 |      Return self*value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  count(self, value, /)
 |      Return number of occurrences of value.
 |
 |  index(self, value, start=0, stop=9223372036854775807, /)
 |      Return first index of value.
 |
 |      Raises ValueError if the value is not present.
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from builtins.tuple:
 |
 |  __class_getitem__(...)
 |      See PEP 585

Python Library Documentation: cython_function_or_method in module pyarrow.lib

chunked_array(arrays, type=None)
    chunked_array(arrays, type=None)

    Construct chunked array from list of array-like objects

    Parameters
    ----------
    arrays : Array, list of Array, or array-like
        Must all be the same data type. Can be empty only if type also passed.
        Any Arrow-compatible array that implements the Arrow PyCapsule Protocol
        (has an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method) can be
        passed as well.
    type : DataType or string coercible to DataType

    Returns
    -------
    ChunkedArray

    Examples
    --------
    >>> import pyarrow as pa
    >>> pa.chunked_array([], type=pa.int8())
    <pyarrow.lib.ChunkedArray object at ...>
    [
    ...
    ]

    >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]])
    <pyarrow.lib.ChunkedArray object at ...>
    [
      [
        2,
        2,
        4
      ],
      [
        4,
        5,
        100
      ]
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

compress(buf, codec='lz4', asbytes=False, memory_pool=None)
    compress(buf, codec='lz4', asbytes=False, memory_pool=None)

    Compress data from buffer-like object.

    Parameters
    ----------
    buf : pyarrow.Buffer, bytes, or other object supporting buffer protocol
    codec : str, default 'lz4'
        Compression codec.
        Supported types: {'brotli, 'gzip', 'lz4', 'lz4_raw', 'snappy', 'zstd'}
    asbytes : bool, default False
        Return result as Python bytes object, otherwise Buffer.
    memory_pool : MemoryPool, default None
        Memory pool to use for buffer allocations, if any.

    Returns
    -------
    compressed : pyarrow.Buffer or bytes (if asbytes=True)

Python Library Documentation: cython_function_or_method in module pyarrow.lib

concat_arrays(arrays, memory_pool=None)
    concat_arrays(arrays, MemoryPool memory_pool=None)

    Concatenate the given arrays.

    The contents of the input arrays are copied into the returned array.

    Raises
    ------
    ArrowInvalid
        If not all of the arrays have the same type.

    Parameters
    ----------
    arrays : iterable of pyarrow.Array
        Arrays to concatenate, must be identically typed.
    memory_pool : MemoryPool, default None
        For memory allocations. If None, the default pool is used.

    Examples
    --------
    >>> import pyarrow as pa
    >>> arr1 = pa.array([2, 4, 5, 100])
    >>> arr2 = pa.array([2, 4])
    >>> pa.concat_arrays([arr1, arr2])
    <pyarrow.lib.Int64Array object at ...>
    [
      2,
      4,
      5,
      100,
      2,
      4
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

concat_batches(recordbatches, memory_pool=None)
    concat_batches(recordbatches, MemoryPool memory_pool=None)

    Concatenate pyarrow.RecordBatch objects.

    All recordbatches must share the same Schema,
    the operation implies a copy of the data to merge
    the arrays of the different RecordBatches.

    Parameters
    ----------
    recordbatches : iterable of pyarrow.RecordBatch objects
        Pyarrow record batches to concatenate into a single RecordBatch.
    memory_pool : MemoryPool, default None
        For memory allocations, if required, otherwise use default pool.

    Examples
    --------
    >>> import pyarrow as pa
    >>> t1 = pa.record_batch([
    ...     pa.array([2, 4, 5, 100]),
    ...     pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
    ...     ], names=['n_legs', 'animals'])
    >>> t2 = pa.record_batch([
    ...     pa.array([2, 4]),
    ...     pa.array(["Parrot", "Dog"])
    ...     ], names=['n_legs', 'animals'])
    >>> pa.concat_batches([t1,t2])
    pyarrow.RecordBatch
    n_legs: int64
    animals: string
    ----
    n_legs: [2,4,5,100,2,4]
    animals: ["Flamingo","Horse","Brittle stars","Centipede","Parrot","Dog"]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

concat_tables(tables, memory_pool=None, promote_options='none', **kwargs)
    concat_tables(tables, MemoryPool memory_pool=None, str promote_options='none', **kwargs)

    Concatenate pyarrow.Table objects.

    If promote_options="none", a zero-copy concatenation will be performed. The schemas
    of all the Tables must be the same (except the metadata), otherwise an
    exception will be raised. The result Table will share the metadata with the
    first table.

    If promote_options="default", any null type arrays will be casted to the type of other
    arrays in the column of the same name. If a table is missing a particular
    field, null values of the appropriate type will be generated to take the
    place of the missing field. The new schema will share the metadata with the
    first table. Each field in the new schema will share the metadata with the
    first table which has the field defined. Note that type promotions may
    involve additional allocations on the given ``memory_pool``.

    If promote_options="permissive", the behavior of default plus types will be promoted
    to the common denominator that fits all the fields.

    Parameters
    ----------
    tables : iterable of pyarrow.Table objects
        Pyarrow tables to concatenate into a single Table.
    memory_pool : MemoryPool, default None
        For memory allocations, if required, otherwise use default pool.
    promote_options : str, default none
        Accepts strings "none", "default" and "permissive".
    **kwargs : dict, optional

    Examples
    --------
    >>> import pyarrow as pa
    >>> t1 = pa.table([
    ...     pa.array([2, 4, 5, 100]),
    ...     pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
    ...     ], names=['n_legs', 'animals'])
    >>> t2 = pa.table([
    ...     pa.array([2, 4]),
    ...     pa.array(["Parrot", "Dog"])
    ...     ], names=['n_legs', 'animals'])
    >>> pa.concat_tables([t1,t2])
    pyarrow.Table
    n_legs: int64
    animals: string
    ----
    n_legs: [[2,4,5,100],[2,4]]
    animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Parrot","Dog"]]

Python Library Documentation: CppBuildInfo in module importlib._bootstrap object

class CppBuildInfo(builtins.tuple)
 |  CppBuildInfo(version, version_info, so_version, full_so_version, compiler_id, compiler_version, compiler_flags, git_id, git_description, package_kind, build_type)
 |
 |  CppBuildInfo(version, version_info, so_version, full_so_version, compiler_id, compiler_version, compiler_flags, git_id, git_description, package_kind, build_type)
 |
 |  Method resolution order:
 |      CppBuildInfo
 |      builtins.tuple
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getnewargs__(self) from collections.CppBuildInfo
 |      Return self as a plain tuple.  Used by copy and pickle.
 |
 |  __repr__(self) from collections.CppBuildInfo
 |      Return a nicely formatted representation string
 |
 |  _asdict(self) from collections.CppBuildInfo
 |      Return a new dict which maps field names to their values.
 |
 |  _replace(self, /, **kwds) from collections.CppBuildInfo
 |      Return a new CppBuildInfo object replacing specified fields with new values
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  _make(iterable) from collections.CppBuildInfo
 |      Make a new CppBuildInfo object from a sequence or iterable
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(_cls, version, version_info, so_version, full_so_version, compiler_id, compiler_version, compiler_flags, git_id, git_description, package_kind, build_type) from namedtuple_CppBuildInfo.CppBuildInfo
 |      Create new instance of CppBuildInfo(version, version_info, so_version, full_so_version, compiler_id, compiler_version, compiler_flags, git_id, git_description, package_kind, build_type)
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  version
 |      Alias for field number 0
 |
 |  version_info
 |      Alias for field number 1
 |
 |  so_version
 |      Alias for field number 2
 |
 |  full_so_version
 |      Alias for field number 3
 |
 |  compiler_id
 |      Alias for field number 4
 |
 |  compiler_version
 |      Alias for field number 5
 |
 |  compiler_flags
 |      Alias for field number 6
 |
 |  git_id
 |      Alias for field number 7
 |
 |  git_description
 |      Alias for field number 8
 |
 |  package_kind
 |      Alias for field number 9
 |
 |  build_type
 |      Alias for field number 10
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __match_args__ = ('version', 'version_info', 'so_version', 'full_so_ve...
 |
 |  _field_defaults = {}
 |
 |  _fields = ('version', 'version_info', 'so_version', 'full_so_version',...
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.tuple:
 |
 |  __add__(self, value, /)
 |      Return self+value.
 |
 |  __contains__(self, key, /)
 |      Return bool(key in self).
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(self, value, /)
 |      Return self*value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  count(self, value, /)
 |      Return number of occurrences of value.
 |
 |  index(self, value, start=0, stop=9223372036854775807, /)
 |      Return first index of value.
 |
 |      Raises ValueError if the value is not present.
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from builtins.tuple:
 |
 |  __class_getitem__(...)
 |      See PEP 585

Error rendering doc: No Python documentation found for '24.0.0'.
Use help() to get the interactive help utility.
Use help(str) for help on the str class.

Python Library Documentation: VersionInfo in module importlib._bootstrap object

class VersionInfo(builtins.tuple)
 |  VersionInfo(major, minor, patch)
 |
 |  VersionInfo(major, minor, patch)
 |
 |  Method resolution order:
 |      VersionInfo
 |      builtins.tuple
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __getnewargs__(self) from collections.VersionInfo
 |      Return self as a plain tuple.  Used by copy and pickle.
 |
 |  __repr__(self) from collections.VersionInfo
 |      Return a nicely formatted representation string
 |
 |  _asdict(self) from collections.VersionInfo
 |      Return a new dict which maps field names to their values.
 |
 |  _replace(self, /, **kwds) from collections.VersionInfo
 |      Return a new VersionInfo object replacing specified fields with new values
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  _make(iterable) from collections.VersionInfo
 |      Make a new VersionInfo object from a sequence or iterable
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(_cls, major, minor, patch) from namedtuple_VersionInfo.VersionInfo
 |      Create new instance of VersionInfo(major, minor, patch)
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  major
 |      Alias for field number 0
 |
 |  minor
 |      Alias for field number 1
 |
 |  patch
 |      Alias for field number 2
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __match_args__ = ('major', 'minor', 'patch')
 |
 |  _field_defaults = {}
 |
 |  _fields = ('major', 'minor', 'patch')
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from builtins.tuple:
 |
 |  __add__(self, value, /)
 |      Return self+value.
 |
 |  __contains__(self, key, /)
 |      Return bool(key in self).
 |
 |  __eq__(self, value, /)
 |      Return self==value.
 |
 |  __ge__(self, value, /)
 |      Return self>=value.
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __getitem__(self, key, /)
 |      Return self[key].
 |
 |  __gt__(self, value, /)
 |      Return self>value.
 |
 |  __hash__(self, /)
 |      Return hash(self).
 |
 |  __iter__(self, /)
 |      Implement iter(self).
 |
 |  __le__(self, value, /)
 |      Return self<=value.
 |
 |  __len__(self, /)
 |      Return len(self).
 |
 |  __lt__(self, value, /)
 |      Return self<value.
 |
 |  __mul__(self, value, /)
 |      Return self*value.
 |
 |  __ne__(self, value, /)
 |      Return self!=value.
 |
 |  __rmul__(self, value, /)
 |      Return value*self.
 |
 |  count(self, value, /)
 |      Return number of occurrences of value.
 |
 |  index(self, value, start=0, stop=9223372036854775807, /)
 |      Return first index of value.
 |
 |      Raises ValueError if the value is not present.
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from builtins.tuple:
 |
 |  __class_getitem__(...)
 |      See PEP 585

Python Library Documentation: cython_function_or_method in module pyarrow.lib

cpu_count()
    cpu_count()

    Return the number of threads to use in parallel operations.

    The number of threads is determined at startup by inspecting the
    ``OMP_NUM_THREADS`` and ``OMP_THREAD_LIMIT`` environment variables.
    If neither is present, it will default to the number of hardware threads
    on the system. It can be modified at runtime by calling
    :func:`set_cpu_count()`.

    See Also
    --------
    set_cpu_count : Modify the size of this pool.
    io_thread_count : The analogous function for the I/O thread pool.

Python Library Documentation: function create_library_symlinks in module pyarrow

create_library_symlinks()
    With Linux and macOS wheels, the bundled shared libraries have an embedded
    ABI version like libarrow.so.17 or libarrow.17.dylib and so linking to them
    with -larrow won't work unless we create symlinks at locations like
    site-packages/pyarrow/libarrow.so. This unfortunate workaround addresses
    prior problems we had with shipping two copies of the shared libraries to
    permit third party projects like turbodbc to build their C++ extensions
    against the pyarrow wheels.

    This function must only be invoked once and only when the shared libraries
    are bundled with the Python package, which should only apply to wheel-based
    installs. It requires write access to the site-packages/pyarrow directory
    and so depending on your system may need to be run with root.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

create_memory_map(path, size)
    create_memory_map(path, size)

    Create a file of the given size and memory-map it.

    Parameters
    ----------
    path : str
        The file path to create, on the local filesystem.
    size : int
        The file size to create.

    Returns
    -------
    mmap : MemoryMappedFile

    Examples
    --------
    Create a file with a memory map:

    >>> import pyarrow as pa
    >>> with pa.create_memory_map('example_mmap_create.dat', 27) as mmap:
    ...     mmap.write(b'Create a memory-mapped file')
    ...     mmap.read_at(10, 9)
    ...
    27
    b'memory-map'

Python Library Documentation: cython_function_or_method in module pyarrow.lib

date32()
    date32()

    Create instance of 32-bit date (days since UNIX epoch 1970-01-01).

    Examples
    --------
    Create an instance of 32-bit date type:

    >>> import pyarrow as pa
    >>> pa.date32()
    DataType(date32[day])

    Create a scalar with 32-bit date type:

    >>> from datetime import date
    >>> pa.scalar(date(2012, 1, 1), type=pa.date32())
    <pyarrow.Date32Scalar: datetime.date(2012, 1, 1)>

Python Library Documentation: cython_function_or_method in module pyarrow.lib

date64()
    date64()

    Create instance of 64-bit date (milliseconds since UNIX epoch 1970-01-01).

    Examples
    --------
    Create an instance of 64-bit date type:

    >>> import pyarrow as pa
    >>> pa.date64()
    DataType(date64[ms])

    Create a scalar with 64-bit date type:

    >>> from datetime import datetime
    >>> pa.scalar(datetime(2012, 1, 1), type=pa.date64())
    <pyarrow.Date64Scalar: datetime.date(2012, 1, 1)>

Python Library Documentation: cython_function_or_method in module pyarrow.lib

decimal128(precision, scale=0)
    decimal128(int precision, int scale=0) -> DataType

    Create decimal type with precision and scale and 128-bit width.

    Arrow decimals are fixed-point decimal numbers encoded as a scaled
    integer.  The precision is the number of significant digits that the
    decimal type can represent; the scale is the number of digits after
    the decimal point (note the scale can be negative).

    As an example, ``decimal128(7, 3)`` can exactly represent the numbers
    1234.567 and -1234.567 (encoded internally as the 128-bit integers
    1234567 and -1234567, respectively), but neither 12345.67 nor 123.4567.

    ``decimal128(5, -3)`` can exactly represent the number 12345000
    (encoded internally as the 128-bit integer 12345), but neither
    123450000 nor 1234500.

    If you need a precision higher than 38 significant digits, consider
    using ``decimal256``.

    Parameters
    ----------
    precision : int
        Must be between 1 and 38
    scale : int

    Returns
    -------
    decimal_type : Decimal128Type

    Examples
    --------
    Create an instance of decimal type:

    >>> import pyarrow as pa
    >>> pa.decimal128(5, 2)
    Decimal128Type(decimal128(5, 2))

    Create an array with decimal type:

    >>> import decimal
    >>> a = decimal.Decimal('123.45')
    >>> pa.array([a], pa.decimal128(5, 2))
    <pyarrow.lib.Decimal128Array object at ...>
    [
      123.45
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

decimal256(precision, scale=0)
    decimal256(int precision, int scale=0) -> DataType

    Create decimal type with precision and scale and 256-bit width.

    Arrow decimals are fixed-point decimal numbers encoded as a scaled
    integer.  The precision is the number of significant digits that the
    decimal type can represent; the scale is the number of digits after
    the decimal point (note the scale can be negative).

    For most use cases, the maximum precision offered by ``decimal128``
    is sufficient, and it will result in a more compact and more efficient
    encoding.  ``decimal256`` is useful if you need a precision higher
    than 38 significant digits.

    Parameters
    ----------
    precision : int
        Must be between 1 and 76
    scale : int

    Returns
    -------
    decimal_type : Decimal256Type

Python Library Documentation: cython_function_or_method in module pyarrow.lib

decimal32(precision, scale=0)
    decimal32(int precision, int scale=0) -> DataType

    Create decimal type with precision and scale and 32-bit width.

    Arrow decimals are fixed-point decimal numbers encoded as a scaled
    integer.  The precision is the number of significant digits that the
    decimal type can represent; the scale is the number of digits after
    the decimal point (note the scale can be negative).

    As an example, ``decimal32(7, 3)`` can exactly represent the numbers
    1234.567 and -1234.567 (encoded internally as the 32-bit integers
    1234567 and -1234567, respectively), but neither 12345.67 nor 123.4567.

    ``decimal32(5, -3)`` can exactly represent the number 12345000
    (encoded internally as the 32-bit integer 12345), but neither
    123450000 nor 1234500.

    If you need a precision higher than 9 significant digits, consider
    using ``decimal64``, ``decimal128``, or ``decimal256``.

    Parameters
    ----------
    precision : int
        Must be between 1 and 9
    scale : int

    Returns
    -------
    decimal_type : Decimal32Type

    Examples
    --------
    Create an instance of decimal type:

    >>> import pyarrow as pa
    >>> pa.decimal32(5, 2)
    Decimal32Type(decimal32(5, 2))

    Create an array with decimal type:

    >>> import decimal
    >>> a = decimal.Decimal('123.45')
    >>> pa.array([a], pa.decimal32(5, 2))
    <pyarrow.lib.Decimal32Array object at ...>
    [
      123.45
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

decimal64(precision, scale=0)
    decimal64(int precision, int scale=0) -> DataType

    Create decimal type with precision and scale and 64-bit width.

    Arrow decimals are fixed-point decimal numbers encoded as a scaled
    integer.  The precision is the number of significant digits that the
    decimal type can represent; the scale is the number of digits after
    the decimal point (note the scale can be negative).

    As an example, ``decimal64(7, 3)`` can exactly represent the numbers
    1234.567 and -1234.567 (encoded internally as the 64-bit integers
    1234567 and -1234567, respectively), but neither 12345.67 nor 123.4567.

    ``decimal64(5, -3)`` can exactly represent the number 12345000
    (encoded internally as the 64-bit integer 12345), but neither
    123450000 nor 1234500.

    If you need a precision higher than 18 significant digits, consider
    using ``decimal128``, or ``decimal256``.

    Parameters
    ----------
    precision : int
        Must be between 1 and 18
    scale : int

    Returns
    -------
    decimal_type : Decimal64Type

    Examples
    --------
    Create an instance of decimal type:

    >>> import pyarrow as pa
    >>> pa.decimal64(5, 2)
    Decimal64Type(decimal64(5, 2))

    Create an array with decimal type:

    >>> import decimal
    >>> a = decimal.Decimal('123.45')
    >>> pa.array([a], pa.decimal64(5, 2))
    <pyarrow.lib.Decimal64Array object at ...>
    [
      123.45
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

decompress(buf, decompressed_size=None, codec='lz4', asbytes=False, memory_pool=None)
    decompress(buf, decompressed_size=None, codec='lz4', asbytes=False, memory_pool=None)

    Decompress data from buffer-like object.

    Parameters
    ----------
    buf : pyarrow.Buffer, bytes, or memoryview-compatible object
        Input object to decompress data from.
    decompressed_size : int, default None
        Size of the decompressed result
    codec : str, default 'lz4'
        Compression codec.
        Supported types: {'brotli, 'gzip', 'lz4', 'lz4_raw', 'snappy', 'zstd'}
    asbytes : bool, default False
        Return result as Python bytes object, otherwise Buffer.
    memory_pool : MemoryPool, default None
        Memory pool to use for buffer allocations, if any.

    Returns
    -------
    uncompressed : pyarrow.Buffer or bytes (if asbytes=True)

Python Library Documentation: cython_function_or_method in module pyarrow.lib

default_cpu_memory_manager()
    default_cpu_memory_manager()

    Return the default CPU MemoryManager instance.

    The returned singleton instance uses the default MemoryPool.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

default_memory_pool()
    default_memory_pool()

    Return the process-global memory pool.

    Examples
    --------
    >>> default_memory_pool()
    <pyarrow.MemoryPool backend_name=... bytes_allocated=0 max_memory=...>

Python Library Documentation: cython_function_or_method in module pyarrow.lib

dense_union(child_fields, type_codes=None)
    dense_union(child_fields, type_codes=None)

    Create DenseUnionType from child fields.

    A dense union is a nested type where each logical value is taken from
    a single child, at a specific offset.  A buffer of 8-bit type ids
    indicates which child a given logical value is to be taken from,
    and a buffer of 32-bit offsets indicates at which physical position
    in the given child array the logical value is to be taken from.

    Unlike a sparse union, a dense union allows encoding only the child array
    values which are actually referred to by the union array.  This is
    counterbalanced by the additional footprint of the offsets buffer, and
    the additional indirection cost when looking up values.

    Parameters
    ----------
    child_fields : sequence of Field values
        Each field must have a UTF8-encoded name, and these field names are
        part of the type metadata.
    type_codes : list of integers, default None

    Returns
    -------
    type : DenseUnionType

Python Library Documentation: function deserialize_pandas in module pyarrow.ipc

deserialize_pandas(buf, *, use_threads=True)
    Deserialize a buffer protocol compatible object into a pandas DataFrame.

    Parameters
    ----------
    buf : buffer
        An object compatible with the buffer protocol.
    use_threads : bool, default True
        Whether to parallelize the conversion using multiple threads.

    Returns
    -------
    df : pandas.DataFrame
        The buffer deserialized as pandas DataFrame

Python Library Documentation: cython_function_or_method in module pyarrow.lib

dictionary(index_type, value_type, ordered=False)
    dictionary(index_type, value_type, bool ordered=False) -> DictionaryType

    Dictionary (categorical, or simply encoded) type.

    Parameters
    ----------
    index_type : DataType
    value_type : DataType
    ordered : bool

    Returns
    -------
    type : DictionaryType

    Examples
    --------
    Create an instance of dictionary type:

    >>> import pyarrow as pa
    >>> pa.dictionary(pa.int64(), pa.utf8())
    DictionaryType(dictionary<values=string, indices=int64, ordered=0>)

    Use dictionary type to create an array:

    >>> pa.array(["a", "b", None, "d"], pa.dictionary(pa.int64(), pa.utf8()))
    <pyarrow.lib.DictionaryArray object at ...>
    ...
    -- dictionary:
      [
        "a",
        "b",
        "d"
      ]
    -- indices:
      [
        0,
        1,
        null,
        2
      ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

duration(unit)
    duration(unit)

    Create instance of a duration type with unit resolution.

    Parameters
    ----------
    unit : str
        One of 's' [second], 'ms' [millisecond], 'us' [microsecond], or
        'ns' [nanosecond].

    Returns
    -------
    type : pyarrow.DurationType

    Examples
    --------
    Create an instance of duration type:

    >>> import pyarrow as pa
    >>> pa.duration('us')
    DurationType(duration[us])
    >>> pa.duration('s')
    DurationType(duration[s])

    Create an array with duration type:

    >>> pa.array([0, 1, 2], type=pa.duration('s'))
    <pyarrow.lib.DurationArray object at ...>
    [
      0,
      1,
      2
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

enable_signal_handlers(enable)
    enable_signal_handlers(bool enable)

    Enable or disable interruption of long-running operations.

    By default, certain long running operations will detect user
    interruptions, such as by pressing Ctrl-C.  This detection relies
    on setting a signal handler for the duration of the long-running
    operation, and may therefore interfere with other frameworks or
    libraries (such as an event loop).

    Parameters
    ----------
    enable : bool
        Whether to enable user interruption by setting a temporary
        signal handler.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

field(name, type=None, nullable=None, metadata=None)
    field(name, type=None, nullable=None, metadata=None)

    Create a pyarrow.Field instance.

    Parameters
    ----------
    name : str or bytes
        Name of the field.
        Alternatively, you can also pass an object that implements the Arrow
        PyCapsule Protocol for schemas (has an ``__arrow_c_schema__`` method).
    type : pyarrow.DataType or str
        Arrow datatype of the field or a string matching one.
    nullable : bool, default True
        Whether the field's values are nullable.
    metadata : dict, default None
        Optional field metadata, the keys and values must be coercible to
        bytes.

    Returns
    -------
    field : pyarrow.Field

    Examples
    --------
    Create an instance of pyarrow.Field:

    >>> import pyarrow as pa
    >>> pa.field('key', pa.int32())
    pyarrow.Field<key: int32>
    >>> pa.field('key', pa.int32(), nullable=False)
    pyarrow.Field<key: int32 not null>

    >>> field = pa.field('key', pa.int32(),
    ...                  metadata={"key": "Something important"})
    >>> field
    pyarrow.Field<key: int32>
    >>> field.metadata
    {b'key': b'Something important'}

    Use the field to create a struct type:

    >>> pa.struct([field])
    StructType(struct<key: int32>)

    A str can also be passed for the type parameter:

    >>> pa.field('key', 'int32')
    pyarrow.Field<key: int32>

Python Library Documentation: cython_function_or_method in module pyarrow.lib

fixed_shape_tensor(value_type, shape, dim_names=None, permutation=None)
    fixed_shape_tensor(DataType value_type, shape, dim_names=None, permutation=None)

    Create instance of fixed shape tensor extension type with shape and optional
    names of tensor dimensions and indices of the desired logical
    ordering of dimensions.

    Parameters
    ----------
    value_type : DataType
        Data type of individual tensor elements.
    shape : tuple or list of integers
        The physical shape of the contained tensors.
    dim_names : tuple or list of strings, default None
        Explicit names to tensor dimensions.
    permutation : tuple or list integers, default None
        Indices of the desired ordering of the original dimensions.
        The indices contain a permutation of the values ``[0, 1, .., N-1]`` where
        N is the number of dimensions. The permutation indicates which dimension
        of the logical layout corresponds to which dimension of the physical tensor.
        For more information on this parameter see
        :ref:`fixed_shape_tensor_extension`.

    Examples
    --------
    Create an instance of fixed shape tensor extension type:

    >>> import pyarrow as pa
    >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), [2, 2])
    >>> tensor_type
    FixedShapeTensorType(extension<arrow.fixed_shape_tensor[value_type=int32, shape=[2,2]]>)

    Inspect the data type:

    >>> tensor_type.value_type
    DataType(int32)
    >>> tensor_type.shape
    [2, 2]

    Create a table with fixed shape tensor extension array:

    >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]]
    >>> storage = pa.array(arr, pa.list_(pa.int32(), 4))
    >>> tensor = pa.ExtensionArray.from_storage(tensor_type, storage)
    >>> pa.table([tensor], names=["tensor_array"])
    pyarrow.Table
    tensor_array: extension<arrow.fixed_shape_tensor[value_type=int32, shape=[2,2]]>
    ----
    tensor_array: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]

    Create an instance of fixed shape tensor extension type with names
    of tensor dimensions:

    >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3),
    ...                                     dim_names=['C', 'H', 'W'])
    >>> tensor_type.dim_names
    ['C', 'H', 'W']

    Create an instance of fixed shape tensor extension type with
    permutation:

    >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3),
    ...                                     permutation=[0, 2, 1])
    >>> tensor_type.permutation
    [0, 2, 1]

    Returns
    -------
    type : FixedShapeTensorType

Python Library Documentation: cython_function_or_method in module pyarrow.lib

float16()
    float16()

    Create half-precision floating point type.

    Examples
    --------
    Create an instance of float16 type:

    >>> import pyarrow as pa
    >>> pa.float16()
    DataType(halffloat)
    >>> print(pa.float16())
    halffloat

    Create an array with float16 type:

    >>> arr = np.array([1.5, np.nan], dtype=np.float16)
    >>> a = pa.array(arr, type=pa.float16())
    >>> a
    <pyarrow.lib.HalfFloatArray object at ...>
    [
      1.5,
      nan
    ]

    Note that unlike other float types, if you convert this array
    to a python list, the types of its elements will be ``np.float16``

    >>> [type(val) for val in a.to_pylist()]
    [<class 'float'>, <class 'float'>]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

float32()
    float32()

    Create single-precision floating point type.

    Examples
    --------
    Create an instance of float32 type:

    >>> import pyarrow as pa
    >>> pa.float32()
    DataType(float)
    >>> print(pa.float32())
    float

    Create an array with float32 type:

    >>> pa.array([0.0, 1.0, 2.0], type=pa.float32())
    <pyarrow.lib.FloatArray object at ...>
    [
      0,
      1,
      2
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

float64()
    float64()

    Create double-precision floating point type.

    Examples
    --------
    Create an instance of float64 type:

    >>> import pyarrow as pa
    >>> pa.float64()
    DataType(double)
    >>> print(pa.float64())
    double

    Create an array with float64 type:

    >>> pa.array([0.0, 1.0, 2.0], type=pa.float64())
    <pyarrow.lib.DoubleArray object at ...>
    [
      0,
      1,
      2
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

foreign_buffer(address, size, base=None)
    foreign_buffer(address, size, base=None)

    Construct an Arrow buffer with the given *address* and *size*.

    The buffer will be optionally backed by the Python *base* object, if given.
    The *base* object will be kept alive as long as this buffer is alive,
    including across language boundaries (for example if the buffer is
    referenced by C++ code).

    Parameters
    ----------
    address : int
        The starting address of the buffer. The address can
        refer to both device or host memory but it must be
        accessible from device after mapping it with
        `get_device_address` method.
    size : int
        The size of device buffer in bytes.
    base : {None, object}
        Object that owns the referenced memory.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

from_numpy_dtype(dtype)
    from_numpy_dtype(dtype)

    Convert NumPy dtype to pyarrow.DataType.

    Parameters
    ----------
    dtype : the numpy dtype to convert


    Examples
    --------
    Create a pyarrow DataType from NumPy dtype:

    >>> import pyarrow as pa
    >>> import numpy as np
    >>> pa.from_numpy_dtype(np.dtype('float16'))
    DataType(halffloat)
    >>> pa.from_numpy_dtype('U')
    DataType(string)
    >>> pa.from_numpy_dtype(bool)
    DataType(bool)
    >>> pa.from_numpy_dtype(np.str_)
    DataType(string)

Python Library Documentation: function get_include in module pyarrow

get_include()
    Return absolute path to directory containing Arrow C++ include
    headers. Similar to numpy.get_include

Python Library Documentation: function get_libraries in module pyarrow

get_libraries()
    Return list of library names to include in the `libraries` argument for C
    or Cython extensions using pyarrow

Python Library Documentation: function get_library_dirs in module pyarrow

get_library_dirs()
    Return lists of directories likely to contain Arrow C++ libraries for
    linking C or Cython extensions using pyarrow

Python Library Documentation: cython_function_or_method in module pyarrow.lib

have_libhdfs()
    have_libhdfs()

    Return true if HDFS (HadoopFileSystem) library is set up correctly.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

infer_type(values, mask=None, from_pandas=False)
    infer_type(values, mask=None, from_pandas=False)

    Attempt to infer Arrow data type that can hold the passed Python
    sequence type in an Array object

    Parameters
    ----------
    values : array-like
        Sequence to infer type from.
    mask : ndarray (bool type), optional
        Optional exclusion mask where True marks null, False non-null.
    from_pandas : bool, default False
        Use pandas's NA/null sentinel values for type inference.

    Returns
    -------
    type : DataType

Python Library Documentation: cython_function_or_method in module pyarrow.lib

input_stream(source, compression='detect', buffer_size=None)
    input_stream(source, compression='detect', buffer_size=None)

    Create an Arrow input stream.

    Parameters
    ----------
    source : str, Path, buffer, or file-like object
        The source to open for reading.
    compression : str optional, default 'detect'
        The compression algorithm to use for on-the-fly decompression.
        If "detect" and source is a file path, then compression will be
        chosen based on the file extension.
        If None, no compression will be applied.
        Otherwise, a well-known algorithm name must be supplied (e.g. "gzip").
    buffer_size : int, default None
        If None or 0, no buffering will happen. Otherwise the size of the
        temporary read buffer.

    Examples
    --------
    Create a readable BufferReader (NativeFile) from a Buffer or a memoryview object:

    >>> import pyarrow as pa
    >>> buf = memoryview(b"some data")
    >>> with pa.input_stream(buf) as stream:
    ...     stream.read(4)
    ...
    b'some'

    Create a readable OSFile (NativeFile) from a string or file path:

    >>> import gzip
    >>> with gzip.open('example.gz', 'wb') as f:
    ...     f.write(b'some data')
    ...
    9
    >>> with pa.input_stream('example.gz') as stream:
    ...     stream.read()
    ...
    b'some data'

    Create a readable PythonFile (NativeFile) from a a Python file object:

    >>> with open('example.txt', mode='w') as f:
    ...     f.write('some text')
    ...
    9
    >>> with pa.input_stream('example.txt') as stream:
    ...     stream.read(6)
    ...
    b'some t'

Python Library Documentation: cython_function_or_method in module pyarrow.lib

int16()
    int16()

    Create instance of signed int16 type.

    Examples
    --------
    Create an instance of int16 type:

    >>> import pyarrow as pa
    >>> pa.int16()
    DataType(int16)
    >>> print(pa.int16())
    int16

    Create an array with int16 type:

    >>> pa.array([0, 1, 2], type=pa.int16())
    <pyarrow.lib.Int16Array object at ...>
    [
      0,
      1,
      2
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

int32()
    int32()

    Create instance of signed int32 type.

    Examples
    --------
    Create an instance of int32 type:

    >>> import pyarrow as pa
    >>> pa.int32()
    DataType(int32)
    >>> print(pa.int32())
    int32

    Create an array with int32 type:

    >>> pa.array([0, 1, 2], type=pa.int32())
    <pyarrow.lib.Int32Array object at ...>
    [
      0,
      1,
      2
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

int64()
    int64()

    Create instance of signed int64 type.

    Examples
    --------
    Create an instance of int64 type:

    >>> import pyarrow as pa
    >>> pa.int64()
    DataType(int64)
    >>> print(pa.int64())
    int64

    Create an array with int64 type:

    >>> pa.array([0, 1, 2], type=pa.int64())
    <pyarrow.lib.Int64Array object at ...>
    [
      0,
      1,
      2
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

int8()
    int8()

    Create instance of signed int8 type.

    Examples
    --------
    Create an instance of int8 type:

    >>> import pyarrow as pa
    >>> pa.int8()
    DataType(int8)
    >>> print(pa.int8())
    int8

    Create an array with int8 type:

    >>> pa.array([0, 1, 2], type=pa.int8())
    <pyarrow.lib.Int8Array object at ...>
    [
      0,
      1,
      2
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

io_thread_count()
    io_thread_count()

    Return the number of threads to use for I/O operations.

    Many operations, such as scanning a dataset, will implicitly make
    use of this pool. The number of threads is set to a fixed value at
    startup. It can be modified at runtime by calling
    :func:`set_io_thread_count()`.

    See Also
    --------
    set_io_thread_count : Modify the size of this pool.
    cpu_count : The analogous function for the CPU thread pool.

Python Library Documentation: module pyarrow.ipc in pyarrow

NAME
    pyarrow.ipc

DESCRIPTION
    # Licensed to the Apache Software Foundation (ASF) under one
    # or more contributor license agreements.  See the NOTICE file
    # distributed with this work for additional information
    # regarding copyright ownership.  The ASF licenses this file
    # to you under the Apache License, Version 2.0 (the
    # "License"); you may not use this file except in compliance
    # with the License.  You may obtain a copy of the License at
    #
    #   http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing,
    # software distributed under the License is distributed on an
    # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    # KIND, either express or implied.  See the License for the
    # specific language governing permissions and limitations
    # under the License.

CLASSES
    pyarrow.lib._RecordBatchFileReader(pyarrow.lib._Weakrefable)
        RecordBatchFileReader
    pyarrow.lib._RecordBatchFileWriter(pyarrow.lib._RecordBatchStreamWriter)
        RecordBatchFileWriter
    pyarrow.lib._RecordBatchStreamReader(pyarrow.lib.RecordBatchReader)
        RecordBatchStreamReader
    pyarrow.lib._RecordBatchStreamWriter(pyarrow.lib._CRecordBatchWriter)
        RecordBatchStreamWriter

    class RecordBatchFileReader(pyarrow.lib._RecordBatchFileReader)
     |  RecordBatchFileReader(source, footer_offset=None, *, options=None, memory_pool=None)
     |
     |  Class for reading Arrow record batch data from the Arrow binary file format
     |
     |  Parameters
     |  ----------
     |  source : bytes/buffer-like, pyarrow.NativeFile, or file-like Python object
     |      Either an in-memory buffer, or a readable file object.
     |      If you want to use memory map use MemoryMappedFile as source.
     |  footer_offset : int, default None
     |      If the file is embedded in some larger file, this is the byte offset to
     |      the very end of the file data
     |  options : pyarrow.ipc.IpcReadOptions
     |      Options for IPC serialization.
     |      If None, default values will be used.
     |  memory_pool : MemoryPool, default None
     |      If None, default memory pool is used.
     |
     |  Method resolution order:
     |      RecordBatchFileReader
     |      pyarrow.lib._RecordBatchFileReader
     |      pyarrow.lib._Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, source, footer_offset=None, *, options=None, memory_pool=None)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  __dict__
     |      dictionary for instance variables
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from pyarrow.lib._RecordBatchFileReader:
     |
     |  __enter__(self)
     |      _RecordBatchFileReader.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, traceback)
     |      _RecordBatchFileReader.__exit__(self, exc_type, exc_value, traceback)
     |
     |  __reduce__ = __reduce_cython__(...)
     |      _RecordBatchFileReader.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _RecordBatchFileReader.__setstate_cython__(self, __pyx_state)
     |
     |  get_batch(self, i)
     |      _RecordBatchFileReader.get_batch(self, int i)
     |
     |      Read the record batch with the given index.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          The index of the record batch in the IPC file.
     |
     |      Returns
     |      -------
     |      batch : RecordBatch
     |
     |  get_batch_with_custom_metadata(self, i)
     |      _RecordBatchFileReader.get_batch_with_custom_metadata(self, int i)
     |
     |      Read the record batch with the given index along with
     |      its custom metadata
     |
     |      Parameters
     |      ----------
     |      i : int
     |          The index of the record batch in the IPC file.
     |
     |      Returns
     |      -------
     |      batch : RecordBatch
     |      custom_metadata : KeyValueMetadata
     |
     |  get_record_batch = get_batch(self, i)
     |
     |  read_all(self)
     |      _RecordBatchFileReader.read_all(self)
     |
     |      Read all record batches as a pyarrow.Table
     |
     |  read_pandas(self, **options)
     |      _ReadPandasMixin.read_pandas(self, **options)
     |
     |      Read contents of stream to a pandas.DataFrame.
     |
     |      Read all record batches as a pyarrow.Table then convert it to a
     |      pandas.DataFrame using Table.to_pandas.
     |
     |      Parameters
     |      ----------
     |      **options
     |          Arguments to forward to :meth:`Table.to_pandas`.
     |
     |      Returns
     |      -------
     |      df : pandas.DataFrame
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from pyarrow.lib._RecordBatchFileReader:
     |
     |  __new__(*args, **kwargs) class method of pyarrow.lib._RecordBatchFileReader
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from pyarrow.lib._RecordBatchFileReader:
     |
     |  metadata
     |      File-level custom metadata as dict, where both keys and values are byte-like.
     |      This kind of metadata can be written via ``ipc.new_file(..., metadata=...)``.
     |
     |  num_record_batches
     |      The number of record batches in the IPC file.
     |
     |  schema
     |
     |  stats
     |      Current IPC read statistics.

    class RecordBatchFileWriter(pyarrow.lib._RecordBatchFileWriter)
     |  RecordBatchFileWriter(sink, schema, *, options=None, metadata=None)
     |
     |  Writer to create the Arrow binary file format
     |
     |  Parameters
     |  ----------
     |  sink : str, pyarrow.NativeFile, or file-like Python object
     |      Either a file path, or a writable file object.
     |  schema : pyarrow.Schema
     |      The Arrow schema for data to be written to the file.
     |  options : pyarrow.ipc.IpcWriteOptions
     |      Options for IPC serialization.
     |
     |      If None, default values will be used: the legacy format will not
     |      be used unless overridden by setting the environment variable
     |      ARROW_PRE_0_15_IPC_FORMAT=1, and the V5 metadata version will be
     |      used unless overridden by setting the environment variable
     |      ARROW_PRE_1_0_METADATA_VERSION=1.
     |  metadata : dict | pyarrow.KeyValueMetadata, optional
     |      Key/value pairs (both must be bytes-like) that will be stored
     |      in the file footer and are retrievable via
     |      pyarrow.ipc.open_file(...).metadata.
     |
     |  Method resolution order:
     |      RecordBatchFileWriter
     |      pyarrow.lib._RecordBatchFileWriter
     |      pyarrow.lib._RecordBatchStreamWriter
     |      pyarrow.lib._CRecordBatchWriter
     |      pyarrow.lib._Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, sink, schema, *, options=None, metadata=None)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  __dict__
     |      dictionary for instance variables
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from pyarrow.lib._RecordBatchFileWriter:
     |
     |  __reduce__ = __reduce_cython__(...)
     |      _RecordBatchFileWriter.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _RecordBatchFileWriter.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from pyarrow.lib._RecordBatchFileWriter:
     |
     |  __new__(*args, **kwargs) class method of pyarrow.lib._RecordBatchFileWriter
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from pyarrow.lib._CRecordBatchWriter:
     |
     |  __enter__(self)
     |      _CRecordBatchWriter.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_val, exc_tb)
     |      _CRecordBatchWriter.__exit__(self, exc_type, exc_val, exc_tb)
     |
     |  close(self)
     |      _CRecordBatchWriter.close(self)
     |
     |      Close stream and write end-of-stream 0 marker.
     |
     |  write(self, table_or_batch)
     |      _CRecordBatchWriter.write(self, table_or_batch)
     |
     |      Write RecordBatch or Table to stream.
     |
     |      Parameters
     |      ----------
     |      table_or_batch : {RecordBatch, Table}
     |
     |  write_batch(self, batch, custom_metadata=None)
     |      _CRecordBatchWriter.write_batch(self, RecordBatch batch, custom_metadata=None)
     |
     |      Write RecordBatch to stream.
     |
     |      Parameters
     |      ----------
     |      batch : RecordBatch
     |      custom_metadata : mapping or KeyValueMetadata
     |          Keys and values must be string-like / coercible to bytes
     |
     |  write_table(self, table, max_chunksize=None)
     |      _CRecordBatchWriter.write_table(self, Table table, max_chunksize=None)
     |
     |      Write Table to stream in (contiguous) RecordBatch objects.
     |
     |      Parameters
     |      ----------
     |      table : Table
     |      max_chunksize : int, default None
     |          Maximum number of rows for RecordBatch chunks. Individual chunks may
     |          be smaller depending on the chunk layout of individual columns.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from pyarrow.lib._CRecordBatchWriter:
     |
     |  stats
     |      Current IPC write statistics.

    class RecordBatchStreamReader(pyarrow.lib._RecordBatchStreamReader)
     |  RecordBatchStreamReader(source, *, options=None, memory_pool=None)
     |
     |  Reader for the Arrow streaming binary format.
     |
     |  Parameters
     |  ----------
     |  source : bytes/buffer-like, pyarrow.NativeFile, or file-like Python object
     |      Either an in-memory buffer, or a readable file object.
     |      If you want to use memory map use MemoryMappedFile as source.
     |  options : pyarrow.ipc.IpcReadOptions
     |      Options for IPC deserialization.
     |      If None, default values will be used.
     |  memory_pool : MemoryPool, default None
     |      If None, default memory pool is used.
     |
     |  Method resolution order:
     |      RecordBatchStreamReader
     |      pyarrow.lib._RecordBatchStreamReader
     |      pyarrow.lib.RecordBatchReader
     |      pyarrow.lib._Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, source, *, options=None, memory_pool=None)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  __dict__
     |      dictionary for instance variables
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from pyarrow.lib._RecordBatchStreamReader:
     |
     |  __reduce__ = __reduce_cython__(...)
     |      _RecordBatchStreamReader.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _RecordBatchStreamReader.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from pyarrow.lib._RecordBatchStreamReader:
     |
     |  __new__(*args, **kwargs) class method of pyarrow.lib._RecordBatchStreamReader
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from pyarrow.lib._RecordBatchStreamReader:
     |
     |  stats
     |      Current IPC read statistics.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from pyarrow.lib.RecordBatchReader:
     |
     |  __arrow_c_stream__(self, requested_schema=None)
     |      RecordBatchReader.__arrow_c_stream__(self, requested_schema=None)
     |
     |      Export to a C ArrowArrayStream PyCapsule.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule, default None
     |          The schema to which the stream should be casted, passed as a
     |          PyCapsule containing a C ArrowSchema representation of the
     |          requested schema.
     |
     |      Returns
     |      -------
     |      PyCapsule
     |          A capsule containing a C ArrowArrayStream struct.
     |
     |  __enter__(self)
     |      RecordBatchReader.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_val, exc_tb)
     |      RecordBatchReader.__exit__(self, exc_type, exc_val, exc_tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  cast(self, target_schema)
     |      RecordBatchReader.cast(self, target_schema)
     |
     |      Wrap this reader with one that casts each batch lazily as it is pulled.
     |      Currently only a safe cast to target_schema is implemented.
     |
     |      Parameters
     |      ----------
     |      target_schema : Schema
     |          Schema to cast to, the names and order of fields must match.
     |
     |      Returns
     |      -------
     |      RecordBatchReader
     |
     |  close(self)
     |      RecordBatchReader.close(self)
     |
     |      Release any resources associated with the reader.
     |
     |  iter_batches_with_custom_metadata(self)
     |      RecordBatchReader.iter_batches_with_custom_metadata(self)
     |
     |      Iterate over record batches from the stream along with their custom
     |      metadata.
     |
     |      Yields
     |      ------
     |      RecordBatchWithMetadata
     |
     |  read_all(self)
     |      RecordBatchReader.read_all(self)
     |
     |      Read all record batches as a pyarrow.Table.
     |
     |      Returns
     |      -------
     |      Table
     |
     |  read_next_batch(self)
     |      RecordBatchReader.read_next_batch(self)
     |
     |      Read next RecordBatch from the stream.
     |
     |      Raises
     |      ------
     |      StopIteration:
     |          At end of stream.
     |
     |      Returns
     |      -------
     |      RecordBatch
     |
     |  read_next_batch_with_custom_metadata(self)
     |      RecordBatchReader.read_next_batch_with_custom_metadata(self)
     |
     |      Read next RecordBatch from the stream along with its custom metadata.
     |
     |      Raises
     |      ------
     |      StopIteration:
     |          At end of stream.
     |
     |      Returns
     |      -------
     |      batch : RecordBatch
     |      custom_metadata : KeyValueMetadata
     |
     |  read_pandas(self, **options)
     |      _ReadPandasMixin.read_pandas(self, **options)
     |
     |      Read contents of stream to a pandas.DataFrame.
     |
     |      Read all record batches as a pyarrow.Table then convert it to a
     |      pandas.DataFrame using Table.to_pandas.
     |
     |      Parameters
     |      ----------
     |      **options
     |          Arguments to forward to :meth:`Table.to_pandas`.
     |
     |      Returns
     |      -------
     |      df : pandas.DataFrame
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from pyarrow.lib.RecordBatchReader:
     |
     |  from_batches(schema, batches)
     |      RecordBatchReader.from_batches(Schema schema, batches)
     |
     |      Create RecordBatchReader from an iterable of batches.
     |
     |      Parameters
     |      ----------
     |      schema : Schema
     |          The shared schema of the record batches
     |      batches : Iterable[RecordBatch]
     |          The batches that this reader will return.
     |
     |      Returns
     |      -------
     |      reader : RecordBatchReader
     |
     |  from_stream(data, schema=None)
     |      RecordBatchReader.from_stream(data, schema=None)
     |
     |      Create RecordBatchReader from a Arrow-compatible stream object.
     |
     |      This accepts objects implementing the Arrow PyCapsule Protocol for
     |      streams, i.e. objects that have a ``__arrow_c_stream__`` method.
     |
     |      Parameters
     |      ----------
     |      data : Arrow-compatible stream object
     |          Any object that implements the Arrow PyCapsule Protocol for
     |          streams.
     |      schema : Schema, default None
     |          The schema to which the stream should be casted, if supported
     |          by the stream object.
     |
     |      Returns
     |      -------
     |      RecordBatchReader
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from pyarrow.lib.RecordBatchReader:
     |
     |  schema
     |      Shared schema of the record batches in the stream.
     |
     |      Returns
     |      -------
     |      Schema

    class RecordBatchStreamWriter(pyarrow.lib._RecordBatchStreamWriter)
     |  RecordBatchStreamWriter(sink, schema, *, options=None)
     |
     |  Writer for the Arrow streaming binary format
     |
     |  Parameters
     |  ----------
     |  sink : str, pyarrow.NativeFile, or file-like Python object
     |      Either a file path, or a writable file object.
     |  schema : pyarrow.Schema
     |      The Arrow schema for data to be written to the file.
     |  options : pyarrow.ipc.IpcWriteOptions
     |      Options for IPC serialization.
     |
     |      If None, default values will be used: the legacy format will not
     |      be used unless overridden by setting the environment variable
     |      ARROW_PRE_0_15_IPC_FORMAT=1, and the V5 metadata version will be
     |      used unless overridden by setting the environment variable
     |      ARROW_PRE_1_0_METADATA_VERSION=1.
     |
     |  Method resolution order:
     |      RecordBatchStreamWriter
     |      pyarrow.lib._RecordBatchStreamWriter
     |      pyarrow.lib._CRecordBatchWriter
     |      pyarrow.lib._Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, sink, schema, *, options=None)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  __dict__
     |      dictionary for instance variables
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from pyarrow.lib._RecordBatchStreamWriter:
     |
     |  __reduce__ = __reduce_cython__(...)
     |      _RecordBatchStreamWriter.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _RecordBatchStreamWriter.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from pyarrow.lib._RecordBatchStreamWriter:
     |
     |  __new__(*args, **kwargs) class method of pyarrow.lib._RecordBatchStreamWriter
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from pyarrow.lib._CRecordBatchWriter:
     |
     |  __enter__(self)
     |      _CRecordBatchWriter.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_val, exc_tb)
     |      _CRecordBatchWriter.__exit__(self, exc_type, exc_val, exc_tb)
     |
     |  close(self)
     |      _CRecordBatchWriter.close(self)
     |
     |      Close stream and write end-of-stream 0 marker.
     |
     |  write(self, table_or_batch)
     |      _CRecordBatchWriter.write(self, table_or_batch)
     |
     |      Write RecordBatch or Table to stream.
     |
     |      Parameters
     |      ----------
     |      table_or_batch : {RecordBatch, Table}
     |
     |  write_batch(self, batch, custom_metadata=None)
     |      _CRecordBatchWriter.write_batch(self, RecordBatch batch, custom_metadata=None)
     |
     |      Write RecordBatch to stream.
     |
     |      Parameters
     |      ----------
     |      batch : RecordBatch
     |      custom_metadata : mapping or KeyValueMetadata
     |          Keys and values must be string-like / coercible to bytes
     |
     |  write_table(self, table, max_chunksize=None)
     |      _CRecordBatchWriter.write_table(self, Table table, max_chunksize=None)
     |
     |      Write Table to stream in (contiguous) RecordBatch objects.
     |
     |      Parameters
     |      ----------
     |      table : Table
     |      max_chunksize : int, default None
     |          Maximum number of rows for RecordBatch chunks. Individual chunks may
     |          be smaller depending on the chunk layout of individual columns.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from pyarrow.lib._CRecordBatchWriter:
     |
     |  stats
     |      Current IPC write statistics.

FUNCTIONS
    deserialize_pandas(buf, *, use_threads=True)
        Deserialize a buffer protocol compatible object into a pandas DataFrame.

        Parameters
        ----------
        buf : buffer
            An object compatible with the buffer protocol.
        use_threads : bool, default True
            Whether to parallelize the conversion using multiple threads.

        Returns
        -------
        df : pandas.DataFrame
            The buffer deserialized as pandas DataFrame

    new_file(sink, schema, *, options=None, metadata=None)
        Create an Arrow columnar IPC file writer instance

        Parameters
        ----------
        sink : str, pyarrow.NativeFile, or file-like Python object
            Either a file path, or a writable file object.
        schema : pyarrow.Schema
            The Arrow schema for data to be written to the file.
        options : pyarrow.ipc.IpcWriteOptions
            Options for IPC serialization.

            If None, default values will be used: the legacy format will not
            be used unless overridden by setting the environment variable
            ARROW_PRE_0_15_IPC_FORMAT=1, and the V5 metadata version will be
            used unless overridden by setting the environment variable
            ARROW_PRE_1_0_METADATA_VERSION=1.
        metadata : dict | pyarrow.KeyValueMetadata, optional
            Key/value pairs (both must be bytes-like) that will be stored
            in the file footer and are retrievable via
            pyarrow.ipc.open_file(...).metadata.

        Returns
        -------
        writer : RecordBatchFileWriter
            A writer for the given sink

    new_stream(sink, schema, *, options=None)
        Create an Arrow columnar IPC stream writer instance

        Parameters
        ----------
        sink : str, pyarrow.NativeFile, or file-like Python object
            Either a file path, or a writable file object.
        schema : pyarrow.Schema
            The Arrow schema for data to be written to the file.
        options : pyarrow.ipc.IpcWriteOptions
            Options for IPC serialization.

            If None, default values will be used: the legacy format will not
            be used unless overridden by setting the environment variable
            ARROW_PRE_0_15_IPC_FORMAT=1, and the V5 metadata version will be
            used unless overridden by setting the environment variable
            ARROW_PRE_1_0_METADATA_VERSION=1.

        Returns
        -------
        writer : RecordBatchStreamWriter
            A writer for the given sink

    open_file(source, footer_offset=None, *, options=None, memory_pool=None)
        Create reader for Arrow file format.

        Parameters
        ----------
        source : bytes/buffer-like, pyarrow.NativeFile, or file-like Python object
            Either an in-memory buffer, or a readable file object.
        footer_offset : int, default None
            If the file is embedded in some larger file, this is the byte offset to
            the very end of the file data.
        options : pyarrow.ipc.IpcReadOptions
            Options for IPC serialization.
            If None, default values will be used.
        memory_pool : MemoryPool, default None
            If None, default memory pool is used.

        Returns
        -------
        reader : RecordBatchFileReader
            A reader for the given source

    open_stream(source, *, options=None, memory_pool=None)
        Create reader for Arrow streaming format.

        Parameters
        ----------
        source : bytes/buffer-like, pyarrow.NativeFile, or file-like Python object
            Either an in-memory buffer, or a readable file object.
        options : pyarrow.ipc.IpcReadOptions
            Options for IPC serialization.
            If None, default values will be used.
        memory_pool : MemoryPool, default None
            If None, default memory pool is used.

        Returns
        -------
        reader : RecordBatchStreamReader
            A reader for the given source

    serialize_pandas(df, *, nthreads=None, preserve_index=None)
        Serialize a pandas DataFrame into a buffer protocol compatible object.

        Parameters
        ----------
        df : pandas.DataFrame
        nthreads : int, default None
            Number of threads to use for conversion to Arrow, default all CPUs.
        preserve_index : bool, default None
            The default of None will store the index as a column, except for
            RangeIndex which is stored as metadata only. If True, always
            preserve the pandas index data as a column. If False, no index
            information is saved and the result will have a default RangeIndex.

        Returns
        -------
        buf : buffer
            An object compatible with the buffer protocol.

FILE
    c:\users\youse\appdata\local\programs\python\python312\lib\site-packages\pyarrow\ipc.py

Python Library Documentation: cython_function_or_method in module pyarrow.lib

is_opentelemetry_enabled() -> 'bool'
    is_opentelemetry_enabled() -> bool

    Returns True if OpenTelemetry is enabled in libarrow.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

jemalloc_memory_pool()
    jemalloc_memory_pool()

    Return a memory pool based on the jemalloc heap.

    NotImplementedError is raised if jemalloc support is not enabled.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

jemalloc_set_decay_ms(decay_ms)
    jemalloc_set_decay_ms(decay_ms)

    Set arenas.dirty_decay_ms and arenas.muzzy_decay_ms to indicated number of
    milliseconds. A value of 0 (the default) results in dirty / muzzy memory
    pages being released right away to the OS, while a higher value will result
    in a time-based decay. See the jemalloc docs for more information

    It's best to set this at the start of your application.

    Parameters
    ----------
    decay_ms : int
        Number of milliseconds to set for jemalloc decay conf parameters. Note
        that this change will only affect future memory arenas

Python Library Documentation: cython_function_or_method in module pyarrow.lib

json_(storage_type=DataType(string))
    json_(DataType storage_type=utf8())

    Create instance of JSON extension type.

    Parameters
    ----------
    storage_type : DataType, default pyarrow.string()
        The underlying data type. Can be on of the following types:
        string, large_string, string_view.

    Returns
    -------
    type : JsonType

    Examples
    --------
    Create an instance of JSON extension type:

    >>> import pyarrow as pa
    >>> pa.json_(pa.utf8())
    JsonType(extension<arrow.json>)

    Use the JSON type to create an array:

    >>> pa.array(['{"a": 1}', '{"b": 2}'], type=pa.json_(pa.utf8()))
    <pyarrow.lib.JsonArray object at ...>
    [
      "{"a": 1}",
      "{"b": 2}"
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

large_binary()
    large_binary()

    Create large variable-length binary type.

    This data type may not be supported by all Arrow implementations.  Unless
    you need to represent data larger than 2GB, you should prefer binary().

    Examples
    --------
    Create an instance of large variable-length binary type:

    >>> import pyarrow as pa
    >>> pa.large_binary()
    DataType(large_binary)

    and use the type to create an array:

    >>> pa.array(['foo', 'bar', 'baz'], type=pa.large_binary())
    <pyarrow.lib.LargeBinaryArray object at ...>
    [
      666F6F,
      626172,
      62617A
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

large_list(value_type)
    large_list(value_type) -> LargeListType

    Create LargeListType instance from child data type or field.

    This data type may not be supported by all Arrow implementations.
    Unless you need to represent data larger than 2**31 elements, you should
    prefer list_().

    Parameters
    ----------
    value_type : DataType or Field

    Returns
    -------
    list_type : DataType

    Examples
    --------
    Create an instance of LargeListType:

    >>> import pyarrow as pa
    >>> pa.large_list(pa.int8())
    LargeListType(large_list<item: int8>)

    Use the LargeListType to create an array:

    >>> pa.array([[-1, 3]] * 5, type=pa.large_list(pa.int8()))
    <pyarrow.lib.LargeListArray object at ...>
    [
      [
        -1,
        3
      ],
      [
        -1,
        3
      ],
    ...

Python Library Documentation: cython_function_or_method in module pyarrow.lib

large_list_view(value_type)
    large_list_view(value_type) -> LargeListViewType

    Create LargeListViewType instance from child data type or field.

    This data type may not be supported by all Arrow implementations
    because it is an alternative to the ListType.

    Parameters
    ----------
    value_type : DataType or Field

    Returns
    -------
    list_view_type : DataType

    Examples
    --------
    Create an instance of LargeListViewType:

    >>> import pyarrow as pa
    >>> pa.large_list_view(pa.int8())
    LargeListViewType(large_list_view<item: int8>)

Python Library Documentation: cython_function_or_method in module pyarrow.lib

large_string()
    large_string()

    Create large UTF8 variable-length string type.

    This data type may not be supported by all Arrow implementations.  Unless
    you need to represent data larger than 2GB, you should prefer string().

    Examples
    --------
    Create an instance of large UTF8 variable-length binary type:

    >>> import pyarrow as pa
    >>> pa.large_string()
    DataType(large_string)

    and use the type to create an array:

    >>> pa.array(['foo', 'bar'] * 50, type=pa.large_string())
    <pyarrow.lib.LargeStringArray object at ...>
    [
      "foo",
      "bar",
      ...
      "foo",
      "bar"
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

large_utf8()
    large_utf8()

    Alias for large_string().

    Examples
    --------
    Create an instance of large UTF8 variable-length binary type:

    >>> import pyarrow as pa
    >>> pa.large_utf8()
    DataType(large_string)

    and use the type to create an array:

    >>> pa.array(['foo', 'bar'] * 50, type=pa.large_utf8())
    <pyarrow.lib.LargeStringArray object at ...>
    [
      "foo",
      "bar",
      ...
      "foo",
      "bar"
    ]

Python Library Documentation: module pyarrow.lib in pyarrow

NAME
    pyarrow.lib

CLASSES
    _frozen_importlib.ReadStats(builtins.tuple)
        ReadStats
    _frozen_importlib.RecordBatchWithMetadata(builtins.tuple)
        RecordBatchWithMetadata
    _frozen_importlib.WriteStats(builtins.tuple)
        WriteStats
    builtins.Exception(builtins.BaseException)
        ArrowException
            ArrowCancelled
            ArrowCapacityError
            ArrowIndexError(builtins.IndexError, ArrowException)
            ArrowInvalid(builtins.ValueError, ArrowException)
            ArrowKeyError(builtins.KeyError, ArrowException)
            ArrowMemoryError(builtins.MemoryError, ArrowException)
            ArrowNotImplementedError(builtins.NotImplementedError, ArrowException)
            ArrowSerializationError
            ArrowTypeError(builtins.TypeError, ArrowException)
    builtins.IndexError(builtins.LookupError)
        ArrowIndexError(builtins.IndexError, ArrowException)
    builtins.KeyError(builtins.LookupError)
        ArrowKeyError(builtins.KeyError, ArrowException)
    builtins.MemoryError(builtins.Exception)
        ArrowMemoryError(builtins.MemoryError, ArrowException)
    builtins.NotImplementedError(builtins.RuntimeError)
        ArrowNotImplementedError(builtins.NotImplementedError, ArrowException)
    builtins.TypeError(builtins.Exception)
        ArrowTypeError(builtins.TypeError, ArrowException)
    builtins.ValueError(builtins.Exception)
        ArrowInvalid(builtins.ValueError, ArrowException)
    builtins.object
        SignalStopHandler
        StopToken
        TableGroupBy
        Transcoder
    collections.abc.Mapping(collections.abc.Collection)
        KeyValueMetadata(_Metadata, collections.abc.Mapping)
    enum.IntFlag(builtins.int, enum.ReprEnum, enum.Flag)
        Alignment
        DeviceAllocationType
        MetadataVersion
    _Metadata(_Weakrefable)
        KeyValueMetadata(_Metadata, collections.abc.Mapping)
    _PandasConvertible(_Weakrefable)
        Array
            BaseListArray
                FixedSizeListArray
                LargeListArray
                LargeListViewArray
                ListArray
                    MapArray
                ListViewArray
            BinaryArray
            BinaryViewArray
            BooleanArray
            DictionaryArray
            ExtensionArray
                Bool8Array
                FixedShapeTensorArray
                JsonArray
                OpaqueArray
                UuidArray
            FixedSizeBinaryArray
                Decima32Array
                Decimal128Array
                Decimal256Array
                Decimal32Array
                Decimal64Array
            LargeBinaryArray
            LargeStringArray
            MonthDayNanoIntervalArray
            NullArray
            NumericArray
                Date32Array
                Date64Array
                DurationArray
                FloatingPointArray
                    DoubleArray
                    FloatArray
                    HalfFloatArray
                IntegerArray
                    Int16Array
                    Int32Array
                    Int64Array
                    Int8Array
                    UInt16Array
                    UInt32Array
                    UInt64Array
                    UInt8Array
                Time32Array
                Time64Array
                TimestampArray
            RunEndEncodedArray
            StringArray
            StringViewArray
            StructArray
            UnionArray
        ChunkedArray
    _Tabular(_PandasConvertible)
        RecordBatch
        Table
    _Weakrefable(builtins.object)
        ArrayStatistics
        Buffer
            ResizableBuffer
        CacheOptions
        Codec
        DataType
            BaseExtensionType
                Bool8Type
                ExtensionType
                    UnknownExtensionType
                FixedShapeTensorType
                JsonType
                OpaqueType
                UuidType
            DictionaryType
            DurationType
            FixedSizeBinaryType
                Decimal128Type
                Decimal256Type
                Decimal32Type
                Decimal64Type
            FixedSizeListType
            LargeListType
            LargeListViewType
            ListType
            ListViewType
            MapType
            RunEndEncodedType
            StructType
            Time32Type
            Time64Type
            TimestampType
            UnionType
                DenseUnionType
                SparseUnionType
        Device
        DictionaryMemo
        Field
        IpcReadOptions
        IpcWriteOptions
        MemoryManager
        MemoryPool
            LoggingMemoryPool
            ProxyMemoryPool
        Message
        MessageReader
        NativeFile
            BufferOutputStream
            BufferReader
            BufferedInputStream
            BufferedOutputStream
            CompressedInputStream
            CompressedOutputStream
            FixedSizeBufferWriter
            MemoryMappedFile
            MockOutputStream
            OSFile
            PythonFile
            TransformInputStream
        RecordBatchReader
        Scalar
            BinaryScalar
                BinaryViewScalar
                FixedSizeBinaryScalar
                LargeBinaryScalar
                StringScalar
                    LargeStringScalar
                    StringViewScalar
            BooleanScalar
            Date32Scalar
            Date64Scalar
            Decimal128Scalar
            Decimal256Scalar
            Decimal32Scalar
            Decimal64Scalar
            DictionaryScalar
            DoubleScalar
            DurationScalar
            ExtensionScalar
                Bool8Scalar
                FixedShapeTensorScalar
                JsonScalar
                OpaqueScalar
                UuidScalar
            FloatScalar
            HalfFloatScalar
            Int16Scalar
            Int32Scalar
            Int64Scalar
            Int8Scalar
            ListScalar(Scalar, collections.abc.Sequence)
                FixedSizeListScalar
                LargeListScalar
                LargeListViewScalar
                ListViewScalar
                MapScalar(ListScalar, collections.abc.Mapping)
            MonthDayNanoIntervalScalar
            NullScalar
            RunEndEncodedScalar
            StructScalar(Scalar, collections.abc.Mapping)
            Time32Scalar
            Time64Scalar
            TimestampScalar
            UInt16Scalar
            UInt32Scalar
            UInt64Scalar
            UInt8Scalar
            UnionScalar
        Schema
        SparseCOOTensor
        SparseCSCMatrix
        SparseCSFTensor
        SparseCSRMatrix
        StringBuilder
        StringViewBuilder
        Tensor

    class Alignment(enum.IntFlag)
     |  Alignment(*values)
     |
     |  Method resolution order:
     |      Alignment
     |      enum.IntFlag
     |      builtins.int
     |      enum.ReprEnum
     |      enum.Flag
     |      enum.Enum
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __and__(self, other) from enum.Flag
     |
     |  __format__(self, format_spec, /) from builtins.int
     |      Convert to a string according to format_spec.
     |
     |  __invert__(self) from enum.Flag
     |
     |  __new__(cls, value) from enum.Enum
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  __or__(self, other) from enum.Flag
     |      Return self|value.
     |
     |  __rand__ = __and__(self, other)
     |
     |  __ror__ = __or__(self, other)
     |
     |  __rxor__ = __xor__(self, other)
     |
     |  __xor__(self, other) from enum.Flag
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  Any = <Alignment.Any: 0>
     |
     |  At64Byte = <Alignment.At64Byte: 64>
     |
     |  DataTypeSpecific = <Alignment.DataTypeSpecific: -3>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from enum.IntFlag:
     |
     |  __repr__(self) from enum.Flag
     |      Return repr(self).
     |
     |  __str__ = __repr__(self, /) from builtins.int
     |      Return repr(self).
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.int:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(self, value, /)
     |      Return self+value.
     |
     |  __bool__(self, /)
     |      True if self else False
     |
     |  __ceil__(...)
     |      Ceiling of an Integral returns itself.
     |
     |  __divmod__(self, value, /)
     |      Return divmod(self, value).
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __float__(self, /)
     |      float(self)
     |
     |  __floor__(...)
     |      Flooring an Integral returns itself.
     |
     |  __floordiv__(self, value, /)
     |      Return self//value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __getnewargs__(self, /)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  __int__(self, /)
     |      int(self)
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(self, value, /)
     |      Return self<<value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mod__(self, value, /)
     |      Return self%value.
     |
     |  __mul__(self, value, /)
     |      Return self*value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __pos__(self, /)
     |      +self
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rdivmod__(self, value, /)
     |      Return divmod(value, self).
     |
     |  __rfloordiv__(self, value, /)
     |      Return value//self.
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmod__(self, value, /)
     |      Return value%self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __round__(...)
     |      Rounding an Integral returns itself.
     |
     |      Rounding with an ndigits argument also returns an integer.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(self, value, /)
     |      Return self>>value.
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __sizeof__(self, /)
     |      Returns size in memory, in bytes.
     |
     |  __sub__(self, value, /)
     |      Return self-value.
     |
     |  __truediv__(self, value, /)
     |      Return self/value.
     |
     |  __trunc__(...)
     |      Truncating an Integral returns itself.
     |
     |  as_integer_ratio(self, /)
     |      Return a pair of integers, whose ratio is equal to the original int.
     |
     |      The ratio is in lowest terms and has a positive denominator.
     |
     |      >>> (10).as_integer_ratio()
     |      (10, 1)
     |      >>> (-10).as_integer_ratio()
     |      (-10, 1)
     |      >>> (0).as_integer_ratio()
     |      (0, 1)
     |
     |  bit_count(self, /)
     |      Number of ones in the binary representation of the absolute value of self.
     |
     |      Also known as the population count.
     |
     |      >>> bin(13)
     |      '0b1101'
     |      >>> (13).bit_count()
     |      3
     |
     |  bit_length(self, /)
     |      Number of bits necessary to represent self in binary.
     |
     |      >>> bin(37)
     |      '0b100101'
     |      >>> (37).bit_length()
     |      6
     |
     |  conjugate(...)
     |      Returns self, the complex conjugate of any int.
     |
     |  is_integer(self, /)
     |      Returns True. Exists for duck type compatibility with float.is_integer.
     |
     |  to_bytes(self, /, length=1, byteorder='big', *, signed=False)
     |      Return an array of bytes representing an integer.
     |
     |      length
     |        Length of bytes object to use.  An OverflowError is raised if the
     |        integer is not representable with the given number of bytes.  Default
     |        is length 1.
     |      byteorder
     |        The byte order used to represent the integer.  If byteorder is 'big',
     |        the most significant byte is at the beginning of the byte array.  If
     |        byteorder is 'little', the most significant byte is at the end of the
     |        byte array.  To request the native byte order of the host system, use
     |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
     |      signed
     |        Determines whether two's complement is used to represent the integer.
     |        If signed is False and a negative integer is given, an OverflowError
     |        is raised.
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from builtins.int:
     |
     |  from_bytes(bytes, byteorder='big', *, signed=False)
     |      Return the integer represented by the given array of bytes.
     |
     |      bytes
     |        Holds the array of bytes to convert.  The argument must either
     |        support the buffer protocol or be an iterable object producing bytes.
     |        Bytes and bytearray are examples of built-in objects that support the
     |        buffer protocol.
     |      byteorder
     |        The byte order used to represent the integer.  If byteorder is 'big',
     |        the most significant byte is at the beginning of the byte array.  If
     |        byteorder is 'little', the most significant byte is at the end of the
     |        byte array.  To request the native byte order of the host system, use
     |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
     |      signed
     |        Indicates whether two's complement is used to represent the integer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.int:
     |
     |  denominator
     |      the denominator of a rational number in lowest terms
     |
     |  imag
     |      the imaginary part of a complex number
     |
     |  numerator
     |      the numerator of a rational number in lowest terms
     |
     |  real
     |      the real part of a complex number
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from enum.Flag:
     |
     |  __contains__(self, other)
     |      Returns True if self has at least the same flags set as other.
     |
     |  __iter__(self)
     |      Returns flags in definition order.
     |
     |  __len__(self)
     |      Return the number of members (no aliases)
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from enum.Enum:
     |
     |  __dir__(self)
     |      Returns public methods and other interesting attributes.
     |
     |  __init__(self, *args, **kwds)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce_ex__(self, proto)
     |      Helper for pickle.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from enum.Enum:
     |
     |  name
     |      The name of the Enum member.
     |
     |  value
     |      The value of the Enum member.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from enum.EnumType:
     |
     |  __getitem__(name)
     |      Return the member matching `name`.
     |
     |  ----------------------------------------------------------------------
     |  Readonly properties inherited from enum.EnumType:
     |
     |  __members__
     |      Returns a mapping of member name->value.
     |
     |      This mapping lists all enum members, including aliases. Note that this
     |      is a read-only view of the internal mapping.

    class Array(_PandasConvertible)
     |  Array()
     |
     |  The base class for all Arrow arrays.
     |
     |  Method resolution order:
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class ArrayStatistics(_Weakrefable)
     |  ArrayStatistics()
     |
     |  The class for statistics of an array.
     |
     |  Method resolution order:
     |      ArrayStatistics
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      ArrayStatistics.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      ArrayStatistics.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  distinct_count
     |      The number of distinct values.
     |
     |  is_distinct_count_exact
     |      Whether the number of distinct values is a valid exact value or not.
     |
     |  is_max_exact
     |      Whether the maximum value is an exact value or not.
     |
     |  is_min_exact
     |      Whether the minimum value is an exact value or not.
     |
     |  is_null_count_exact
     |      Whether the number of null values is a valid exact value or not.
     |
     |  max
     |      The maximum value.
     |
     |  min
     |      The minimum value.
     |
     |  null_count
     |      The number of nulls.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class ArrowCancelled(ArrowException)
     |  ArrowCancelled(message, signum=None)
     |
     |  Method resolution order:
     |      ArrowCancelled
     |      ArrowException
     |      builtins.Exception
     |      builtins.BaseException
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, message, signum=None)
     |      ArrowCancelled.__init__(self, message, signum=None)
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ArrowException:
     |
     |  __weakref__
     |      list of weak references to the object
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from builtins.Exception:
     |
     |  __new__(*args, **kwargs) class method of builtins.Exception
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.BaseException:
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __reduce__(...)
     |      Helper for pickle.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__(...)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  add_note(...)
     |      Exception.add_note(note) --
     |      add a note to the exception
     |
     |  with_traceback(...)
     |      Exception.with_traceback(tb) --
     |      set self.__traceback__ to tb and return self.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.BaseException:
     |
     |  __cause__
     |      exception cause
     |
     |  __context__
     |      exception context
     |
     |  __dict__
     |
     |  __suppress_context__
     |
     |  __traceback__
     |
     |  args

    class ArrowCapacityError(ArrowException)
     |  Method resolution order:
     |      ArrowCapacityError
     |      ArrowException
     |      builtins.Exception
     |      builtins.BaseException
     |      builtins.object
     |
     |  Data descriptors inherited from ArrowException:
     |
     |  __weakref__
     |      list of weak references to the object
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.Exception:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from builtins.Exception:
     |
     |  __new__(*args, **kwargs) class method of builtins.Exception
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.BaseException:
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __reduce__(...)
     |      Helper for pickle.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__(...)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  add_note(...)
     |      Exception.add_note(note) --
     |      add a note to the exception
     |
     |  with_traceback(...)
     |      Exception.with_traceback(tb) --
     |      set self.__traceback__ to tb and return self.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.BaseException:
     |
     |  __cause__
     |      exception cause
     |
     |  __context__
     |      exception context
     |
     |  __dict__
     |
     |  __suppress_context__
     |
     |  __traceback__
     |
     |  args

    class ArrowException(builtins.Exception)
     |  Method resolution order:
     |      ArrowException
     |      builtins.Exception
     |      builtins.BaseException
     |      builtins.object
     |
     |  Data descriptors defined here:
     |
     |  __weakref__
     |      list of weak references to the object
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.Exception:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from builtins.Exception:
     |
     |  __new__(*args, **kwargs) class method of builtins.Exception
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.BaseException:
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __reduce__(...)
     |      Helper for pickle.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__(...)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  add_note(...)
     |      Exception.add_note(note) --
     |      add a note to the exception
     |
     |  with_traceback(...)
     |      Exception.with_traceback(tb) --
     |      set self.__traceback__ to tb and return self.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.BaseException:
     |
     |  __cause__
     |      exception cause
     |
     |  __context__
     |      exception context
     |
     |  __dict__
     |
     |  __suppress_context__
     |
     |  __traceback__
     |
     |  args

    class ArrowIndexError(builtins.IndexError, ArrowException)
     |  Method resolution order:
     |      ArrowIndexError
     |      builtins.IndexError
     |      builtins.LookupError
     |      ArrowException
     |      builtins.Exception
     |      builtins.BaseException
     |      builtins.object
     |
     |  Data descriptors defined here:
     |
     |  __weakref__
     |      list of weak references to the object
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.IndexError:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from builtins.IndexError:
     |
     |  __new__(*args, **kwargs) class method of builtins.IndexError
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.BaseException:
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __reduce__(...)
     |      Helper for pickle.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__(...)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  add_note(...)
     |      Exception.add_note(note) --
     |      add a note to the exception
     |
     |  with_traceback(...)
     |      Exception.with_traceback(tb) --
     |      set self.__traceback__ to tb and return self.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.BaseException:
     |
     |  __cause__
     |      exception cause
     |
     |  __context__
     |      exception context
     |
     |  __dict__
     |
     |  __suppress_context__
     |
     |  __traceback__
     |
     |  args

    class ArrowInvalid(builtins.ValueError, ArrowException)
     |  Method resolution order:
     |      ArrowInvalid
     |      builtins.ValueError
     |      ArrowException
     |      builtins.Exception
     |      builtins.BaseException
     |      builtins.object
     |
     |  Data descriptors defined here:
     |
     |  __weakref__
     |      list of weak references to the object
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.ValueError:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from builtins.ValueError:
     |
     |  __new__(*args, **kwargs) class method of builtins.ValueError
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.BaseException:
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __reduce__(...)
     |      Helper for pickle.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__(...)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  add_note(...)
     |      Exception.add_note(note) --
     |      add a note to the exception
     |
     |  with_traceback(...)
     |      Exception.with_traceback(tb) --
     |      set self.__traceback__ to tb and return self.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.BaseException:
     |
     |  __cause__
     |      exception cause
     |
     |  __context__
     |      exception context
     |
     |  __dict__
     |
     |  __suppress_context__
     |
     |  __traceback__
     |
     |  args

    class ArrowKeyError(builtins.KeyError, ArrowException)
     |  Method resolution order:
     |      ArrowKeyError
     |      builtins.KeyError
     |      builtins.LookupError
     |      ArrowException
     |      builtins.Exception
     |      builtins.BaseException
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __str__(self)
     |      ArrowKeyError.__str__(self)
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  __weakref__
     |      list of weak references to the object
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.KeyError:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from builtins.LookupError:
     |
     |  __new__(*args, **kwargs) class method of builtins.LookupError
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.BaseException:
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __reduce__(...)
     |      Helper for pickle.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__(...)
     |
     |  add_note(...)
     |      Exception.add_note(note) --
     |      add a note to the exception
     |
     |  with_traceback(...)
     |      Exception.with_traceback(tb) --
     |      set self.__traceback__ to tb and return self.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.BaseException:
     |
     |  __cause__
     |      exception cause
     |
     |  __context__
     |      exception context
     |
     |  __dict__
     |
     |  __suppress_context__
     |
     |  __traceback__
     |
     |  args

    class ArrowMemoryError(builtins.MemoryError, ArrowException)
     |  Method resolution order:
     |      ArrowMemoryError
     |      builtins.MemoryError
     |      ArrowException
     |      builtins.Exception
     |      builtins.BaseException
     |      builtins.object
     |
     |  Data descriptors defined here:
     |
     |  __weakref__
     |      list of weak references to the object
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.MemoryError:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from builtins.MemoryError:
     |
     |  __new__(*args, **kwargs) class method of builtins.MemoryError
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.BaseException:
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __reduce__(...)
     |      Helper for pickle.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__(...)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  add_note(...)
     |      Exception.add_note(note) --
     |      add a note to the exception
     |
     |  with_traceback(...)
     |      Exception.with_traceback(tb) --
     |      set self.__traceback__ to tb and return self.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.BaseException:
     |
     |  __cause__
     |      exception cause
     |
     |  __context__
     |      exception context
     |
     |  __dict__
     |
     |  __suppress_context__
     |
     |  __traceback__
     |
     |  args

    class ArrowNotImplementedError(builtins.NotImplementedError, ArrowException)
     |  Method resolution order:
     |      ArrowNotImplementedError
     |      builtins.NotImplementedError
     |      builtins.RuntimeError
     |      ArrowException
     |      builtins.Exception
     |      builtins.BaseException
     |      builtins.object
     |
     |  Data descriptors defined here:
     |
     |  __weakref__
     |      list of weak references to the object
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.NotImplementedError:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from builtins.NotImplementedError:
     |
     |  __new__(*args, **kwargs) class method of builtins.NotImplementedError
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.BaseException:
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __reduce__(...)
     |      Helper for pickle.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__(...)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  add_note(...)
     |      Exception.add_note(note) --
     |      add a note to the exception
     |
     |  with_traceback(...)
     |      Exception.with_traceback(tb) --
     |      set self.__traceback__ to tb and return self.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.BaseException:
     |
     |  __cause__
     |      exception cause
     |
     |  __context__
     |      exception context
     |
     |  __dict__
     |
     |  __suppress_context__
     |
     |  __traceback__
     |
     |  args

    class ArrowSerializationError(ArrowException)
     |  Method resolution order:
     |      ArrowSerializationError
     |      ArrowException
     |      builtins.Exception
     |      builtins.BaseException
     |      builtins.object
     |
     |  Data descriptors inherited from ArrowException:
     |
     |  __weakref__
     |      list of weak references to the object
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.Exception:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from builtins.Exception:
     |
     |  __new__(*args, **kwargs) class method of builtins.Exception
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.BaseException:
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __reduce__(...)
     |      Helper for pickle.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__(...)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  add_note(...)
     |      Exception.add_note(note) --
     |      add a note to the exception
     |
     |  with_traceback(...)
     |      Exception.with_traceback(tb) --
     |      set self.__traceback__ to tb and return self.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.BaseException:
     |
     |  __cause__
     |      exception cause
     |
     |  __context__
     |      exception context
     |
     |  __dict__
     |
     |  __suppress_context__
     |
     |  __traceback__
     |
     |  args

    class ArrowTypeError(builtins.TypeError, ArrowException)
     |  Method resolution order:
     |      ArrowTypeError
     |      builtins.TypeError
     |      ArrowException
     |      builtins.Exception
     |      builtins.BaseException
     |      builtins.object
     |
     |  Data descriptors defined here:
     |
     |  __weakref__
     |      list of weak references to the object
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.TypeError:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from builtins.TypeError:
     |
     |  __new__(*args, **kwargs) class method of builtins.TypeError
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.BaseException:
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __reduce__(...)
     |      Helper for pickle.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__(...)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  add_note(...)
     |      Exception.add_note(note) --
     |      add a note to the exception
     |
     |  with_traceback(...)
     |      Exception.with_traceback(tb) --
     |      set self.__traceback__ to tb and return self.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.BaseException:
     |
     |  __cause__
     |      exception cause
     |
     |  __context__
     |      exception context
     |
     |  __dict__
     |
     |  __suppress_context__
     |
     |  __traceback__
     |
     |  args

    class BaseExtensionType(DataType)
     |  Concrete base class for extension types.
     |
     |  Method resolution order:
     |      BaseExtensionType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_ext_class__(self)
     |      BaseExtensionType.__arrow_ext_class__(self)
     |
     |      The associated array extension class
     |
     |  __arrow_ext_scalar_class__(self)
     |      BaseExtensionType.__arrow_ext_scalar_class__(self)
     |
     |      The associated scalar class
     |
     |  wrap_array(self, storage)
     |      BaseExtensionType.wrap_array(self, storage)
     |
     |      Wrap the given storage array as an extension array.
     |
     |      Parameters
     |      ----------
     |      storage : Array or ChunkedArray
     |
     |      Returns
     |      -------
     |      array : Array or ChunkedArray
     |          Extension array wrapping the storage array
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  bit_width
     |      The bit width of the extension type.
     |
     |  byte_width
     |      The byte width of the extension type.
     |
     |  extension_name
     |      The extension type name.
     |
     |  storage_type
     |      The underlying storage type.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      DataType.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class BaseListArray(Array)
     |  Method resolution order:
     |      BaseListArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  flatten(self, recursive=False)
     |      BaseListArray.flatten(self, recursive=False)
     |
     |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
     |      according to 'recursive'.
     |
     |      Note that this method is different from ``self.values`` in that
     |      it takes care of the slicing offset as well as null elements backed
     |      by non-empty sub-lists.
     |
     |      Parameters
     |      ----------
     |      recursive : bool, default False, optional
     |          When True, flatten this logical list-array recursively until an
     |          array of non-list values is formed.
     |
     |          When False, flatten only the top level.
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |      Examples
     |      --------
     |
     |      Basic logical list-array's flatten
     |
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, 3, 4]
     |      >>> offsets = [2, 1, 0]
     |      >>> sizes = [2, 2, 2]
     |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array
     |      <pyarrow.lib.ListViewArray object at ...>
     |      [
     |        [
     |          3,
     |          4
     |        ],
     |        [
     |          2,
     |          3
     |        ],
     |        [
     |          1,
     |          2
     |        ]
     |      ]
     |      >>> array.flatten()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        3,
     |        4,
     |        2,
     |        3,
     |        1,
     |        2
     |      ]
     |
     |      When recursive=True, nested list arrays are flattened recursively
     |      until an array of non-list values is formed.
     |
     |      >>> array = pa.array([
     |      ...    None,
     |      ...    [
     |      ...        [1, None, 2],
     |      ...        None,
     |      ...        [3, 4]
     |      ...    ],
     |      ...    [],
     |      ...    [
     |      ...        [],
     |      ...        [5, 6],
     |      ...        None
     |      ...    ],
     |      ...    [
     |      ...        [7, 8]
     |      ...    ]
     |      ... ], type=pa.list_(pa.list_(pa.int64())))
     |      >>> array.flatten(True)
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        null,
     |        2,
     |        3,
     |        4,
     |        5,
     |        6,
     |        7,
     |        8
     |      ]
     |
     |  value_lengths(self)
     |      BaseListArray.value_lengths(self)
     |
     |      Return integers array with values equal to the respective length of
     |      each list element. Null list values are null in the output.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_lengths()
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        3,
     |        0,
     |        null,
     |        1
     |      ]
     |
     |  value_parent_indices(self)
     |      BaseListArray.value_parent_indices(self)
     |
     |      Return array of same length as list child values array where each
     |      output value is the index of the parent list array slot containing each
     |      child value.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_parent_indices()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        0,
     |        0,
     |        0,
     |        3
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class BinaryArray(Array)
     |  Concrete class for Arrow arrays of variable-sized binary data type.
     |
     |  Method resolution order:
     |      BinaryArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  total_values_length
     |      The number of bytes from beginning to end of the data buffer addressed
     |      by the offsets of this BinaryArray.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class BinaryScalar(Scalar)
     |  Concrete class for binary-like scalars.
     |
     |  Method resolution order:
     |      BinaryScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __buffer__(self, flags, /)
     |      Return a buffer object that exposes the underlying memory of the object.
     |
     |  __bytes__(self)
     |      BinaryScalar.__bytes__(self)
     |
     |  as_buffer(self)
     |      BinaryScalar.as_buffer(self)
     |
     |      Return a view over this value as a Buffer object.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      BinaryScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python bytes.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class BinaryViewArray(Array)
     |  Concrete class for Arrow arrays of variable-sized binary view data type.
     |
     |  Method resolution order:
     |      BinaryViewArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class BinaryViewScalar(BinaryScalar)
     |  Method resolution order:
     |      BinaryViewScalar
     |      BinaryScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BinaryScalar:
     |
     |  __buffer__(self, flags, /)
     |      Return a buffer object that exposes the underlying memory of the object.
     |
     |  __bytes__(self)
     |      BinaryScalar.__bytes__(self)
     |
     |  as_buffer(self)
     |      BinaryScalar.as_buffer(self)
     |
     |      Return a view over this value as a Buffer object.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      BinaryScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python bytes.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Bool8Array(ExtensionArray)
     |  Concrete class for bool8 extension arrays.
     |
     |  Examples
     |  --------
     |  Define the extension type for an bool8 array
     |
     |  >>> import pyarrow as pa
     |  >>> bool8_type = pa.bool8()
     |
     |  Create an extension array
     |
     |  >>> arr = [-1, 0, 1, 2, None]
     |  >>> storage = pa.array(arr, pa.int8())
     |  >>> pa.ExtensionArray.from_storage(bool8_type, storage)
     |  <pyarrow.lib.Bool8Array object at ...>
     |  [
     |    -1,
     |    0,
     |    1,
     |    2,
     |    null
     |  ]
     |
     |  Method resolution order:
     |      Bool8Array
     |      ExtensionArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Bool8Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy bool view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for arrays without any nulls.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_numpy(obj)
     |      Bool8Array.from_numpy(obj)
     |
     |      Convert numpy array to a bool8 extension array without making a copy.
     |      The input array must be 1-dimensional, with either ``bool_`` or ``int8`` dtype.
     |
     |      Parameters
     |      ----------
     |      obj : numpy.ndarray
     |
     |      Returns
     |      -------
     |      bool8_array : Bool8Array
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> arr = np.array([True, False, True], dtype=np.bool_)
     |      >>> pa.Bool8Array.from_numpy(arr)
     |      <pyarrow.lib.Bool8Array object at ...>
     |      [
     |        1,
     |        0,
     |        1
     |      ]
     |
     |  from_storage(storage)
     |      Bool8Array.from_storage(Int8Array storage)
     |
     |      Construct Bool8Array from Int8Array storage.
     |
     |      Parameters
     |      ----------
     |      storage : Int8Array
     |          The underlying storage for the result array.
     |
     |      Returns
     |      -------
     |      bool8_array : Bool8Array
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ExtensionArray:
     |
     |  storage
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Bool8Scalar(ExtensionScalar)
     |  Concrete class for bool8 extension scalar.
     |
     |  Method resolution order:
     |      Bool8Scalar
     |      ExtensionScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Bool8Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this scalar as a Python object.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from ExtensionScalar:
     |
     |  from_storage(typ, value)
     |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
     |
     |      Construct ExtensionScalar from type and storage value.
     |
     |      Parameters
     |      ----------
     |      typ : DataType
     |          The extension type for the result scalar.
     |      value : object
     |          The storage value for the result scalar.
     |
     |      Returns
     |      -------
     |      ext_scalar : ExtensionScalar
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ExtensionScalar:
     |
     |  value
     |      Return storage value as a scalar.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Bool8Type(BaseExtensionType)
     |  Concrete class for bool8 extension type.
     |
     |  Bool8 is an alternate representation for boolean
     |  arrays using 8 bits instead of 1 bit per value. The underlying
     |  storage type is int8.
     |
     |  Examples
     |  --------
     |  Create an instance of bool8 extension type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.bool8()
     |  Bool8Type(extension<arrow.bool8>)
     |
     |  Method resolution order:
     |      Bool8Type
     |      BaseExtensionType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_ext_class__(self)
     |      Bool8Type.__arrow_ext_class__(self)
     |
     |  __arrow_ext_scalar_class__(self)
     |      Bool8Type.__arrow_ext_scalar_class__(self)
     |
     |  __reduce__(self)
     |      Bool8Type.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseExtensionType:
     |
     |  wrap_array(self, storage)
     |      BaseExtensionType.wrap_array(self, storage)
     |
     |      Wrap the given storage array as an extension array.
     |
     |      Parameters
     |      ----------
     |      storage : Array or ChunkedArray
     |
     |      Returns
     |      -------
     |      array : Array or ChunkedArray
     |          Extension array wrapping the storage array
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from BaseExtensionType:
     |
     |  bit_width
     |      The bit width of the extension type.
     |
     |  byte_width
     |      The byte width of the extension type.
     |
     |  extension_name
     |      The extension type name.
     |
     |  storage_type
     |      The underlying storage type.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class BooleanArray(Array)
     |  Concrete class for Arrow arrays of boolean data type.
     |
     |  Method resolution order:
     |      BooleanArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  false_count
     |
     |  true_count
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class BooleanScalar(Scalar)
     |  Concrete class for boolean scalars.
     |
     |  Method resolution order:
     |      BooleanScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __bool__(self, /)
     |      True if self else False
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      BooleanScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python bool.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Buffer(_Weakrefable)
     |  Buffer()
     |
     |  The base class for all Arrow buffers.
     |
     |  A buffer represents a contiguous memory area.  Many buffers will own
     |  their memory, though not all of them do.
     |
     |  Method resolution order:
     |      Buffer
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __buffer__(self, flags, /)
     |      Return a buffer object that exposes the underlying memory of the object.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(self, key, /)
     |      Return self[key].
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce_ex__(self, protocol)
     |      Buffer.__reduce_ex__(self, protocol)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  equals(self, other)
     |      Buffer.equals(self, Buffer other)
     |
     |      Determine if two buffers contain exactly the same data.
     |
     |      Parameters
     |      ----------
     |      other : Buffer
     |
     |      Returns
     |      -------
     |      are_equal : bool
     |          True if buffer contents and size are equal
     |
     |  hex(self)
     |      Buffer.hex(self)
     |
     |      Compute hexadecimal representation of the buffer.
     |
     |      Returns
     |      -------
     |      : bytes
     |
     |  slice(self, offset=0, length=None)
     |      Buffer.slice(self, offset=0, length=None)
     |
     |      Slice this buffer.  Memory is not copied.
     |
     |      You can also use the Python slice notation ``buffer[start:stop]``.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of buffer to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Buffer starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Buffer
     |          A logical view over this buffer.
     |
     |  to_pybytes(self)
     |      Buffer.to_pybytes(self)
     |
     |      Return this buffer as a Python bytes object. Memory is copied.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  address
     |      The buffer's address, as an integer.
     |
     |      The returned address may point to CPU or device memory.
     |      Use `is_cpu()` to disambiguate.
     |
     |  device
     |      The device where the buffer resides.
     |
     |      Returns
     |      -------
     |      Device
     |
     |  device_type
     |      The device type where the buffer resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the buffer is CPU-accessible.
     |
     |  is_mutable
     |      Whether the buffer is mutable.
     |
     |  memory_manager
     |      The memory manager associated with the buffer.
     |
     |      Returns
     |      -------
     |      MemoryManager
     |
     |  parent
     |
     |  size
     |      The buffer size in bytes.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class BufferOutputStream(NativeFile)
     |  An output stream that writes to a resizable buffer.
     |
     |  The buffer is produced as a result when ``getvalue()`` is called.
     |
     |  Examples
     |  --------
     |  Create an output stream, write data to it and finalize it with
     |  ``getvalue()``:
     |
     |  >>> import pyarrow as pa
     |  >>> f = pa.BufferOutputStream()
     |  >>> f.write(b'pyarrow.Buffer')
     |  14
     |  >>> f.closed
     |  False
     |  >>> f.getvalue()
     |  <pyarrow.Buffer address=... size=14 is_cpu=True is_mutable=True>
     |  >>> f.closed
     |  True
     |
     |  Method resolution order:
     |      BufferOutputStream
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__ = __reduce_cython__(...)
     |      BufferOutputStream.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      BufferOutputStream.__setstate_cython__(self, __pyx_state)
     |
     |  getvalue(self)
     |      BufferOutputStream.getvalue(self)
     |
     |      Finalize output stream and return result as pyarrow.Buffer.
     |
     |      Returns
     |      -------
     |      value : Buffer
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  fileno(self)
     |      NativeFile.fileno(self)
     |
     |      NOT IMPLEMENTED
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class BufferReader(NativeFile)
     |  BufferReader(obj)
     |
     |  Zero-copy reader from objects convertible to Arrow buffer.
     |
     |  Parameters
     |  ----------
     |  obj : Python bytes or pyarrow.Buffer
     |
     |  Examples
     |  --------
     |  Create an Arrow input stream and inspect it:
     |
     |  >>> import pyarrow as pa
     |  >>> data = b'reader data'
     |  >>> buf = memoryview(data)
     |  >>> with pa.input_stream(buf) as stream:
     |  ...     stream.size()
     |  ...     stream.read(6)
     |  ...     stream.seek(7)
     |  ...     stream.read(15)
     |  ...
     |  11
     |  b'reader'
     |  7
     |  b'data'
     |
     |  Method resolution order:
     |      BufferReader
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      BufferReader.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      BufferReader.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  fileno(self)
     |      NativeFile.fileno(self)
     |
     |      NOT IMPLEMENTED
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class BufferedInputStream(NativeFile)
     |  BufferedInputStream(NativeFile stream, int buffer_size, MemoryPool memory_pool=None)
     |
     |  An input stream that performs buffered reads from
     |  an unbuffered input stream, which can mitigate the overhead
     |  of many small reads in some cases.
     |
     |  Parameters
     |  ----------
     |  stream : NativeFile
     |      The input stream to wrap with the buffer
     |  buffer_size : int
     |      Size of the temporary read buffer.
     |  memory_pool : MemoryPool
     |      The memory pool used to allocate the buffer.
     |
     |  Method resolution order:
     |      BufferedInputStream
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      BufferedInputStream.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      BufferedInputStream.__setstate_cython__(self, __pyx_state)
     |
     |  detach(self)
     |      BufferedInputStream.detach(self)
     |
     |      Release the raw InputStream.
     |      Further operations on this stream are invalid.
     |
     |      Returns
     |      -------
     |      raw : NativeFile
     |          The underlying raw input stream
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  fileno(self)
     |      NativeFile.fileno(self)
     |
     |      NOT IMPLEMENTED
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class BufferedOutputStream(NativeFile)
     |  BufferedOutputStream(NativeFile stream, int buffer_size, MemoryPool memory_pool=None)
     |
     |  An output stream that performs buffered reads from
     |  an unbuffered output stream, which can mitigate the overhead
     |  of many small writes in some cases.
     |
     |  Parameters
     |  ----------
     |  stream : NativeFile
     |      The writable output stream to wrap with the buffer
     |  buffer_size : int
     |      Size of the buffer that should be added.
     |  memory_pool : MemoryPool
     |      The memory pool used to allocate the buffer.
     |
     |  Method resolution order:
     |      BufferedOutputStream
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      BufferedOutputStream.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      BufferedOutputStream.__setstate_cython__(self, __pyx_state)
     |
     |  detach(self)
     |      BufferedOutputStream.detach(self)
     |
     |      Flush any buffered writes and release the raw OutputStream.
     |      Further operations on this stream are invalid.
     |
     |      Returns
     |      -------
     |      raw : NativeFile
     |          The underlying raw output stream.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  fileno(self)
     |      NativeFile.fileno(self)
     |
     |      NOT IMPLEMENTED
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class CacheOptions(_Weakrefable)
     |  CacheOptions(hole_size_limit=None, *, range_size_limit=None, lazy=None, prefetch_limit=None)
     |
     |  Cache options for a pre-buffered fragment scan.
     |
     |  Parameters
     |  ----------
     |  hole_size_limit : int, default 8KiB
     |      The maximum distance in bytes between two consecutive ranges; beyond
     |      this value, ranges are not combined.
     |  range_size_limit : int, default 32MiB
     |      The maximum size in bytes of a combined range; if combining two
     |      consecutive ranges would produce a range of a size greater than this,
     |      they are not combined
     |  lazy : bool, default True
     |      lazy = false: request all byte ranges when PreBuffer or WillNeed is called.
     |      lazy = True, prefetch_limit = 0: request merged byte ranges only after the reader
     |      needs them.
     |      lazy = True, prefetch_limit = k: prefetch up to k merged byte ranges ahead of the
     |      range that is currently being read.
     |  prefetch_limit : int, default 0
     |      The maximum number of ranges to be prefetched. This is only used for
     |      lazy cache to asynchronously read some ranges after reading the target
     |      range.
     |
     |  Method resolution order:
     |      CacheOptions
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      CacheOptions.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_network_metrics(time_to_first_byte_millis, transfer_bandwidth_mib_per_sec, ideal_bandwidth_utilization_frac=0.9, max_ideal_request_size_mib=64)
     |      CacheOptions.from_network_metrics(time_to_first_byte_millis, transfer_bandwidth_mib_per_sec, ideal_bandwidth_utilization_frac=0.9, max_ideal_request_size_mib=64)
     |
     |      Create suitable CacheOptions based on provided network metrics.
     |
     |      Typically this will be used with object storage solutions like Amazon S3,
     |      Google Cloud Storage and Azure Blob Storage.
     |
     |      Parameters
     |      ----------
     |      time_to_first_byte_millis : int
     |          Seek-time or Time-To-First-Byte (TTFB) in milliseconds, also called call
     |          setup latency of a new read request. The value is a positive integer.
     |      transfer_bandwidth_mib_per_sec : int
     |          Data transfer Bandwidth (BW) in MiB/sec (per connection). The value is a positive
     |          integer.
     |      ideal_bandwidth_utilization_frac : int, default 0.9
     |          Transfer bandwidth utilization fraction (per connection) to maximize the net
     |          data load. The value is a positive float less than 1.
     |      max_ideal_request_size_mib : int, default 64
     |          The maximum single data request size (in MiB) to maximize the net data load.
     |
     |      Returns
     |      -------
     |      CacheOptions
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  hole_size_limit
     |
     |  lazy
     |
     |  prefetch_limit
     |
     |  range_size_limit
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class ChunkedArray(_PandasConvertible)
     |  ChunkedArray()
     |
     |  An array-like composed from a (possibly empty) collection of pyarrow.Arrays
     |
     |  Warnings
     |  --------
     |  Do not call this class's constructor directly.
     |
     |  Examples
     |  --------
     |  To construct a ChunkedArray object use :func:`pyarrow.chunked_array`:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.chunked_array([], type=pa.int8())
     |  <pyarrow.lib.ChunkedArray object at ...>
     |  [
     |  ...
     |  ]
     |
     |  >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |  <pyarrow.lib.ChunkedArray object at ...>
     |  [
     |    [
     |      2,
     |      2,
     |      4
     |    ],
     |    [
     |      4,
     |      5,
     |      100
     |    ]
     |  ]
     |  >>> isinstance(pa.chunked_array([[2, 2, 4], [4, 5, 100]]), pa.ChunkedArray)
     |  True
     |
     |  Method resolution order:
     |      ChunkedArray
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __array__(self, dtype=None, copy=None)
     |      ChunkedArray.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_stream__(self, requested_schema=None)
     |      ChunkedArray.__arrow_c_stream__(self, requested_schema=None)
     |
     |      Export to a C ArrowArrayStream PyCapsule.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule, default None
     |          The schema to which the stream should be casted, passed as a
     |          PyCapsule containing a C ArrowSchema representation of the
     |          requested schema.
     |
     |      Returns
     |      -------
     |      PyCapsule
     |          A capsule containing a C ArrowArrayStream struct.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      ChunkedArray.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or ChunkedArray (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      ChunkedArray.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __sizeof__(self)
     |      ChunkedArray.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  cast(self, target_type=None, safe=None, options=None)
     |      ChunkedArray.cast(self, target_type=None, safe=None, options=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |
     |      Returns
     |      -------
     |      cast : Array or ChunkedArray
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.type
     |      DataType(int64)
     |
     |      Change the data type of an array:
     |
     |      >>> n_legs_seconds = n_legs.cast(pa.duration('s'))
     |      >>> n_legs_seconds.type
     |      DurationType(duration[s])
     |
     |  chunk(self, i)
     |      ChunkedArray.chunk(self, i)
     |
     |      Select a chunk by its index.
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Array
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
     |      >>> n_legs.chunk(1)
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        4,
     |        5,
     |        100
     |      ]
     |
     |  combine_chunks(self, memory_pool=None)
     |      ChunkedArray.combine_chunks(self, MemoryPool memory_pool=None)
     |
     |      Flatten this ChunkedArray into a single non-chunked array.
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          For memory allocations, if required, otherwise use default pool
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2,
     |          4
     |        ],
     |        [
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |      >>> n_legs.combine_chunks()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        2,
     |        2,
     |        4,
     |        4,
     |        5,
     |        100
     |      ]
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      ChunkedArray.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : ChunkedArray
     |          A dictionary-encoded version of this array.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> animals = pa.chunked_array((
     |      ...             ["Flamingo", "Parrot", "Dog"],
     |      ...             ["Horse", "Brittle stars", "Centipede"]
     |      ...             ))
     |      >>> animals.dictionary_encode()
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |      ...
     |        -- dictionary:
     |          [
     |            "Flamingo",
     |            "Parrot",
     |            "Dog",
     |            "Horse",
     |            "Brittle stars",
     |            "Centipede"
     |          ]
     |        -- indices:
     |          [
     |            0,
     |            1,
     |            2
     |          ],
     |      ...
     |        -- dictionary:
     |          [
     |            "Flamingo",
     |            "Parrot",
     |            "Dog",
     |            "Horse",
     |            "Brittle stars",
     |            "Centipede"
     |          ]
     |        -- indices:
     |          [
     |            3,
     |            4,
     |            5
     |          ]
     |      ]
     |
     |  drop_null(self)
     |      ChunkedArray.drop_null(self)
     |
     |      Remove missing values from a chunked array.
     |      See :func:`pyarrow.compute.drop_null` for full description.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
     |      >>> n_legs
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2,
     |          null
     |        ],
     |        [
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |      >>> n_legs.drop_null()
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2
     |        ],
     |        [
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |
     |  equals(self, other)
     |      ChunkedArray.equals(self, ChunkedArray other)
     |
     |      Return whether the contents of two chunked arrays are equal.
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.ChunkedArray
     |          Chunked array to compare against.
     |
     |      Returns
     |      -------
     |      are_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> animals = pa.chunked_array((
     |      ...             ["Flamingo", "Parrot", "Dog"],
     |      ...             ["Horse", "Brittle stars", "Centipede"]
     |      ...             ))
     |      >>> n_legs.equals(n_legs)
     |      True
     |      >>> n_legs.equals(animals)
     |      False
     |
     |  fill_null(self, fill_value)
     |      ChunkedArray.fill_null(self, fill_value)
     |
     |      Replace each null element in values with fill_value.
     |
     |      See :func:`pyarrow.compute.fill_null` for full usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array or ChunkedArray
     |          A new array with nulls replaced by the given value.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> fill_value = pa.scalar(5, type=pa.int8())
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
     |      >>> n_legs.fill_null(fill_value)
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2,
     |          4,
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |
     |  filter(self, mask, null_selection_behavior='drop')
     |      ChunkedArray.filter(self, mask, null_selection_behavior='drop')
     |
     |      Select values from the chunked array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the chunked array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array or ChunkedArray
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2,
     |          4
     |        ],
     |        [
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |      >>> mask = pa.array([True, False, None, True, False, True])
     |      >>> n_legs.filter(mask)
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2
     |        ],
     |        [
     |          4,
     |          100
     |        ]
     |      ]
     |      >>> n_legs.filter(mask, null_selection_behavior="emit_null")
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          null
     |        ],
     |        [
     |          4,
     |          100
     |        ]
     |      ]
     |
     |  flatten(self, memory_pool=None)
     |      ChunkedArray.flatten(self, MemoryPool memory_pool=None)
     |
     |      Flatten this ChunkedArray.  If it has a struct type, the column is
     |      flattened into one array per struct field.
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          For memory allocations, if required, otherwise use default pool
     |
     |      Returns
     |      -------
     |      result : list of ChunkedArray
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> c_arr = pa.chunked_array(n_legs.value_counts())
     |      >>> c_arr
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        -- is_valid: all not null
     |        -- child 0 type: int64
     |          [
     |            2,
     |            4,
     |            5,
     |            100
     |          ]
     |        -- child 1 type: int64
     |          [
     |            2,
     |            2,
     |            1,
     |            1
     |          ]
     |      ]
     |      >>> c_arr.flatten()
     |      [<pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          4,
     |          5,
     |          100
     |        ]
     |      ], <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2,
     |          1,
     |          1
     |        ]
     |      ]]
     |      >>> c_arr.type
     |      StructType(struct<values: int64, counts: int64>)
     |      >>> n_legs.type
     |      DataType(int64)
     |
     |  format(self, **kwargs)
     |      ChunkedArray.format(self, **kwargs)
     |
     |      DEPRECATED, use pyarrow.ChunkedArray.to_string
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict
     |
     |      Returns
     |      -------
     |      str
     |
     |  get_total_buffer_size(self)
     |      ChunkedArray.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the chunked array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
     |      >>> n_legs.get_total_buffer_size()
     |      49
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      ChunkedArray.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2,
     |          4
     |        ],
     |        [
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |      >>> n_legs.index(4)
     |      <pyarrow.Int64Scalar: 2>
     |      >>> n_legs.index(4, start=3)
     |      <pyarrow.Int64Scalar: 3>
     |
     |  is_nan(self)
     |      ChunkedArray.is_nan(self)
     |
     |      Return boolean array indicating the NaN values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> arr = pa.chunked_array([[2, np.nan, 4], [4, None, 100]])
     |      >>> arr.is_nan()
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          false,
     |          true,
     |          false,
     |          false,
     |          null,
     |          false
     |        ]
     |      ]
     |
     |  is_null(self, *, nan_is_null=False)
     |      ChunkedArray.is_null(self, *, nan_is_null=False)
     |
     |      Return boolean array indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array or ChunkedArray
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
     |      >>> n_legs.is_null()
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          false,
     |          false,
     |          false,
     |          false,
     |          true,
     |          false
     |        ]
     |      ]
     |
     |  is_valid(self)
     |      ChunkedArray.is_valid(self)
     |
     |      Return boolean array indicating the non-null values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
     |      >>> n_legs.is_valid()
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          true,
     |          true,
     |          true
     |        ],
     |        [
     |          true,
     |          false,
     |          true
     |        ]
     |      ]
     |
     |  iterchunks(self)
     |      ChunkedArray.iterchunks(self)
     |
     |      Convert to an iterator of ChunkArrays.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
     |      >>> for i in n_legs.iterchunks():
     |      ...     print(i.null_count)
     |      ...
     |      0
     |      1
     |
     |  length(self)
     |      ChunkedArray.length(self)
     |
     |      Return length of a ChunkedArray.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.length()
     |      6
     |
     |  slice(self, offset=0, length=None)
     |      ChunkedArray.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this ChunkedArray
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice
     |      length : int, default None
     |          Length of slice (default is until end of batch starting from
     |          offset)
     |
     |      Returns
     |      -------
     |      sliced : ChunkedArray
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2,
     |          4
     |        ],
     |        [
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |      >>> n_legs.slice(2,2)
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          4
     |        ],
     |        [
     |          4
     |        ]
     |      ]
     |
     |  sort(self, order='ascending', **kwargs)
     |      ChunkedArray.sort(self, order='ascending', **kwargs)
     |
     |      Sort the ChunkedArray
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : ChunkedArray
     |
     |  take(self, indices)
     |      ChunkedArray.take(self, indices)
     |
     |      Select values from the chunked array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array or ChunkedArray
     |          An array with the same datatype, containing the taken values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2,
     |          4
     |        ],
     |        [
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |      >>> n_legs.take([1,4,5])
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          5,
     |          100
     |        ]
     |      ]
     |
     |  to_numpy(self, zero_copy_only=False)
     |      ChunkedArray.to_numpy(self, zero_copy_only=False)
     |
     |      Return a NumPy copy of this array (experimental).
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default False
     |          Introduced for signature consistence with pyarrow.Array.to_numpy.
     |          This must be False here since NumPy arrays' buffer must be contiguous.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_numpy()
     |      array([  2,   2,   4,   4,   5, 100])
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      ChunkedArray.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
     |      >>> n_legs.to_pylist()
     |      [2, 2, 4, 4, None, 100]
     |
     |  to_string(self, *, indent=0, window=5, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      ChunkedArray.to_string(self, *, int indent=0, int window=5, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the ChunkedArray
     |
     |      Parameters
     |      ----------
     |      indent : int
     |          How much to indent right the content of the array,
     |          by default ``0``.
     |      window : int
     |          How many items to preview within each chunk at the begin and end
     |          of the chunk when the chunk is bigger than the window.
     |          The other elements will be ellipsed.
     |      container_window : int
     |          How many chunks to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other elements will be ellipsed.
     |          This setting also applies to list columns.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_string(skip_new_lines=True)
     |      '[[2,2,4],[4,5,100]]'
     |
     |  unify_dictionaries(self, memory_pool=None)
     |      ChunkedArray.unify_dictionaries(self, MemoryPool memory_pool=None)
     |
     |      Unify dictionaries across all chunks.
     |
     |      This method returns an equivalent chunked array, but where all
     |      chunks share the same dictionary values.  Dictionary indices are
     |      transposed accordingly.
     |
     |      If there are no dictionaries in the chunked array, it is returned
     |      unchanged.
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          For memory allocations, if required, otherwise use default pool
     |
     |      Returns
     |      -------
     |      result : ChunkedArray
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode()
     |      >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode()
     |      >>> c_arr = pa.chunked_array([arr_1, arr_2])
     |      >>> c_arr
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |      ...
     |        -- dictionary:
     |          [
     |            "Flamingo",
     |            "Parrot",
     |            "Dog"
     |          ]
     |        -- indices:
     |          [
     |            0,
     |            1,
     |            2
     |          ],
     |      ...
     |        -- dictionary:
     |          [
     |            "Horse",
     |            "Brittle stars",
     |            "Centipede"
     |          ]
     |        -- indices:
     |          [
     |            0,
     |            1,
     |            2
     |          ]
     |      ]
     |      >>> c_arr.unify_dictionaries()
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |      ...
     |        -- dictionary:
     |          [
     |            "Flamingo",
     |            "Parrot",
     |            "Dog",
     |            "Horse",
     |            "Brittle stars",
     |            "Centipede"
     |          ]
     |        -- indices:
     |          [
     |            0,
     |            1,
     |            2
     |          ],
     |      ...
     |        -- dictionary:
     |          [
     |            "Flamingo",
     |            "Parrot",
     |            "Dog",
     |            "Horse",
     |            "Brittle stars",
     |            "Centipede"
     |          ]
     |        -- indices:
     |          [
     |            3,
     |            4,
     |            5
     |          ]
     |      ]
     |
     |  unique(self)
     |      ChunkedArray.unique(self)
     |
     |      Compute distinct elements in array
     |
     |      Returns
     |      -------
     |      pyarrow.Array
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2,
     |          4
     |        ],
     |        [
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |      >>> n_legs.unique()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        2,
     |        4,
     |        5,
     |        100
     |      ]
     |
     |  validate(self, *, full=False)
     |      ChunkedArray.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      ChunkedArray.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      An array of  <input type "Values", int64_t "Counts"> structs
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2,
     |          4
     |        ],
     |        [
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |      >>> n_legs.value_counts()
     |      <pyarrow.lib.StructArray object at ...>
     |      -- is_valid: all not null
     |      -- child 0 type: int64
     |        [
     |          2,
     |          4,
     |          5,
     |          100
     |        ]
     |      -- child 1 type: int64
     |        [
     |          2,
     |          2,
     |          1,
     |          1
     |        ]
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  chunks
     |      Convert to a list of single-chunked arrays.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
     |      >>> n_legs
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          2,
     |          null
     |        ],
     |        [
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |      >>> n_legs.chunks
     |      [<pyarrow.lib.Int64Array object at ...>
     |      [
     |        2,
     |        2,
     |        null
     |      ], <pyarrow.lib.Int64Array object at ...>
     |      [
     |        4,
     |        5,
     |        100
     |      ]]
     |
     |  data
     |
     |  is_cpu
     |      Whether all chunks in the ChunkedArray are CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the chunked array.
     |
     |      In other words, the sum of bytes from all buffer ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will only be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
     |      >>> n_legs.nbytes
     |      49
     |
     |  null_count
     |      Number of null entries
     |
     |      Returns
     |      -------
     |      int
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
     |      >>> n_legs.null_count
     |      1
     |
     |  num_chunks
     |      Number of underlying chunks.
     |
     |      Returns
     |      -------
     |      int
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
     |      >>> n_legs.num_chunks
     |      2
     |
     |  type
     |      Return data type of a ChunkedArray.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.type
     |      DataType(int64)
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Codec(_Weakrefable)
     |  Codec(str compression, compression_level=None)
     |
     |  Compression codec.
     |
     |  Parameters
     |  ----------
     |  compression : str
     |      Type of compression codec to initialize, valid values are: 'gzip',
     |      'bz2', 'brotli', 'lz4' (or 'lz4_frame'), 'lz4_raw', 'zstd' and
     |      'snappy'.
     |  compression_level : int, None
     |      Optional parameter specifying how aggressively to compress.  The
     |      possible ranges and effect of this parameter depend on the specific
     |      codec chosen.  Higher values compress more but typically use more
     |      resources (CPU/RAM).  Some codecs support negative values.
     |
     |      gzip
     |          The compression_level maps to the memlevel parameter of
     |          deflateInit2.  Higher levels use more RAM but are faster
     |          and should have higher compression ratios.
     |
     |      bz2
     |          The compression level maps to the blockSize100k parameter of
     |          the BZ2_bzCompressInit function.  Higher levels use more RAM
     |          but are faster and should have higher compression ratios.
     |
     |      brotli
     |          The compression level maps to the BROTLI_PARAM_QUALITY
     |          parameter.  Higher values are slower and should have higher
     |          compression ratios.
     |
     |      lz4/lz4_frame/lz4_raw
     |          The compression level parameter is not supported and must
     |          be None
     |
     |      zstd
     |          The compression level maps to the compressionLevel parameter
     |          of ZSTD_initCStream.  Negative values are supported.  Higher
     |          values are slower and should have higher compression ratios.
     |
     |      snappy
     |          The compression level parameter is not supported and must
     |          be None
     |
     |
     |  Raises
     |  ------
     |  ValueError
     |      If invalid compression value is passed.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> pa.Codec.is_available('gzip')
     |  True
     |  >>> codec = pa.Codec('gzip')
     |  >>> codec.name
     |  'gzip'
     |  >>> codec.compression_level
     |  9
     |
     |  Method resolution order:
     |      Codec
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      Codec.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      Codec.__setstate_cython__(self, __pyx_state)
     |
     |  compress(self, buf, asbytes=False, memory_pool=None)
     |      Codec.compress(self, buf, asbytes=False, memory_pool=None)
     |
     |      Compress data from buffer-like object.
     |
     |      Parameters
     |      ----------
     |      buf : pyarrow.Buffer, bytes, or other object supporting buffer protocol
     |      asbytes : bool, default False
     |          Return result as Python bytes object, otherwise Buffer
     |      memory_pool : MemoryPool, default None
     |          Memory pool to use for buffer allocations, if any
     |
     |      Returns
     |      -------
     |      compressed : pyarrow.Buffer or bytes (if asbytes=True)
     |
     |  decompress(self, buf, decompressed_size=None, asbytes=False, memory_pool=None)
     |      Codec.decompress(self, buf, decompressed_size=None, asbytes=False, memory_pool=None)
     |
     |      Decompress data from buffer-like object.
     |
     |      Parameters
     |      ----------
     |      buf : pyarrow.Buffer, bytes, or memoryview-compatible object
     |      decompressed_size : int, default None
     |          Size of the decompressed result
     |      asbytes : boolean, default False
     |          Return result as Python bytes object, otherwise Buffer
     |      memory_pool : MemoryPool, default None
     |          Memory pool to use for buffer allocations, if any.
     |
     |      Returns
     |      -------
     |      uncompressed : pyarrow.Buffer or bytes (if asbytes=True)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  default_compression_level(compression)
     |      Codec.default_compression_level(str compression)
     |
     |      Returns the compression level that Arrow will use for the codec if
     |      None is specified.
     |
     |      Parameters
     |      ----------
     |      compression : str
     |          Type of compression codec,
     |          refer to Codec docstring for a list of supported ones.
     |
     |  detect(path)
     |      Codec.detect(path)
     |
     |      Detect and instantiate compression codec based on file extension.
     |
     |      Parameters
     |      ----------
     |      path : str, path-like
     |          File-path to detect compression from.
     |
     |      Raises
     |      ------
     |      TypeError
     |          If the passed value is not path-like.
     |      ValueError
     |          If the compression can't be detected from the path.
     |
     |      Returns
     |      -------
     |      Codec
     |
     |  is_available(compression)
     |      Codec.is_available(str compression)
     |
     |      Returns whether the compression support has been built and enabled.
     |
     |      Parameters
     |      ----------
     |      compression : str
     |           Type of compression codec,
     |           refer to Codec docstring for a list of supported ones.
     |
     |      Returns
     |      -------
     |      bool
     |
     |  maximum_compression_level(compression)
     |      Codec.maximum_compression_level(str compression)
     |
     |      Returns the largest valid value for the compression level
     |
     |      Parameters
     |      ----------
     |      compression : str
     |          Type of compression codec,
     |          refer to Codec docstring for a list of supported ones.
     |
     |  minimum_compression_level(compression)
     |      Codec.minimum_compression_level(str compression)
     |
     |      Returns the smallest valid value for the compression level
     |
     |      Parameters
     |      ----------
     |      compression : str
     |          Type of compression codec,
     |          refer to Codec docstring for a list of supported ones.
     |
     |  supports_compression_level(compression)
     |      Codec.supports_compression_level(str compression)
     |
     |      Returns true if the compression level parameter is supported
     |      for the given codec.
     |
     |      Parameters
     |      ----------
     |      compression : str
     |          Type of compression codec,
     |          refer to Codec docstring for a list of supported ones.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  compression_level
     |      Returns the compression level parameter of the codec
     |
     |  name
     |      Returns the name of the codec
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class CompressedInputStream(NativeFile)
     |  CompressedInputStream(stream, str compression)
     |
     |  An input stream wrapper which decompresses data on the fly.
     |
     |  Parameters
     |  ----------
     |  stream : string, path, pyarrow.NativeFile, or file-like object
     |      Input stream object to wrap with the compression.
     |  compression : str
     |      The compression type ("bz2", "brotli", "gzip", "lz4" or "zstd").
     |
     |  Examples
     |  --------
     |  Create an output stream which compresses the data:
     |
     |  >>> import pyarrow as pa
     |  >>> data = b"Compressed stream"
     |  >>> raw = pa.BufferOutputStream()
     |  >>> with pa.CompressedOutputStream(raw, "gzip") as compressed:
     |  ...     compressed.write(data)
     |  ...
     |  17
     |
     |  Create an input stream with decompression referencing the
     |  buffer with compressed data:
     |
     |  >>> cdata = raw.getvalue()
     |  >>> with pa.input_stream(cdata, compression="gzip") as compressed:
     |  ...     compressed.read()
     |  ...
     |  b'Compressed stream'
     |
     |  which actually translates to the use of ``BufferReader``and
     |  ``CompressedInputStream``:
     |
     |  >>> raw = pa.BufferReader(cdata)
     |  >>> with pa.CompressedInputStream(raw, "gzip") as compressed:
     |  ...     compressed.read()
     |  ...
     |  b'Compressed stream'
     |
     |  Method resolution order:
     |      CompressedInputStream
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      CompressedInputStream.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      CompressedInputStream.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  fileno(self)
     |      NativeFile.fileno(self)
     |
     |      NOT IMPLEMENTED
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class CompressedOutputStream(NativeFile)
     |  CompressedOutputStream(stream, str compression)
     |
     |  An output stream wrapper which compresses data on the fly.
     |
     |  Parameters
     |  ----------
     |  stream : string, path, pyarrow.NativeFile, or file-like object
     |      Input stream object to wrap with the compression.
     |  compression : str
     |      The compression type ("bz2", "brotli", "gzip", "lz4" or "zstd").
     |
     |  Examples
     |  --------
     |  Create an output stream which compresses the data:
     |
     |  >>> import pyarrow as pa
     |  >>> data = b"Compressed stream"
     |  >>> raw = pa.BufferOutputStream()
     |  >>> with pa.CompressedOutputStream(raw, "gzip") as compressed:
     |  ...     compressed.write(data)
     |  ...
     |  17
     |
     |  Method resolution order:
     |      CompressedOutputStream
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      CompressedOutputStream.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      CompressedOutputStream.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  fileno(self)
     |      NativeFile.fileno(self)
     |
     |      NOT IMPLEMENTED
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class DataType(_Weakrefable)
     |  DataType()
     |
     |  Base class of all Arrow data types.
     |
     |  Each data type is an *instance* of this class.
     |
     |  Examples
     |  --------
     |  Instance of int64 type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.int64()
     |  DataType(int64)
     |
     |  Method resolution order:
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      DataType.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class Date32Array(NumericArray)
     |  Concrete class for Arrow arrays of date32 data type.
     |
     |  Method resolution order:
     |      Date32Array
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Date32Scalar(Scalar)
     |  Concrete class for date32 scalars.
     |
     |  Method resolution order:
     |      Date32Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Date32Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python datetime.datetime instance.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Date64Array(NumericArray)
     |  Concrete class for Arrow arrays of date64 data type.
     |
     |  Method resolution order:
     |      Date64Array
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Date64Scalar(Scalar)
     |  Concrete class for date64 scalars.
     |
     |  Method resolution order:
     |      Date64Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Date64Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python datetime.datetime instance.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Decima32Array(FixedSizeBinaryArray)
     |  Concrete class for Arrow arrays of decimal32 data type.
     |
     |  Method resolution order:
     |      Decima32Array
     |      FixedSizeBinaryArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Decimal128Array(FixedSizeBinaryArray)
     |  Concrete class for Arrow arrays of decimal128 data type.
     |
     |  Method resolution order:
     |      Decimal128Array
     |      FixedSizeBinaryArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Decimal128Scalar(Scalar)
     |  Concrete class for decimal128 scalars.
     |
     |  Method resolution order:
     |      Decimal128Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Decimal128Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python Decimal.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Decimal128Type(FixedSizeBinaryType)
     |  Concrete class for decimal128 data types.
     |
     |  Examples
     |  --------
     |  Create an instance of decimal128 type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.decimal128(5, 2)
     |  Decimal128Type(decimal128(5, 2))
     |
     |  Method resolution order:
     |      Decimal128Type
     |      FixedSizeBinaryType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      Decimal128Type.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  precision
     |      The decimal precision, in number of decimal digits (an integer).
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.decimal128(5, 2)
     |      >>> t.precision
     |      5
     |
     |  scale
     |      The decimal scale (an integer).
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.decimal128(5, 2)
     |      >>> t.scale
     |      2
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class Decimal256Array(FixedSizeBinaryArray)
     |  Concrete class for Arrow arrays of decimal256 data type.
     |
     |  Method resolution order:
     |      Decimal256Array
     |      FixedSizeBinaryArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Decimal256Scalar(Scalar)
     |  Concrete class for decimal256 scalars.
     |
     |  Method resolution order:
     |      Decimal256Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Decimal256Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python Decimal.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Decimal256Type(FixedSizeBinaryType)
     |  Concrete class for decimal256 data types.
     |
     |  Examples
     |  --------
     |  Create an instance of decimal256 type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.decimal256(76, 38)
     |  Decimal256Type(decimal256(76, 38))
     |
     |  Method resolution order:
     |      Decimal256Type
     |      FixedSizeBinaryType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      Decimal256Type.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  precision
     |      The decimal precision, in number of decimal digits (an integer).
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.decimal256(76, 38)
     |      >>> t.precision
     |      76
     |
     |  scale
     |      The decimal scale (an integer).
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.decimal256(76, 38)
     |      >>> t.scale
     |      38
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class Decimal32Array(FixedSizeBinaryArray)
     |  Method resolution order:
     |      Decimal32Array
     |      FixedSizeBinaryArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Decimal32Scalar(Scalar)
     |  Concrete class for decimal32 scalars.
     |
     |  Method resolution order:
     |      Decimal32Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Decimal32Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python Decimal.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Decimal32Type(FixedSizeBinaryType)
     |  Concrete class for decimal32 data types.
     |
     |  Examples
     |  --------
     |  Create an instance of decimal32 type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.decimal32(5, 2)
     |  Decimal32Type(decimal32(5, 2))
     |
     |  Method resolution order:
     |      Decimal32Type
     |      FixedSizeBinaryType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      Decimal32Type.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  precision
     |      The decimal precision, in number of decimal digits (an integer).
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.decimal32(5, 2)
     |      >>> t.precision
     |      5
     |
     |  scale
     |      The decimal scale (an integer).
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.decimal32(5, 2)
     |      >>> t.scale
     |      2
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class Decimal64Array(FixedSizeBinaryArray)
     |  Concrete class for Arrow arrays of decimal64 data type.
     |
     |  Method resolution order:
     |      Decimal64Array
     |      FixedSizeBinaryArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Decimal64Scalar(Scalar)
     |  Concrete class for decimal64 scalars.
     |
     |  Method resolution order:
     |      Decimal64Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Decimal64Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python Decimal.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Decimal64Type(FixedSizeBinaryType)
     |  Concrete class for decimal64 data types.
     |
     |  Examples
     |  --------
     |  Create an instance of decimal64 type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.decimal64(5, 2)
     |  Decimal64Type(decimal64(5, 2))
     |
     |  Method resolution order:
     |      Decimal64Type
     |      FixedSizeBinaryType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      Decimal64Type.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  precision
     |      The decimal precision, in number of decimal digits (an integer).
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.decimal64(5, 2)
     |      >>> t.precision
     |      5
     |
     |  scale
     |      The decimal scale (an integer).
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.decimal64(5, 2)
     |      >>> t.scale
     |      2
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class DenseUnionType(UnionType)
     |  Concrete class for dense union types.
     |
     |  Examples
     |  --------
     |  Create an instance of a dense UnionType using ``pa.union``:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())],
     |  ...          mode=pa.lib.UnionMode_DENSE),
     |  (DenseUnionType(dense_union<a: fixed_size_binary[10]=0, b: string=1>),)
     |
     |  Create an instance of a dense UnionType using ``pa.dense_union``:
     |
     |  >>> pa.dense_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |  DenseUnionType(dense_union<a: fixed_size_binary[10]=0, b: string=1>)
     |
     |  Method resolution order:
     |      DenseUnionType
     |      UnionType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from UnionType:
     |
     |  __getitem__(...)
     |      UnionType.__getitem__(self, i)
     |
     |      Return a child field by its index.
     |
     |      Alias of ``field``.
     |
     |  __iter__(...)
     |      UnionType.__iter__(self)
     |
     |      Iterate over union members, in order.
     |
     |  __len__(...)
     |      UnionType.__len__(self)
     |
     |      Like num_fields().
     |
     |  __reduce__(self)
     |      UnionType.__reduce__(self)
     |
     |  field(self, i)
     |      UnionType.field(self, i) -> Field
     |
     |      Return a child field by its numeric index.
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |      >>> union[0]
     |      pyarrow.Field<a: fixed_size_binary[10]>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from UnionType:
     |
     |  mode
     |      The mode of the union ("dense" or "sparse").
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |      >>> union.mode
     |      'sparse'
     |
     |  type_codes
     |      The type code to indicate each data type in this union.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |      >>> union.type_codes
     |      [0, 1]
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class Device(_Weakrefable)
     |  Device()
     |
     |  Abstract interface for hardware devices
     |
     |  This object represents a device with access to some memory spaces.
     |  When handling a Buffer or raw memory address, it allows deciding in which
     |  context the raw memory address should be interpreted
     |  (e.g. CPU-accessible memory, or embedded memory on some particular GPU).
     |
     |  Method resolution order:
     |      Device
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      Device.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      Device.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  device_id
     |      A device ID to identify this device if there are multiple of this type.
     |
     |      If there is no "device_id" equivalent (such as for the main CPU device on
     |      non-numa systems) returns -1.
     |
     |  device_type
     |      Return the DeviceAllocationType of this device.
     |
     |  is_cpu
     |      Whether this device is the main CPU device.
     |
     |      This shorthand method is very useful when deciding whether a memory address
     |      is CPU-accessible.
     |
     |  type_name
     |      A shorthand for this device's type.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class DeviceAllocationType(enum.IntFlag)
     |  DeviceAllocationType(*values)
     |
     |  Method resolution order:
     |      DeviceAllocationType
     |      enum.IntFlag
     |      builtins.int
     |      enum.ReprEnum
     |      enum.Flag
     |      enum.Enum
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __and__(self, other) from enum.Flag
     |
     |  __format__(self, format_spec, /) from builtins.int
     |      Convert to a string according to format_spec.
     |
     |  __invert__(self) from enum.Flag
     |
     |  __new__(cls, value) from enum.Enum
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  __or__(self, other) from enum.Flag
     |      Return self|value.
     |
     |  __rand__ = __and__(self, other)
     |
     |  __ror__ = __or__(self, other)
     |
     |  __rxor__ = __xor__(self, other)
     |
     |  __xor__(self, other) from enum.Flag
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  CPU = <DeviceAllocationType.CPU: 1>
     |
     |  CUDA = <DeviceAllocationType.CUDA: 2>
     |
     |  CUDA_HOST = <DeviceAllocationType.CUDA_HOST: 3>
     |
     |  CUDA_MANAGED = <DeviceAllocationType.CUDA_MANAGED: 13>
     |
     |  EXT_DEV = <DeviceAllocationType.EXT_DEV: 12>
     |
     |  HEXAGON = <DeviceAllocationType.HEXAGON: 16>
     |
     |  METAL = <DeviceAllocationType.METAL: 8>
     |
     |  ONEAPI = <DeviceAllocationType.ONEAPI: 14>
     |
     |  OPENCL = <DeviceAllocationType.OPENCL: 4>
     |
     |  ROCM = <DeviceAllocationType.ROCM: 10>
     |
     |  ROCM_HOST = <DeviceAllocationType.ROCM_HOST: 11>
     |
     |  VPI = <DeviceAllocationType.VPI: 9>
     |
     |  VULKAN = <DeviceAllocationType.VULKAN: 7>
     |
     |  WEBGPU = <DeviceAllocationType.WEBGPU: 15>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from enum.IntFlag:
     |
     |  __repr__(self) from enum.Flag
     |      Return repr(self).
     |
     |  __str__ = __repr__(self, /) from builtins.int
     |      Return repr(self).
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.int:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(self, value, /)
     |      Return self+value.
     |
     |  __bool__(self, /)
     |      True if self else False
     |
     |  __ceil__(...)
     |      Ceiling of an Integral returns itself.
     |
     |  __divmod__(self, value, /)
     |      Return divmod(self, value).
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __float__(self, /)
     |      float(self)
     |
     |  __floor__(...)
     |      Flooring an Integral returns itself.
     |
     |  __floordiv__(self, value, /)
     |      Return self//value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __getnewargs__(self, /)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  __int__(self, /)
     |      int(self)
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(self, value, /)
     |      Return self<<value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mod__(self, value, /)
     |      Return self%value.
     |
     |  __mul__(self, value, /)
     |      Return self*value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __pos__(self, /)
     |      +self
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rdivmod__(self, value, /)
     |      Return divmod(value, self).
     |
     |  __rfloordiv__(self, value, /)
     |      Return value//self.
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmod__(self, value, /)
     |      Return value%self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __round__(...)
     |      Rounding an Integral returns itself.
     |
     |      Rounding with an ndigits argument also returns an integer.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(self, value, /)
     |      Return self>>value.
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __sizeof__(self, /)
     |      Returns size in memory, in bytes.
     |
     |  __sub__(self, value, /)
     |      Return self-value.
     |
     |  __truediv__(self, value, /)
     |      Return self/value.
     |
     |  __trunc__(...)
     |      Truncating an Integral returns itself.
     |
     |  as_integer_ratio(self, /)
     |      Return a pair of integers, whose ratio is equal to the original int.
     |
     |      The ratio is in lowest terms and has a positive denominator.
     |
     |      >>> (10).as_integer_ratio()
     |      (10, 1)
     |      >>> (-10).as_integer_ratio()
     |      (-10, 1)
     |      >>> (0).as_integer_ratio()
     |      (0, 1)
     |
     |  bit_count(self, /)
     |      Number of ones in the binary representation of the absolute value of self.
     |
     |      Also known as the population count.
     |
     |      >>> bin(13)
     |      '0b1101'
     |      >>> (13).bit_count()
     |      3
     |
     |  bit_length(self, /)
     |      Number of bits necessary to represent self in binary.
     |
     |      >>> bin(37)
     |      '0b100101'
     |      >>> (37).bit_length()
     |      6
     |
     |  conjugate(...)
     |      Returns self, the complex conjugate of any int.
     |
     |  is_integer(self, /)
     |      Returns True. Exists for duck type compatibility with float.is_integer.
     |
     |  to_bytes(self, /, length=1, byteorder='big', *, signed=False)
     |      Return an array of bytes representing an integer.
     |
     |      length
     |        Length of bytes object to use.  An OverflowError is raised if the
     |        integer is not representable with the given number of bytes.  Default
     |        is length 1.
     |      byteorder
     |        The byte order used to represent the integer.  If byteorder is 'big',
     |        the most significant byte is at the beginning of the byte array.  If
     |        byteorder is 'little', the most significant byte is at the end of the
     |        byte array.  To request the native byte order of the host system, use
     |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
     |      signed
     |        Determines whether two's complement is used to represent the integer.
     |        If signed is False and a negative integer is given, an OverflowError
     |        is raised.
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from builtins.int:
     |
     |  from_bytes(bytes, byteorder='big', *, signed=False)
     |      Return the integer represented by the given array of bytes.
     |
     |      bytes
     |        Holds the array of bytes to convert.  The argument must either
     |        support the buffer protocol or be an iterable object producing bytes.
     |        Bytes and bytearray are examples of built-in objects that support the
     |        buffer protocol.
     |      byteorder
     |        The byte order used to represent the integer.  If byteorder is 'big',
     |        the most significant byte is at the beginning of the byte array.  If
     |        byteorder is 'little', the most significant byte is at the end of the
     |        byte array.  To request the native byte order of the host system, use
     |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
     |      signed
     |        Indicates whether two's complement is used to represent the integer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.int:
     |
     |  denominator
     |      the denominator of a rational number in lowest terms
     |
     |  imag
     |      the imaginary part of a complex number
     |
     |  numerator
     |      the numerator of a rational number in lowest terms
     |
     |  real
     |      the real part of a complex number
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from enum.Flag:
     |
     |  __contains__(self, other)
     |      Returns True if self has at least the same flags set as other.
     |
     |  __iter__(self)
     |      Returns flags in definition order.
     |
     |  __len__(self)
     |      Return the number of members (no aliases)
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from enum.Enum:
     |
     |  __dir__(self)
     |      Returns public methods and other interesting attributes.
     |
     |  __init__(self, *args, **kwds)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce_ex__(self, proto)
     |      Helper for pickle.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from enum.Enum:
     |
     |  name
     |      The name of the Enum member.
     |
     |  value
     |      The value of the Enum member.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from enum.EnumType:
     |
     |  __getitem__(name)
     |      Return the member matching `name`.
     |
     |  ----------------------------------------------------------------------
     |  Readonly properties inherited from enum.EnumType:
     |
     |  __members__
     |      Returns a mapping of member name->value.
     |
     |      This mapping lists all enum members, including aliases. Note that this
     |      is a read-only view of the internal mapping.

    class DictionaryArray(Array)
     |  Concrete class for dictionary-encoded Arrow arrays.
     |
     |  Method resolution order:
     |      DictionaryArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  dictionary_decode(self)
     |      DictionaryArray.dictionary_decode(self)
     |
     |      Decodes the DictionaryArray to an Array.
     |
     |  dictionary_encode(self)
     |      DictionaryArray.dictionary_encode(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_arrays(indices, dictionary, mask=None, ordered=False, from_pandas=False, safe=True, memory_pool=None)
     |      DictionaryArray.from_arrays(indices, dictionary, mask=None, bool ordered=False, bool from_pandas=False, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Construct a DictionaryArray from indices and values.
     |
     |      Parameters
     |      ----------
     |      indices : pyarrow.Array, numpy.ndarray or pandas.Series, int type
     |          Non-negative integers referencing the dictionary values by zero
     |          based index.
     |      dictionary : pyarrow.Array, ndarray or pandas.Series
     |          The array of values referenced by the indices.
     |      mask : ndarray or pandas.Series, bool type
     |          True values indicate that indices are actually null.
     |      ordered : bool, default False
     |          Set to True if the category values are ordered.
     |      from_pandas : bool, default False
     |          If True, the indices should be treated as though they originated in
     |          a pandas.Categorical (null encoded as -1).
     |      safe : bool, default True
     |          If True, check that the dictionary indices are in range.
     |      memory_pool : MemoryPool, default None
     |          For memory allocations, if required, otherwise uses default pool.
     |
     |      Returns
     |      -------
     |      dict_array : DictionaryArray
     |
     |  from_buffers(type, length, buffers, dictionary, null_count=-1, offset=0)
     |      DictionaryArray.from_buffers(DataType type, int64_t length, buffers, Array dictionary, int64_t null_count=-1, int64_t offset=0)
     |
     |      Construct a DictionaryArray from buffers.
     |
     |      Parameters
     |      ----------
     |      type : pyarrow.DataType
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing the indices array.
     |      dictionary : pyarrow.Array, ndarray or pandas.Series
     |          The array of values referenced by the indices.
     |      null_count : int, default -1
     |          The number of null entries in the indices array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |
     |      Returns
     |      -------
     |      dict_array : DictionaryArray
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  dictionary
     |
     |  indices
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class DictionaryMemo(_Weakrefable)
     |  Tracking container for dictionary-encoded fields.
     |
     |  Method resolution order:
     |      DictionaryMemo
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__ = __reduce_cython__(...)
     |      DictionaryMemo.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      DictionaryMemo.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.

    class DictionaryScalar(Scalar)
     |  Concrete class for dictionary-encoded scalars.
     |
     |  Method resolution order:
     |      DictionaryScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      DictionaryScalar.__reduce__(self)
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      DictionaryScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this encoded value as a Python object.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  dictionary
     |
     |  index
     |      Return this value's underlying index as a scalar.
     |
     |  value
     |      Return the encoded value as a scalar.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class DictionaryType(DataType)
     |  Concrete class for dictionary data types.
     |
     |  Examples
     |  --------
     |  Create an instance of dictionary type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.dictionary(pa.int64(), pa.utf8())
     |  DictionaryType(dictionary<values=string, indices=int64, ordered=0>)
     |
     |  Method resolution order:
     |      DictionaryType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      DictionaryType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  index_type
     |      The data type of dictionary indices (a signed integer type).
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.dictionary(pa.int16(), pa.utf8()).index_type
     |      DataType(int16)
     |
     |  ordered
     |      Whether the dictionary is ordered, i.e. whether the ordering of values
     |      in the dictionary is important.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.dictionary(pa.int64(), pa.utf8()).ordered
     |      False
     |
     |  value_type
     |      The dictionary value type.
     |
     |      The dictionary values are found in an instance of DictionaryArray.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.dictionary(pa.int16(), pa.utf8()).value_type
     |      DataType(string)
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class DoubleArray(FloatingPointArray)
     |  Concrete class for Arrow arrays of float64 data type.
     |
     |  Method resolution order:
     |      DoubleArray
     |      FloatingPointArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class DoubleScalar(Scalar)
     |  Concrete class for double scalars.
     |
     |  Method resolution order:
     |      DoubleScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __float__(self, /)
     |      float(self)
     |
     |  __int__(self, /)
     |      int(self)
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      DoubleScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python float.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class DurationArray(NumericArray)
     |  Concrete class for Arrow arrays of duration data type.
     |
     |  Method resolution order:
     |      DurationArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class DurationScalar(Scalar)
     |  Concrete class for duration scalars.
     |
     |  Method resolution order:
     |      DurationScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      DurationScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Pandas Timedelta instance (if units are
     |      nanoseconds and pandas is available), otherwise as a Python
     |      datetime.timedelta instance.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class DurationType(DataType)
     |  Concrete class for duration data types.
     |
     |  Examples
     |  --------
     |  Create an instance of duration type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.duration('s')
     |  DurationType(duration[s])
     |
     |  Method resolution order:
     |      DurationType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  unit
     |      The duration unit ('s', 'ms', 'us' or 'ns').
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.duration('s')
     |      >>> t.unit
     |      's'
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      DataType.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class ExtensionArray(Array)
     |  Concrete class for Arrow extension arrays.
     |
     |  Method resolution order:
     |      ExtensionArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_storage(typ, storage)
     |      ExtensionArray.from_storage(BaseExtensionType typ, Array storage)
     |
     |      Construct ExtensionArray from type and storage array.
     |
     |      Parameters
     |      ----------
     |      typ : DataType
     |          The extension type for the result array.
     |      storage : Array
     |          The underlying storage for the result array.
     |
     |      Returns
     |      -------
     |      ext_array : ExtensionArray
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  storage
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class ExtensionScalar(Scalar)
     |  Concrete class for Extension scalars.
     |
     |  Method resolution order:
     |      ExtensionScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      ExtensionScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this scalar as a Python object.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_storage(typ, value)
     |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
     |
     |      Construct ExtensionScalar from type and storage value.
     |
     |      Parameters
     |      ----------
     |      typ : DataType
     |          The extension type for the result scalar.
     |      value : object
     |          The storage value for the result scalar.
     |
     |      Returns
     |      -------
     |      ext_scalar : ExtensionScalar
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value
     |      Return storage value as a scalar.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class ExtensionType(BaseExtensionType)
     |  ExtensionType(DataType storage_type, extension_name)
     |
     |  Concrete base class for Python-defined extension types.
     |
     |  Parameters
     |  ----------
     |  storage_type : DataType
     |      The underlying storage type for the extension type.
     |  extension_name : str
     |      A unique name distinguishing this extension type. The name will be
     |      used when deserializing IPC data.
     |
     |  Examples
     |  --------
     |  Define a RationalType extension type subclassing ExtensionType:
     |
     |  >>> import pyarrow as pa
     |  >>> class RationalType(pa.ExtensionType):
     |  ...     def __init__(self, data_type: pa.DataType):
     |  ...         if not pa.types.is_integer(data_type):
     |  ...             raise TypeError(f"data_type must be an integer type not {data_type}")
     |  ...         super().__init__(
     |  ...             pa.struct(
     |  ...                 [
     |  ...                     ("numer", data_type),
     |  ...                     ("denom", data_type),
     |  ...                 ],
     |  ...             ),
     |  ...             # N.B. This name does _not_ reference `data_type` so deserialization
     |  ...             # will work for _any_ integer `data_type` after registration
     |  ...             "my_package.rational",
     |  ...         )
     |  ...     def __arrow_ext_serialize__(self) -> bytes:
     |  ...         # No parameters are necessary
     |  ...         return b""
     |  ...     @classmethod
     |  ...     def __arrow_ext_deserialize__(cls, storage_type, serialized):
     |  ...         # return an instance of this subclass
     |  ...         return RationalType(storage_type[0].type)
     |
     |  Register the extension type:
     |
     |  >>> pa.register_extension_type(RationalType(pa.int64()))
     |
     |  Create an instance of RationalType extension type:
     |
     |  >>> rational_type = RationalType(pa.int32())
     |
     |  Inspect the extension type:
     |
     |  >>> rational_type.extension_name
     |  'my_package.rational'
     |  >>> rational_type.storage_type
     |  StructType(struct<numer: int32, denom: int32>)
     |
     |  Wrap an array as an extension array:
     |
     |  >>> storage_array = pa.array(
     |  ...     [
     |  ...         {"numer": 10, "denom": 17},
     |  ...         {"numer": 20, "denom": 13},
     |  ...     ],
     |  ...     type=rational_type.storage_type
     |  ... )
     |  >>> rational_array = rational_type.wrap_array(storage_array)
     |  >>> rational_array
     |  <pyarrow.lib.ExtensionArray object at ...>
     |  -- is_valid: all not null
     |  -- child 0 type: int32
     |    [
     |      10,
     |      20
     |    ]
     |  -- child 1 type: int32
     |    [
     |      17,
     |      13
     |    ]
     |
     |  Or do the same with creating an ExtensionArray:
     |
     |  >>> rational_array = pa.ExtensionArray.from_storage(rational_type, storage_array)
     |  >>> rational_array
     |  <pyarrow.lib.ExtensionArray object at ...>
     |  -- is_valid: all not null
     |  -- child 0 type: int32
     |    [
     |      10,
     |      20
     |    ]
     |  -- child 1 type: int32
     |    [
     |      17,
     |      13
     |    ]
     |
     |  Unregister the extension type:
     |
     |  >>> pa.unregister_extension_type("my_package.rational")
     |
     |  Note that even though we registered the concrete type
     |  ``RationalType(pa.int64())``, PyArrow will be able to deserialize
     |  ``RationalType(integer_type)`` for any ``integer_type``, as the deserializer
     |  will reference the name ``my_package.rational`` and the ``@classmethod``
     |  ``__arrow_ext_deserialize__``.
     |
     |  Method resolution order:
     |      ExtensionType
     |      BaseExtensionType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_ext_class__(self)
     |      ExtensionType.__arrow_ext_class__(self)
     |
     |      Return an extension array class to be used for building or
     |      deserializing arrays with this extension type.
     |
     |      This method should return a subclass of the ExtensionArray class. By
     |      default, if not specialized in the extension implementation, an
     |      extension type array will be a built-in ExtensionArray instance.
     |
     |  __arrow_ext_scalar_class__(self)
     |      ExtensionType.__arrow_ext_scalar_class__(self)
     |
     |      Return an extension scalar class for building scalars with this
     |      extension type.
     |
     |      This method should return subclass of the ExtensionScalar class. By
     |      default, if not specialized in the extension implementation, an
     |      extension type scalar will be a built-in ExtensionScalar instance.
     |
     |  __arrow_ext_serialize__(self)
     |      ExtensionType.__arrow_ext_serialize__(self)
     |
     |      Serialized representation of metadata to reconstruct the type object.
     |
     |      This method should return a bytes object, and those serialized bytes
     |      are stored in the custom metadata of the Field holding an extension
     |      type in an IPC message.
     |      The bytes are passed to ``__arrow_ext_deserialize`` and should hold
     |      sufficient information to reconstruct the data type instance.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(...)
     |      Initialize an extension type instance.
     |
     |      This should be called at the end of the subclass'
     |      ``__init__`` method.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      ExtensionType.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  ----------------------------------------------------------------------
     |  Class methods defined here:
     |
     |  __arrow_ext_deserialize__(storage_type, serialized)
     |      ExtensionType.__arrow_ext_deserialize__(cls, storage_type, serialized)
     |
     |      Return an extension type instance from the storage type and serialized
     |      metadata.
     |
     |      This method should return an instance of the ExtensionType subclass
     |      that matches the passed storage type and serialized metadata (the
     |      return value of ``__arrow_ext_serialize__``).
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseExtensionType:
     |
     |  wrap_array(self, storage)
     |      BaseExtensionType.wrap_array(self, storage)
     |
     |      Wrap the given storage array as an extension array.
     |
     |      Parameters
     |      ----------
     |      storage : Array or ChunkedArray
     |
     |      Returns
     |      -------
     |      array : Array or ChunkedArray
     |          Extension array wrapping the storage array
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from BaseExtensionType:
     |
     |  bit_width
     |      The bit width of the extension type.
     |
     |  byte_width
     |      The byte width of the extension type.
     |
     |  extension_name
     |      The extension type name.
     |
     |  storage_type
     |      The underlying storage type.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class Field(_Weakrefable)
     |  Field()
     |
     |  A named field, with a data type, nullability, and optional metadata.
     |
     |  Notes
     |  -----
     |  Do not use this class's constructor directly; use pyarrow.field
     |
     |  Examples
     |  --------
     |  Create an instance of pyarrow.Field:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.field('key', pa.int32())
     |  pyarrow.Field<key: int32>
     |  >>> pa.field('key', pa.int32(), nullable=False)
     |  pyarrow.Field<key: int32 not null>
     |  >>> field = pa.field('key', pa.int32(),
     |  ...                  metadata={"key": "Something important"})
     |  >>> field
     |  pyarrow.Field<key: int32>
     |  >>> field.metadata
     |  {b'key': b'Something important'}
     |
     |  Use the field to create a struct type:
     |
     |  >>> pa.struct([field])
     |  StructType(struct<key: int32>)
     |
     |  Method resolution order:
     |      Field
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_c_schema__(self)
     |      Field.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      Field.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, check_metadata=False)
     |      Field.equals(self, Field other, bool check_metadata=False)
     |
     |      Test if this field is equal to the other
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Field
     |      check_metadata : bool, default False
     |          Whether Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> f1 = pa.field('key', pa.int32())
     |      >>> f2 = pa.field('key', pa.int32(), nullable=False)
     |      >>> f1.equals(f2)
     |      False
     |      >>> f1.equals(f1)
     |      True
     |
     |  flatten(self)
     |      Field.flatten(self)
     |
     |      Flatten this field.  If a struct field, individual child fields
     |      will be returned with their names prefixed by the parent's name.
     |
     |      Returns
     |      -------
     |      fields : List[pyarrow.Field]
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> f1 = pa.field('bar', pa.float64(), nullable=False)
     |      >>> f2 = pa.field('foo', pa.int32()).with_metadata({"key": "Something important"})
     |      >>> ff = pa.field('ff', pa.struct([f1, f2]), nullable=False)
     |
     |      Flatten a struct field:
     |
     |      >>> ff
     |      pyarrow.Field<ff: struct<bar: double not null, foo: int32> not null>
     |      >>> ff.flatten()
     |      [pyarrow.Field<ff.bar: double not null>, pyarrow.Field<ff.foo: int32>]
     |
     |  remove_metadata(self)
     |      Field.remove_metadata(self)
     |
     |      Create new field without metadata, if any
     |
     |      Returns
     |      -------
     |      field : pyarrow.Field
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> field = pa.field('key', pa.int32(),
     |      ...                  metadata={"key": "Something important"})
     |      >>> field.metadata
     |      {b'key': b'Something important'}
     |
     |      Create new field by removing the metadata from the existing one:
     |
     |      >>> field_new = field.remove_metadata()
     |      >>> field_new.metadata
     |
     |  with_metadata(self, metadata)
     |      Field.with_metadata(self, metadata)
     |
     |      Add metadata as dict of string keys and values to Field
     |
     |      Parameters
     |      ----------
     |      metadata : dict
     |          Keys and values must be string-like / coercible to bytes
     |
     |      Returns
     |      -------
     |      field : pyarrow.Field
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> field = pa.field('key', pa.int32())
     |
     |      Create new field by adding metadata to existing one:
     |
     |      >>> field_new = field.with_metadata({"key": "Something important"})
     |      >>> field_new
     |      pyarrow.Field<key: int32>
     |      >>> field_new.metadata
     |      {b'key': b'Something important'}
     |
     |  with_name(self, name)
     |      Field.with_name(self, name)
     |
     |      A copy of this field with the replaced name
     |
     |      Parameters
     |      ----------
     |      name : str
     |
     |      Returns
     |      -------
     |      field : pyarrow.Field
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> field = pa.field('key', pa.int32())
     |      >>> field
     |      pyarrow.Field<key: int32>
     |
     |      Create new field by replacing the name of an existing one:
     |
     |      >>> field_new = field.with_name('lock')
     |      >>> field_new
     |      pyarrow.Field<lock: int32>
     |
     |  with_nullable(self, nullable)
     |      Field.with_nullable(self, nullable)
     |
     |      A copy of this field with the replaced nullability
     |
     |      Parameters
     |      ----------
     |      nullable : bool
     |
     |      Returns
     |      -------
     |      field: pyarrow.Field
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> field = pa.field('key', pa.int32())
     |      >>> field
     |      pyarrow.Field<key: int32>
     |      >>> field.nullable
     |      True
     |
     |      Create new field by replacing the nullability of an existing one:
     |
     |      >>> field_new = field.with_nullable(False)
     |      >>> field_new
     |      pyarrow.Field<key: int32 not null>
     |      >>> field_new.nullable
     |      False
     |
     |  with_type(self, new_type)
     |      Field.with_type(self, DataType new_type)
     |
     |      A copy of this field with the replaced type
     |
     |      Parameters
     |      ----------
     |      new_type : pyarrow.DataType
     |
     |      Returns
     |      -------
     |      field : pyarrow.Field
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> field = pa.field('key', pa.int32())
     |      >>> field
     |      pyarrow.Field<key: int32>
     |
     |      Create new field by replacing type of an existing one:
     |
     |      >>> field_new = field.with_type(pa.int64())
     |      >>> field_new
     |      pyarrow.Field<key: int64>
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  metadata
     |      The field metadata (if any is set).
     |
     |      Returns
     |      -------
     |      metadata : dict or None
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> field = pa.field('key', pa.int32(),
     |      ...                  metadata={"key": "Something important"})
     |      >>> field.metadata
     |      {b'key': b'Something important'}
     |
     |  name
     |      The field name.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> field = pa.field('key', pa.int32())
     |      >>> field.name
     |      'key'
     |
     |  nullable
     |      The field nullability.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> f1 = pa.field('key', pa.int32())
     |      >>> f2 = pa.field('key', pa.int32(), nullable=False)
     |      >>> f1.nullable
     |      True
     |      >>> f2.nullable
     |      False
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class FixedShapeTensorArray(ExtensionArray)
     |  Concrete class for fixed shape tensor extension arrays.
     |
     |  Examples
     |  --------
     |  Define the extension type for tensor array
     |
     |  >>> import pyarrow as pa
     |  >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), [2, 2])
     |
     |  Create an extension array
     |
     |  >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]]
     |  >>> storage = pa.array(arr, pa.list_(pa.int32(), 4))
     |  >>> pa.ExtensionArray.from_storage(tensor_type, storage)
     |  <pyarrow.lib.FixedShapeTensorArray object at ...>
     |  [
     |    [
     |      1,
     |      2,
     |      3,
     |      4
     |    ],
     |    [
     |      10,
     |      20,
     |      30,
     |      40
     |    ],
     |    [
     |      100,
     |      200,
     |      300,
     |      400
     |    ]
     |  ]
     |
     |  Method resolution order:
     |      FixedShapeTensorArray
     |      ExtensionArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  to_numpy_ndarray(self)
     |      FixedShapeTensorArray.to_numpy_ndarray(self)
     |
     |      Convert fixed shape tensor extension array to a multi-dimensional numpy.ndarray.
     |
     |      The resulting ndarray will have (ndim + 1) dimensions.
     |      The size of the first dimension will be the length of the fixed shape tensor array
     |      and the rest of the dimensions will match the permuted shape of the fixed
     |      shape tensor.
     |
     |      The conversion is zero-copy.
     |
     |      Returns
     |      -------
     |      numpy.ndarray
     |          Ndarray representing tensors in the fixed shape tensor array concatenated
     |          along the first dimension.
     |
     |  to_tensor(self)
     |      FixedShapeTensorArray.to_tensor(self)
     |
     |      Convert fixed shape tensor extension array to a pyarrow.Tensor.
     |
     |      The resulting Tensor will have (ndim + 1) dimensions.
     |      The size of the first dimension will be the length of the fixed shape tensor array
     |      and the rest of the dimensions will match the permuted shape of the fixed
     |      shape tensor.
     |
     |      The conversion is zero-copy.
     |
     |      Returns
     |      -------
     |      pyarrow.Tensor
     |          Tensor representing tensors in the fixed shape tensor array concatenated
     |          along the first dimension.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_numpy_ndarray(obj, dim_names=None)
     |      FixedShapeTensorArray.from_numpy_ndarray(obj, dim_names=None)
     |
     |      Convert numpy tensors (ndarrays) to a fixed shape tensor extension array.
     |      The first dimension of ndarray will become the length of the fixed
     |      shape tensor array.
     |      If input array data is not contiguous a copy will be made.
     |
     |      Parameters
     |      ----------
     |      obj : numpy.ndarray
     |      dim_names : tuple or list of strings, default None
     |          Explicit names to tensor dimensions.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> arr = np.array(
     |      ...         [[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]],
     |      ...         dtype=np.float32)
     |      >>> pa.FixedShapeTensorArray.from_numpy_ndarray(arr)
     |      <pyarrow.lib.FixedShapeTensorArray object at ...>
     |      [
     |        [
     |          1,
     |          2,
     |          3,
     |          4,
     |          5,
     |          6
     |        ],
     |        [
     |          1,
     |          2,
     |          3,
     |          4,
     |          5,
     |          6
     |        ]
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from ExtensionArray:
     |
     |  from_storage(typ, storage)
     |      ExtensionArray.from_storage(BaseExtensionType typ, Array storage)
     |
     |      Construct ExtensionArray from type and storage array.
     |
     |      Parameters
     |      ----------
     |      typ : DataType
     |          The extension type for the result array.
     |      storage : Array
     |          The underlying storage for the result array.
     |
     |      Returns
     |      -------
     |      ext_array : ExtensionArray
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ExtensionArray:
     |
     |  storage
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class FixedShapeTensorScalar(ExtensionScalar)
     |  Concrete class for fixed shape tensor extension scalar.
     |
     |  Method resolution order:
     |      FixedShapeTensorScalar
     |      ExtensionScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  to_numpy(self)
     |      FixedShapeTensorScalar.to_numpy(self)
     |
     |      Convert fixed shape tensor scalar to a numpy.ndarray.
     |
     |      The resulting ndarray's shape matches the permuted shape of the
     |      fixed shape tensor scalar.
     |      The conversion is zero-copy.
     |
     |      Returns
     |      -------
     |      numpy.ndarray
     |
     |  to_tensor(self)
     |      FixedShapeTensorScalar.to_tensor(self)
     |
     |      Convert fixed shape tensor extension scalar to a pyarrow.Tensor, using shape
     |      and strides derived from corresponding FixedShapeTensorType.
     |
     |      The conversion is zero-copy.
     |
     |      Returns
     |      -------
     |      pyarrow.Tensor
     |          Tensor represented stored in FixedShapeTensorScalar.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from ExtensionScalar:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      ExtensionScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this scalar as a Python object.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from ExtensionScalar:
     |
     |  from_storage(typ, value)
     |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
     |
     |      Construct ExtensionScalar from type and storage value.
     |
     |      Parameters
     |      ----------
     |      typ : DataType
     |          The extension type for the result scalar.
     |      value : object
     |          The storage value for the result scalar.
     |
     |      Returns
     |      -------
     |      ext_scalar : ExtensionScalar
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ExtensionScalar:
     |
     |  value
     |      Return storage value as a scalar.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class FixedShapeTensorType(BaseExtensionType)
     |  Concrete class for fixed shape tensor extension type.
     |
     |  Examples
     |  --------
     |  Create an instance of fixed shape tensor extension type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.fixed_shape_tensor(pa.int32(), [2, 2])
     |  FixedShapeTensorType(extension<arrow.fixed_shape_tensor[value_type=int32, shape=[2,2]]>)
     |
     |  Create an instance of fixed shape tensor extension type with
     |  permutation:
     |
     |  >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3),
     |  ...                                     permutation=[0, 2, 1])
     |  >>> tensor_type.permutation
     |  [0, 2, 1]
     |
     |  Method resolution order:
     |      FixedShapeTensorType
     |      BaseExtensionType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_ext_class__(self)
     |      FixedShapeTensorType.__arrow_ext_class__(self)
     |
     |  __arrow_ext_scalar_class__(self)
     |      FixedShapeTensorType.__arrow_ext_scalar_class__(self)
     |
     |  __reduce__(self)
     |      FixedShapeTensorType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  dim_names
     |      Explicit names of the dimensions.
     |
     |  permutation
     |      Indices of the dimensions ordering.
     |
     |  shape
     |      Shape of the tensors.
     |
     |  value_type
     |      Data type of an individual tensor.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseExtensionType:
     |
     |  wrap_array(self, storage)
     |      BaseExtensionType.wrap_array(self, storage)
     |
     |      Wrap the given storage array as an extension array.
     |
     |      Parameters
     |      ----------
     |      storage : Array or ChunkedArray
     |
     |      Returns
     |      -------
     |      array : Array or ChunkedArray
     |          Extension array wrapping the storage array
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from BaseExtensionType:
     |
     |  bit_width
     |      The bit width of the extension type.
     |
     |  byte_width
     |      The byte width of the extension type.
     |
     |  extension_name
     |      The extension type name.
     |
     |  storage_type
     |      The underlying storage type.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class FixedSizeBinaryArray(Array)
     |  Concrete class for Arrow arrays of a fixed-size binary data type.
     |
     |  Method resolution order:
     |      FixedSizeBinaryArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class FixedSizeBinaryScalar(BinaryScalar)
     |  Method resolution order:
     |      FixedSizeBinaryScalar
     |      BinaryScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BinaryScalar:
     |
     |  __buffer__(self, flags, /)
     |      Return a buffer object that exposes the underlying memory of the object.
     |
     |  __bytes__(self)
     |      BinaryScalar.__bytes__(self)
     |
     |  as_buffer(self)
     |      BinaryScalar.as_buffer(self)
     |
     |      Return a view over this value as a Buffer object.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      BinaryScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python bytes.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class FixedSizeBinaryType(DataType)
     |  Concrete class for fixed-size binary data types.
     |
     |  Examples
     |  --------
     |  Create an instance of fixed-size binary type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.binary(3)
     |  FixedSizeBinaryType(fixed_size_binary[3])
     |
     |  Method resolution order:
     |      FixedSizeBinaryType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      FixedSizeBinaryType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class FixedSizeBufferWriter(NativeFile)
     |  A stream writing to a Arrow buffer.
     |
     |  Examples
     |  --------
     |  Create a stream to write to ``pyarrow.Buffer``:
     |
     |  >>> import pyarrow as pa
     |  >>> buf = pa.allocate_buffer(5)
     |  >>> with pa.output_stream(buf) as stream:
     |  ...     stream.write(b'abcde')
     |  ...     stream
     |  ...
     |  5
     |  <pyarrow.FixedSizeBufferWriter closed=False own_file=False is_seekable=False is_writable=True is_readable=False>
     |
     |  Inspect the buffer:
     |
     |  >>> buf.to_pybytes()
     |  b'abcde'
     |  >>> buf
     |  <pyarrow.Buffer address=... size=5 is_cpu=True is_mutable=True>
     |
     |  Method resolution order:
     |      FixedSizeBufferWriter
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__ = __reduce_cython__(...)
     |      FixedSizeBufferWriter.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      FixedSizeBufferWriter.__setstate_cython__(self, __pyx_state)
     |
     |  set_memcopy_blocksize(self, blocksize)
     |      FixedSizeBufferWriter.set_memcopy_blocksize(self, int64_t blocksize)
     |
     |      Parameters
     |      ----------
     |      blocksize : int64
     |
     |  set_memcopy_threads(self, num_threads)
     |      FixedSizeBufferWriter.set_memcopy_threads(self, int num_threads)
     |
     |      Parameters
     |      ----------
     |      num_threads : int
     |
     |  set_memcopy_threshold(self, threshold)
     |      FixedSizeBufferWriter.set_memcopy_threshold(self, int64_t threshold)
     |
     |      Parameters
     |      ----------
     |      threshold : int64
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  fileno(self)
     |      NativeFile.fileno(self)
     |
     |      NOT IMPLEMENTED
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class FixedSizeListArray(BaseListArray)
     |  Concrete class for Arrow arrays of a fixed size list data type.
     |
     |  Method resolution order:
     |      FixedSizeListArray
     |      BaseListArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_arrays(values, list_size=None, type=None, mask=None)
     |      FixedSizeListArray.from_arrays(values, list_size=None, DataType type=None, mask=None)
     |
     |      Construct FixedSizeListArray from array of values and a list length.
     |
     |      Parameters
     |      ----------
     |      values : Array (any type)
     |      list_size : int
     |          The fixed length of the lists.
     |      type : DataType, optional
     |          If not specified, a default ListType with the values' type and
     |          `list_size` length is used.
     |      mask : Array (boolean type), optional
     |          Indicate which values are null (True) or not null (False).
     |
     |
     |      Returns
     |      -------
     |      FixedSizeListArray
     |
     |      Examples
     |      --------
     |
     |      Create from a values array and a list size:
     |
     |      >>> import pyarrow as pa
     |      >>> values = pa.array([1, 2, 3, 4])
     |      >>> arr = pa.FixedSizeListArray.from_arrays(values, 2)
     |      >>> arr
     |      <pyarrow.lib.FixedSizeListArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        [
     |          3,
     |          4
     |        ]
     |      ]
     |
     |      Or create from a values array, list size and matching type:
     |
     |      >>> typ = pa.list_(pa.field("values", pa.int64()), 2)
     |      >>> arr = pa.FixedSizeListArray.from_arrays(values,type=typ)
     |      >>> arr
     |      <pyarrow.lib.FixedSizeListArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        [
     |          3,
     |          4
     |        ]
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  values
     |      Return the underlying array of values which backs the
     |      FixedSizeListArray ignoring the array's offset.
     |
     |      Note even null elements are included.
     |
     |      Compare with :meth:`flatten`, which returns only the non-null
     |      sub-list values.
     |
     |      Returns
     |      -------
     |      values : Array
     |
     |      See Also
     |      --------
     |      FixedSizeListArray.flatten : ...
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> array = pa.array(
     |      ...     [[1, 2], None, [3, None]],
     |      ...     type=pa.list_(pa.int32(), 2)
     |      ... )
     |      >>> array.values
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        1,
     |        2,
     |        null,
     |        null,
     |        3,
     |        null
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseListArray:
     |
     |  flatten(self, recursive=False)
     |      BaseListArray.flatten(self, recursive=False)
     |
     |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
     |      according to 'recursive'.
     |
     |      Note that this method is different from ``self.values`` in that
     |      it takes care of the slicing offset as well as null elements backed
     |      by non-empty sub-lists.
     |
     |      Parameters
     |      ----------
     |      recursive : bool, default False, optional
     |          When True, flatten this logical list-array recursively until an
     |          array of non-list values is formed.
     |
     |          When False, flatten only the top level.
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |      Examples
     |      --------
     |
     |      Basic logical list-array's flatten
     |
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, 3, 4]
     |      >>> offsets = [2, 1, 0]
     |      >>> sizes = [2, 2, 2]
     |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array
     |      <pyarrow.lib.ListViewArray object at ...>
     |      [
     |        [
     |          3,
     |          4
     |        ],
     |        [
     |          2,
     |          3
     |        ],
     |        [
     |          1,
     |          2
     |        ]
     |      ]
     |      >>> array.flatten()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        3,
     |        4,
     |        2,
     |        3,
     |        1,
     |        2
     |      ]
     |
     |      When recursive=True, nested list arrays are flattened recursively
     |      until an array of non-list values is formed.
     |
     |      >>> array = pa.array([
     |      ...    None,
     |      ...    [
     |      ...        [1, None, 2],
     |      ...        None,
     |      ...        [3, 4]
     |      ...    ],
     |      ...    [],
     |      ...    [
     |      ...        [],
     |      ...        [5, 6],
     |      ...        None
     |      ...    ],
     |      ...    [
     |      ...        [7, 8]
     |      ...    ]
     |      ... ], type=pa.list_(pa.list_(pa.int64())))
     |      >>> array.flatten(True)
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        null,
     |        2,
     |        3,
     |        4,
     |        5,
     |        6,
     |        7,
     |        8
     |      ]
     |
     |  value_lengths(self)
     |      BaseListArray.value_lengths(self)
     |
     |      Return integers array with values equal to the respective length of
     |      each list element. Null list values are null in the output.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_lengths()
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        3,
     |        0,
     |        null,
     |        1
     |      ]
     |
     |  value_parent_indices(self)
     |      BaseListArray.value_parent_indices(self)
     |
     |      Return array of same length as list child values array where each
     |      output value is the index of the parent list array slot containing each
     |      child value.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_parent_indices()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        0,
     |        0,
     |        0,
     |        3
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class FixedSizeListScalar(ListScalar)
     |  Method resolution order:
     |      FixedSizeListScalar
     |      ListScalar
     |      Scalar
     |      _Weakrefable
     |      collections.abc.Sequence
     |      collections.abc.Reversible
     |      collections.abc.Collection
     |      collections.abc.Sized
     |      collections.abc.Iterable
     |      collections.abc.Container
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from ListScalar:
     |
     |  __getitem__(...)
     |      ListScalar.__getitem__(self, i)
     |
     |      Return the value at the given index.
     |
     |  __iter__(...)
     |      ListScalar.__iter__(self)
     |
     |      Iterate over this element's values.
     |
     |  __len__(...)
     |      ListScalar.__len__(self)
     |
     |      Return the number of values.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      ListScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python list.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ListScalar:
     |
     |  values
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from collections.abc.Sequence:
     |
     |  __contains__(self, value)
     |
     |  __reversed__(self)
     |
     |  count(self, value)
     |      S.count(value) -> integer -- return number of occurrences of value
     |
     |  index(self, value, start=0, stop=None)
     |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
     |      Raises ValueError if the value is not present.
     |
     |      Supporting start and stop arguments is optional, but
     |      recommended.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from collections.abc.Sequence:
     |
     |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Reversible:
     |
     |  __subclasshook__(C)
     |      Abstract classes can override this to customize issubclass().
     |
     |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
     |      It should return True, False or NotImplemented.  If it returns
     |      NotImplemented, the normal algorithm is used.  Otherwise, it
     |      overrides the normal algorithm (and the outcome is cached).
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Iterable:
     |
     |  __class_getitem__ = GenericAlias(...)
     |      Represent a PEP 585 generic type
     |
     |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

    class FixedSizeListType(DataType)
     |  Concrete class for fixed size list data types.
     |
     |  Examples
     |  --------
     |  Create an instance of FixedSizeListType:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.list_(pa.int32(), 2)
     |  FixedSizeListType(fixed_size_list<item: int32>[2])
     |
     |  Method resolution order:
     |      FixedSizeListType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      FixedSizeListType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  list_size
     |      The size of the fixed size lists.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.list_(pa.int32(), 2).list_size
     |      2
     |
     |  value_field
     |      The field for list values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.list_(pa.int32(), 2).value_field
     |      pyarrow.Field<item: int32>
     |
     |  value_type
     |      The data type of large list values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.list_(pa.int32(), 2).value_type
     |      DataType(int32)
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class FloatArray(FloatingPointArray)
     |  Concrete class for Arrow arrays of float32 data type.
     |
     |  Method resolution order:
     |      FloatArray
     |      FloatingPointArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class FloatScalar(Scalar)
     |  Concrete class for float scalars.
     |
     |  Method resolution order:
     |      FloatScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __float__(self, /)
     |      float(self)
     |
     |  __int__(self, /)
     |      int(self)
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      FloatScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python float.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class FloatingPointArray(NumericArray)
     |  A base class for Arrow floating-point arrays.
     |
     |  Method resolution order:
     |      FloatingPointArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class HalfFloatArray(FloatingPointArray)
     |  Concrete class for Arrow arrays of float16 data type.
     |
     |  Method resolution order:
     |      HalfFloatArray
     |      FloatingPointArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class HalfFloatScalar(Scalar)
     |  Concrete class for float scalars.
     |
     |  Method resolution order:
     |      HalfFloatScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __float__(self, /)
     |      float(self)
     |
     |  __int__(self, /)
     |      int(self)
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      HalfFloatScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python float.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Int16Array(IntegerArray)
     |  Concrete class for Arrow arrays of int16 data type.
     |
     |  Method resolution order:
     |      Int16Array
     |      IntegerArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Int16Scalar(Scalar)
     |  Concrete class for int16 scalars.
     |
     |  Method resolution order:
     |      Int16Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Int16Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python int.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Int32Array(IntegerArray)
     |  Concrete class for Arrow arrays of int32 data type.
     |
     |  Method resolution order:
     |      Int32Array
     |      IntegerArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Int32Scalar(Scalar)
     |  Concrete class for int32 scalars.
     |
     |  Method resolution order:
     |      Int32Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Int32Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python int.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Int64Array(IntegerArray)
     |  Concrete class for Arrow arrays of int64 data type.
     |
     |  Method resolution order:
     |      Int64Array
     |      IntegerArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Int64Scalar(Scalar)
     |  Concrete class for int64 scalars.
     |
     |  Method resolution order:
     |      Int64Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Int64Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python int.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Int8Array(IntegerArray)
     |  Concrete class for Arrow arrays of int8 data type.
     |
     |  Method resolution order:
     |      Int8Array
     |      IntegerArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Int8Scalar(Scalar)
     |  Concrete class for int8 scalars.
     |
     |  Method resolution order:
     |      Int8Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Int8Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python int.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class IntegerArray(NumericArray)
     |  A base class for Arrow integer arrays.
     |
     |  Method resolution order:
     |      IntegerArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class IpcReadOptions(_Weakrefable)
     |  IpcReadOptions(bool ensure_native_endian=True, *, Alignment ensure_alignment=Alignment.Any, bool use_threads=True, list included_fields=None)
     |
     |  Serialization options for reading IPC format.
     |
     |  Parameters
     |  ----------
     |  ensure_native_endian : bool, default True
     |      Whether to convert incoming data to platform-native endianness.
     |  ensure_alignment : Alignment, default Alignment.Any
     |      Data is copied to aligned memory locations if mis-aligned.
     |      Some use cases might require data to have a specific alignment, for example,
     |      for the data buffer of an int32 array to be aligned on a 4-byte boundary.
     |  use_threads : bool
     |      Whether to use the global CPU thread pool to parallelize any
     |      computational tasks like decompression
     |  included_fields : list
     |      If empty (the default), return all deserialized fields.
     |      If non-empty, the values are the indices of fields to read on
     |      the top-level schema
     |
     |  Method resolution order:
     |      IpcReadOptions
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      IpcReadOptions.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      IpcReadOptions.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  ensure_alignment
     |
     |  ensure_native_endian
     |
     |  included_fields
     |
     |  use_threads

    class IpcWriteOptions(_Weakrefable)
     |  IpcWriteOptions(metadata_version=MetadataVersion.V5, *, bool allow_64bit=False, use_legacy_format=False, compression=None, bool use_threads=True, bool emit_dictionary_deltas=False, bool unify_dictionaries=False)
     |
     |  Serialization options for the IPC format.
     |
     |  Parameters
     |  ----------
     |  metadata_version : MetadataVersion, default MetadataVersion.V5
     |      The metadata version to write.  V5 is the current and latest,
     |      V4 is the pre-1.0 metadata version (with incompatible Union layout).
     |  allow_64bit : bool, default False
     |      If true, allow field lengths that don't fit in a signed 32-bit int.
     |  use_legacy_format : bool, default False
     |      Whether to use the pre-Arrow 0.15 IPC format.
     |  compression : str, Codec, or None
     |      compression codec to use for record batch buffers.
     |      If None then batch buffers will be uncompressed.
     |      Must be "lz4", "zstd" or None.
     |      To specify a compression_level use `pyarrow.Codec`
     |  use_threads : bool
     |      Whether to use the global CPU thread pool to parallelize any
     |      computational tasks like compression.
     |  emit_dictionary_deltas : bool
     |      Whether to emit dictionary deltas.  Default is false for maximum
     |      stream compatibility.
     |  unify_dictionaries : bool
     |      If true then calls to write_table will attempt to unify dictionaries
     |      across all batches in the table.  This can help avoid the need for
     |      replacement dictionaries (which the file format does not support)
     |      but requires computing the unified dictionary and then remapping
     |      the indices arrays.
     |
     |      This parameter is ignored when writing to the IPC stream format as
     |      the IPC stream format can support replacement dictionaries.
     |
     |  Method resolution order:
     |      IpcWriteOptions
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      IpcWriteOptions.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      IpcWriteOptions.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  allow_64bit
     |
     |  compression
     |
     |  emit_dictionary_deltas
     |
     |  metadata_version
     |
     |  unify_dictionaries
     |
     |  use_legacy_format
     |
     |  use_threads

    class JsonArray(ExtensionArray)
     |  Concrete class for Arrow arrays of JSON data type.
     |
     |  This does not guarantee that the JSON data actually
     |  is valid JSON.
     |
     |  Examples
     |  --------
     |  Define the extension type for JSON array
     |
     |  >>> import pyarrow as pa
     |  >>> json_type = pa.json_(pa.large_utf8())
     |
     |  Create an extension array
     |
     |  >>> arr = [None, '{ "id":30, "values":["a", "b"] }']
     |  >>> storage = pa.array(arr, pa.large_utf8())
     |  >>> pa.ExtensionArray.from_storage(json_type, storage)
     |  <pyarrow.lib.JsonArray object at ...>
     |  [
     |    null,
     |    "{ "id":30, "values":["a", "b"] }"
     |  ]
     |
     |  Method resolution order:
     |      JsonArray
     |      ExtensionArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Data descriptors defined here:
     |
     |  __dict__
     |      dictionary for instance variables
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from ExtensionArray:
     |
     |  __new__(*args, **kwargs) class method of ExtensionArray
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_storage(typ, storage)
     |      ExtensionArray.from_storage(BaseExtensionType typ, Array storage)
     |
     |      Construct ExtensionArray from type and storage array.
     |
     |      Parameters
     |      ----------
     |      typ : DataType
     |          The extension type for the result array.
     |      storage : Array
     |          The underlying storage for the result array.
     |
     |      Returns
     |      -------
     |      ext_array : ExtensionArray
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ExtensionArray:
     |
     |  storage
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from ExtensionArray:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class JsonScalar(ExtensionScalar)
     |  Concrete class for JSON extension scalar.
     |
     |  Method resolution order:
     |      JsonScalar
     |      ExtensionScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Data descriptors defined here:
     |
     |  __dict__
     |      dictionary for instance variables
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from ExtensionScalar:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      ExtensionScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this scalar as a Python object.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from ExtensionScalar:
     |
     |  __new__(*args, **kwargs) class method of ExtensionScalar
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_storage(typ, value)
     |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
     |
     |      Construct ExtensionScalar from type and storage value.
     |
     |      Parameters
     |      ----------
     |      typ : DataType
     |          The extension type for the result scalar.
     |      value : object
     |          The storage value for the result scalar.
     |
     |      Returns
     |      -------
     |      ext_scalar : ExtensionScalar
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ExtensionScalar:
     |
     |  value
     |      Return storage value as a scalar.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from ExtensionScalar:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class JsonType(BaseExtensionType)
     |  Concrete class for JSON extension type.
     |
     |  Examples
     |  --------
     |  Define the extension type for JSON array
     |
     |  >>> import pyarrow as pa
     |  >>> json_type = pa.json_(pa.large_utf8())
     |
     |  Create an extension array
     |
     |  >>> arr = [None, '{ "id":30, "values":["a", "b"] }']
     |  >>> storage = pa.array(arr, pa.large_utf8())
     |  >>> pa.ExtensionArray.from_storage(json_type, storage)
     |  <pyarrow.lib.JsonArray object at ...>
     |  [
     |    null,
     |    "{ "id":30, "values":["a", "b"] }"
     |  ]
     |
     |  Method resolution order:
     |      JsonType
     |      BaseExtensionType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_ext_class__(self)
     |      JsonType.__arrow_ext_class__(self)
     |
     |  __arrow_ext_scalar_class__(self)
     |      JsonType.__arrow_ext_scalar_class__(self)
     |
     |  __reduce__(self)
     |      JsonType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseExtensionType:
     |
     |  wrap_array(self, storage)
     |      BaseExtensionType.wrap_array(self, storage)
     |
     |      Wrap the given storage array as an extension array.
     |
     |      Parameters
     |      ----------
     |      storage : Array or ChunkedArray
     |
     |      Returns
     |      -------
     |      array : Array or ChunkedArray
     |          Extension array wrapping the storage array
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from BaseExtensionType:
     |
     |  bit_width
     |      The bit width of the extension type.
     |
     |  byte_width
     |      The byte width of the extension type.
     |
     |  extension_name
     |      The extension type name.
     |
     |  storage_type
     |      The underlying storage type.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class KeyValueMetadata(_Metadata, collections.abc.Mapping)
     |  KeyValueMetadata(__arg0__=None, **kwargs)
     |
     |  KeyValueMetadata
     |
     |  Parameters
     |  ----------
     |  __arg0__ : dict
     |      A dict of the key-value metadata
     |  **kwargs : optional
     |      additional key-value metadata
     |
     |  Method resolution order:
     |      KeyValueMetadata
     |      _Metadata
     |      _Weakrefable
     |      collections.abc.Mapping
     |      collections.abc.Collection
     |      collections.abc.Sized
     |      collections.abc.Iterable
     |      collections.abc.Container
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __contains__(self, key, /)
     |      Return bool(key in self).
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(self, key, /)
     |      Return self[key].
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      KeyValueMetadata.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other)
     |      KeyValueMetadata.equals(self, KeyValueMetadata other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.KeyValueMetadata
     |
     |      Returns
     |      -------
     |      bool
     |
     |  get_all(self, key)
     |      KeyValueMetadata.get_all(self, key)
     |
     |      Parameters
     |      ----------
     |      key : str
     |
     |      Returns
     |      -------
     |      list[byte]
     |
     |  items(self)
     |      KeyValueMetadata.items(self)
     |
     |  key(self, i)
     |      KeyValueMetadata.key(self, i)
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      byte
     |
     |  keys(self)
     |      KeyValueMetadata.keys(self)
     |
     |  to_dict(self)
     |      KeyValueMetadata.to_dict(self)
     |
     |      Convert KeyValueMetadata to dict. If a key occurs twice, the value for
     |      the first one is returned
     |
     |  value(self, i)
     |      KeyValueMetadata.value(self, i)
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      byte
     |
     |  values(self)
     |      KeyValueMetadata.values(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from collections.abc.Mapping:
     |
     |  get(self, key, default=None)
     |      D.get(k[,d]) -> D[k] if k in D, else d.  d defaults to None.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from collections.abc.Mapping:
     |
     |  __abstractmethods__ = frozenset({'__getitem__', '__iter__', '__len__'}...
     |
     |  __reversed__ = None
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Collection:
     |
     |  __subclasshook__(C)
     |      Abstract classes can override this to customize issubclass().
     |
     |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
     |      It should return True, False or NotImplemented.  If it returns
     |      NotImplemented, the normal algorithm is used.  Otherwise, it
     |      overrides the normal algorithm (and the outcome is cached).
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Iterable:
     |
     |  __class_getitem__ = GenericAlias(...)
     |      Represent a PEP 585 generic type
     |
     |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

    class LargeBinaryArray(Array)
     |  Concrete class for Arrow arrays of large variable-sized binary data type.
     |
     |  Method resolution order:
     |      LargeBinaryArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  total_values_length
     |      The number of bytes from beginning to end of the data buffer addressed
     |      by the offsets of this LargeBinaryArray.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class LargeBinaryScalar(BinaryScalar)
     |  Method resolution order:
     |      LargeBinaryScalar
     |      BinaryScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BinaryScalar:
     |
     |  __buffer__(self, flags, /)
     |      Return a buffer object that exposes the underlying memory of the object.
     |
     |  __bytes__(self)
     |      BinaryScalar.__bytes__(self)
     |
     |  as_buffer(self)
     |      BinaryScalar.as_buffer(self)
     |
     |      Return a view over this value as a Buffer object.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      BinaryScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python bytes.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class LargeListArray(BaseListArray)
     |  Concrete class for Arrow arrays of a large list data type.
     |
     |  Identical to ListArray, but 64-bit offsets.
     |
     |  Method resolution order:
     |      LargeListArray
     |      BaseListArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_arrays(offsets, values, type=None, pool=None, mask=None)
     |      LargeListArray.from_arrays(offsets, values, DataType type=None, MemoryPool pool=None, mask=None)
     |
     |      Construct LargeListArray from arrays of int64 offsets and values.
     |
     |      Parameters
     |      ----------
     |      offsets : Array (int64 type)
     |      values : Array (any type)
     |      type : DataType, optional
     |          If not specified, a default ListType with the values' type is
     |          used.
     |      pool : MemoryPool, optional
     |      mask : Array (boolean type), optional
     |          Indicate which values are null (True) or not null (False).
     |
     |      Returns
     |      -------
     |      list_array : LargeListArray
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  offsets
     |      Return the list offsets as an int64 array.
     |
     |      The returned array will not have a validity bitmap, so you cannot
     |      expect to pass it to `LargeListArray.from_arrays` and get back the
     |      same list array if the original one has nulls.
     |
     |      Returns
     |      -------
     |      offsets : Int64Array
     |
     |  values
     |      Return the underlying array of values which backs the LargeListArray
     |      ignoring the array's offset.
     |
     |      If any of the list elements are null, but are backed by a
     |      non-empty sub-list, those elements will be included in the
     |      output.
     |
     |      Compare with :meth:`flatten`, which returns only the non-null
     |      values taking into consideration the array's offset.
     |
     |      Returns
     |      -------
     |      values : Array
     |
     |      See Also
     |      --------
     |      LargeListArray.flatten : ...
     |
     |      Examples
     |      --------
     |
     |      The values include null elements from the sub-lists:
     |
     |      >>> import pyarrow as pa
     |      >>> array = pa.array(
     |      ...     [[1, 2], None, [3, 4, None, 6]],
     |      ...     type=pa.large_list(pa.int32()),
     |      ... )
     |      >>> array.values
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        1,
     |        2,
     |        3,
     |        4,
     |        null,
     |        6
     |      ]
     |
     |      If an array is sliced, the slice still uses the same
     |      underlying data as the original array, just with an
     |      offset. Since values ignores the offset, the values are the
     |      same:
     |
     |      >>> sliced = array.slice(1, 2)
     |      >>> sliced
     |      <pyarrow.lib.LargeListArray object at ...>
     |      [
     |        null,
     |        [
     |          3,
     |          4,
     |          null,
     |          6
     |        ]
     |      ]
     |      >>> sliced.values
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        1,
     |        2,
     |        3,
     |        4,
     |        null,
     |        6
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseListArray:
     |
     |  flatten(self, recursive=False)
     |      BaseListArray.flatten(self, recursive=False)
     |
     |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
     |      according to 'recursive'.
     |
     |      Note that this method is different from ``self.values`` in that
     |      it takes care of the slicing offset as well as null elements backed
     |      by non-empty sub-lists.
     |
     |      Parameters
     |      ----------
     |      recursive : bool, default False, optional
     |          When True, flatten this logical list-array recursively until an
     |          array of non-list values is formed.
     |
     |          When False, flatten only the top level.
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |      Examples
     |      --------
     |
     |      Basic logical list-array's flatten
     |
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, 3, 4]
     |      >>> offsets = [2, 1, 0]
     |      >>> sizes = [2, 2, 2]
     |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array
     |      <pyarrow.lib.ListViewArray object at ...>
     |      [
     |        [
     |          3,
     |          4
     |        ],
     |        [
     |          2,
     |          3
     |        ],
     |        [
     |          1,
     |          2
     |        ]
     |      ]
     |      >>> array.flatten()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        3,
     |        4,
     |        2,
     |        3,
     |        1,
     |        2
     |      ]
     |
     |      When recursive=True, nested list arrays are flattened recursively
     |      until an array of non-list values is formed.
     |
     |      >>> array = pa.array([
     |      ...    None,
     |      ...    [
     |      ...        [1, None, 2],
     |      ...        None,
     |      ...        [3, 4]
     |      ...    ],
     |      ...    [],
     |      ...    [
     |      ...        [],
     |      ...        [5, 6],
     |      ...        None
     |      ...    ],
     |      ...    [
     |      ...        [7, 8]
     |      ...    ]
     |      ... ], type=pa.list_(pa.list_(pa.int64())))
     |      >>> array.flatten(True)
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        null,
     |        2,
     |        3,
     |        4,
     |        5,
     |        6,
     |        7,
     |        8
     |      ]
     |
     |  value_lengths(self)
     |      BaseListArray.value_lengths(self)
     |
     |      Return integers array with values equal to the respective length of
     |      each list element. Null list values are null in the output.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_lengths()
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        3,
     |        0,
     |        null,
     |        1
     |      ]
     |
     |  value_parent_indices(self)
     |      BaseListArray.value_parent_indices(self)
     |
     |      Return array of same length as list child values array where each
     |      output value is the index of the parent list array slot containing each
     |      child value.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_parent_indices()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        0,
     |        0,
     |        0,
     |        3
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class LargeListScalar(ListScalar)
     |  Method resolution order:
     |      LargeListScalar
     |      ListScalar
     |      Scalar
     |      _Weakrefable
     |      collections.abc.Sequence
     |      collections.abc.Reversible
     |      collections.abc.Collection
     |      collections.abc.Sized
     |      collections.abc.Iterable
     |      collections.abc.Container
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from ListScalar:
     |
     |  __getitem__(...)
     |      ListScalar.__getitem__(self, i)
     |
     |      Return the value at the given index.
     |
     |  __iter__(...)
     |      ListScalar.__iter__(self)
     |
     |      Iterate over this element's values.
     |
     |  __len__(...)
     |      ListScalar.__len__(self)
     |
     |      Return the number of values.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      ListScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python list.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ListScalar:
     |
     |  values
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from collections.abc.Sequence:
     |
     |  __contains__(self, value)
     |
     |  __reversed__(self)
     |
     |  count(self, value)
     |      S.count(value) -> integer -- return number of occurrences of value
     |
     |  index(self, value, start=0, stop=None)
     |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
     |      Raises ValueError if the value is not present.
     |
     |      Supporting start and stop arguments is optional, but
     |      recommended.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from collections.abc.Sequence:
     |
     |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Reversible:
     |
     |  __subclasshook__(C)
     |      Abstract classes can override this to customize issubclass().
     |
     |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
     |      It should return True, False or NotImplemented.  If it returns
     |      NotImplemented, the normal algorithm is used.  Otherwise, it
     |      overrides the normal algorithm (and the outcome is cached).
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Iterable:
     |
     |  __class_getitem__ = GenericAlias(...)
     |      Represent a PEP 585 generic type
     |
     |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

    class LargeListType(DataType)
     |  Concrete class for large list data types
     |  (like ListType, but with 64-bit offsets).
     |
     |  Examples
     |  --------
     |  Create an instance of LargeListType:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.large_list(pa.string())
     |  LargeListType(large_list<item: string>)
     |
     |  Method resolution order:
     |      LargeListType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      LargeListType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value_field
     |
     |  value_type
     |      The data type of large list values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.large_list(pa.string()).value_type
     |      DataType(string)
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class LargeListViewArray(BaseListArray)
     |  Concrete class for Arrow arrays of a large list view data type.
     |
     |  Identical to ListViewArray, but with 64-bit offsets.
     |
     |  Method resolution order:
     |      LargeListViewArray
     |      BaseListArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_arrays(offsets, sizes, values, type=None, pool=None, mask=None)
     |      LargeListViewArray.from_arrays(offsets, sizes, values, DataType type=None, MemoryPool pool=None, mask=None)
     |
     |      Construct LargeListViewArray from arrays of int64 offsets and values.
     |
     |      Parameters
     |      ----------
     |      offsets : Array (int64 type)
     |      sizes : Array (int64 type)
     |      values : Array (any type)
     |      type : DataType, optional
     |          If not specified, a default ListType with the values' type is
     |          used.
     |      pool : MemoryPool, optional
     |      mask : Array (boolean type), optional
     |          Indicate which values are null (True) or not null (False).
     |
     |      Returns
     |      -------
     |      list_view_array : LargeListViewArray
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> values = pa.array([1, 2, 3, 4])
     |      >>> offsets = pa.array([0, 1, 2])
     |      >>> sizes = pa.array([2, 2, 2])
     |      >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values)
     |      <pyarrow.lib.LargeListViewArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        [
     |          2,
     |          3
     |        ],
     |        [
     |          3,
     |          4
     |        ]
     |      ]
     |      >>> # use a null mask to represent null values
     |      >>> mask = pa.array([False, True, False])
     |      >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values, mask=mask)
     |      <pyarrow.lib.LargeListViewArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        null,
     |        [
     |          3,
     |          4
     |        ]
     |      ]
     |      >>> # null values can be defined in either offsets or sizes arrays
     |      >>> # WARNING: this will result in a copy of the offsets or sizes arrays
     |      >>> offsets = pa.array([0, None, 2])
     |      >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values)
     |      <pyarrow.lib.LargeListViewArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        null,
     |        [
     |          3,
     |          4
     |        ]
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  offsets
     |      Return the list view offsets as an int64 array.
     |
     |      The returned array will not have a validity bitmap, so you cannot
     |      expect to pass it to `LargeListViewArray.from_arrays` and get back the
     |      same list array if the original one has nulls.
     |
     |      Returns
     |      -------
     |      offsets : Int64Array
     |
     |      Examples
     |      --------
     |
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, None, 3, 4]
     |      >>> offsets = [0, 0, 1]
     |      >>> sizes = [2, 0, 4]
     |      >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array.offsets
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        0,
     |        0,
     |        1
     |      ]
     |
     |  sizes
     |      Return the list view sizes as an int64 array.
     |
     |      The returned array will not have a validity bitmap, so you cannot
     |      expect to pass it to `LargeListViewArray.from_arrays` and get back the
     |      same list array if the original one has nulls.
     |
     |      Returns
     |      -------
     |      sizes : Int64Array
     |
     |      Examples
     |      --------
     |
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, None, 3, 4]
     |      >>> offsets = [0, 0, 1]
     |      >>> sizes = [2, 0, 4]
     |      >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array.sizes
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        2,
     |        0,
     |        4
     |      ]
     |
     |  values
     |      Return the underlying array of values which backs the LargeListArray
     |      ignoring the array's offset.
     |
     |      The values array may be out of order and/or contain additional values
     |      that are not found in the logical representation of the array. The only
     |      guarantee is that each non-null value in the ListView Array is contiguous.
     |
     |      Compare with :meth:`flatten`, which returns only the non-null
     |      values taking into consideration the array's order and offset.
     |
     |      Returns
     |      -------
     |      values : Array
     |
     |      See Also
     |      --------
     |      LargeListArray.flatten : ...
     |
     |      Examples
     |      --------
     |
     |      The values include null elements from sub-lists:
     |
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, None, 3, 4]
     |      >>> offsets = [0, 0, 1]
     |      >>> sizes = [2, 0, 4]
     |      >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array
     |      <pyarrow.lib.LargeListViewArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        [],
     |        [
     |          2,
     |          null,
     |          3,
     |          4
     |        ]
     |      ]
     |      >>> array.values
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        2,
     |        null,
     |        3,
     |        4
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseListArray:
     |
     |  flatten(self, recursive=False)
     |      BaseListArray.flatten(self, recursive=False)
     |
     |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
     |      according to 'recursive'.
     |
     |      Note that this method is different from ``self.values`` in that
     |      it takes care of the slicing offset as well as null elements backed
     |      by non-empty sub-lists.
     |
     |      Parameters
     |      ----------
     |      recursive : bool, default False, optional
     |          When True, flatten this logical list-array recursively until an
     |          array of non-list values is formed.
     |
     |          When False, flatten only the top level.
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |      Examples
     |      --------
     |
     |      Basic logical list-array's flatten
     |
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, 3, 4]
     |      >>> offsets = [2, 1, 0]
     |      >>> sizes = [2, 2, 2]
     |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array
     |      <pyarrow.lib.ListViewArray object at ...>
     |      [
     |        [
     |          3,
     |          4
     |        ],
     |        [
     |          2,
     |          3
     |        ],
     |        [
     |          1,
     |          2
     |        ]
     |      ]
     |      >>> array.flatten()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        3,
     |        4,
     |        2,
     |        3,
     |        1,
     |        2
     |      ]
     |
     |      When recursive=True, nested list arrays are flattened recursively
     |      until an array of non-list values is formed.
     |
     |      >>> array = pa.array([
     |      ...    None,
     |      ...    [
     |      ...        [1, None, 2],
     |      ...        None,
     |      ...        [3, 4]
     |      ...    ],
     |      ...    [],
     |      ...    [
     |      ...        [],
     |      ...        [5, 6],
     |      ...        None
     |      ...    ],
     |      ...    [
     |      ...        [7, 8]
     |      ...    ]
     |      ... ], type=pa.list_(pa.list_(pa.int64())))
     |      >>> array.flatten(True)
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        null,
     |        2,
     |        3,
     |        4,
     |        5,
     |        6,
     |        7,
     |        8
     |      ]
     |
     |  value_lengths(self)
     |      BaseListArray.value_lengths(self)
     |
     |      Return integers array with values equal to the respective length of
     |      each list element. Null list values are null in the output.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_lengths()
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        3,
     |        0,
     |        null,
     |        1
     |      ]
     |
     |  value_parent_indices(self)
     |      BaseListArray.value_parent_indices(self)
     |
     |      Return array of same length as list child values array where each
     |      output value is the index of the parent list array slot containing each
     |      child value.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_parent_indices()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        0,
     |        0,
     |        0,
     |        3
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class LargeListViewScalar(ListScalar)
     |  Method resolution order:
     |      LargeListViewScalar
     |      ListScalar
     |      Scalar
     |      _Weakrefable
     |      collections.abc.Sequence
     |      collections.abc.Reversible
     |      collections.abc.Collection
     |      collections.abc.Sized
     |      collections.abc.Iterable
     |      collections.abc.Container
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from ListScalar:
     |
     |  __getitem__(...)
     |      ListScalar.__getitem__(self, i)
     |
     |      Return the value at the given index.
     |
     |  __iter__(...)
     |      ListScalar.__iter__(self)
     |
     |      Iterate over this element's values.
     |
     |  __len__(...)
     |      ListScalar.__len__(self)
     |
     |      Return the number of values.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      ListScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python list.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ListScalar:
     |
     |  values
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from collections.abc.Sequence:
     |
     |  __contains__(self, value)
     |
     |  __reversed__(self)
     |
     |  count(self, value)
     |      S.count(value) -> integer -- return number of occurrences of value
     |
     |  index(self, value, start=0, stop=None)
     |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
     |      Raises ValueError if the value is not present.
     |
     |      Supporting start and stop arguments is optional, but
     |      recommended.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from collections.abc.Sequence:
     |
     |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Reversible:
     |
     |  __subclasshook__(C)
     |      Abstract classes can override this to customize issubclass().
     |
     |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
     |      It should return True, False or NotImplemented.  If it returns
     |      NotImplemented, the normal algorithm is used.  Otherwise, it
     |      overrides the normal algorithm (and the outcome is cached).
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Iterable:
     |
     |  __class_getitem__ = GenericAlias(...)
     |      Represent a PEP 585 generic type
     |
     |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

    class LargeListViewType(DataType)
     |  Concrete class for large list view data types
     |  (like ListViewType, but with 64-bit offsets).
     |
     |  Examples
     |  --------
     |  Create an instance of LargeListViewType:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.large_list_view(pa.string())
     |  LargeListViewType(large_list_view<item: string>)
     |
     |  Method resolution order:
     |      LargeListViewType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      LargeListViewType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value_field
     |      The field for large list view values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.large_list_view(pa.string()).value_field
     |      pyarrow.Field<item: string>
     |
     |  value_type
     |      The data type of large list view values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.large_list_view(pa.string()).value_type
     |      DataType(string)
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class LargeStringArray(Array)
     |  Concrete class for Arrow arrays of large string (or utf8) data type.
     |
     |  Method resolution order:
     |      LargeStringArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_buffers(length, value_offsets, data, null_bitmap=None, null_count=-1, offset=0)
     |      LargeStringArray.from_buffers(int length, Buffer value_offsets, Buffer data, Buffer null_bitmap=None, int null_count=-1, int offset=0)
     |
     |      Construct a LargeStringArray from value_offsets and data buffers.
     |      If there are nulls in the data, also a null_bitmap and the matching
     |      null_count must be passed.
     |
     |      Parameters
     |      ----------
     |      length : int
     |      value_offsets : Buffer
     |      data : Buffer
     |      null_bitmap : Buffer, optional
     |      null_count : int, default 0
     |      offset : int, default 0
     |
     |      Returns
     |      -------
     |      string_array : StringArray
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class LargeStringScalar(StringScalar)
     |  Method resolution order:
     |      LargeStringScalar
     |      StringScalar
     |      BinaryScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from StringScalar:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      StringScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python string.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BinaryScalar:
     |
     |  __buffer__(self, flags, /)
     |      Return a buffer object that exposes the underlying memory of the object.
     |
     |  __bytes__(self)
     |      BinaryScalar.__bytes__(self)
     |
     |  as_buffer(self)
     |      BinaryScalar.as_buffer(self)
     |
     |      Return a view over this value as a Buffer object.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class ListArray(BaseListArray)
     |  Concrete class for Arrow arrays of a list data type.
     |
     |  Method resolution order:
     |      ListArray
     |      BaseListArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_arrays(offsets, values, type=None, pool=None, mask=None)
     |      ListArray.from_arrays(offsets, values, DataType type=None, MemoryPool pool=None, mask=None)
     |
     |      Construct ListArray from arrays of int32 offsets and values.
     |
     |      Parameters
     |      ----------
     |      offsets : Array (int32 type)
     |      values : Array (any type)
     |      type : DataType, optional
     |          If not specified, a default ListType with the values' type is
     |          used.
     |      pool : MemoryPool, optional
     |      mask : Array (boolean type), optional
     |          Indicate which values are null (True) or not null (False).
     |
     |      Returns
     |      -------
     |      list_array : ListArray
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> values = pa.array([1, 2, 3, 4])
     |      >>> offsets = pa.array([0, 2, 4])
     |      >>> pa.ListArray.from_arrays(offsets, values)
     |      <pyarrow.lib.ListArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        [
     |          3,
     |          4
     |        ]
     |      ]
     |      >>> # nulls in the offsets array become null lists
     |      >>> offsets = pa.array([0, None, 2, 4])
     |      >>> pa.ListArray.from_arrays(offsets, values)
     |      <pyarrow.lib.ListArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        null,
     |        [
     |          3,
     |          4
     |        ]
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  offsets
     |      Return the list offsets as an int32 array.
     |
     |      The returned array will not have a validity bitmap, so you cannot
     |      expect to pass it to `ListArray.from_arrays` and get back the same
     |      list array if the original one has nulls.
     |
     |      Returns
     |      -------
     |      offsets : Int32Array
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> array = pa.array([[1, 2], None, [3, 4, 5]])
     |      >>> array.offsets
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        0,
     |        2,
     |        2,
     |        5
     |      ]
     |
     |  values
     |      Return the underlying array of values which backs the ListArray
     |      ignoring the array's offset.
     |
     |      If any of the list elements are null, but are backed by a
     |      non-empty sub-list, those elements will be included in the
     |      output.
     |
     |      Compare with :meth:`flatten`, which returns only the non-null
     |      values taking into consideration the array's offset.
     |
     |      Returns
     |      -------
     |      values : Array
     |
     |      See Also
     |      --------
     |      ListArray.flatten : ...
     |
     |      Examples
     |      --------
     |
     |      The values include null elements from sub-lists:
     |
     |      >>> import pyarrow as pa
     |      >>> array = pa.array([[1, 2], None, [3, 4, None, 6]])
     |      >>> array.values
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        2,
     |        3,
     |        4,
     |        null,
     |        6
     |      ]
     |
     |      If an array is sliced, the slice still uses the same
     |      underlying data as the original array, just with an
     |      offset. Since values ignores the offset, the values are the
     |      same:
     |
     |      >>> sliced = array.slice(1, 2)
     |      >>> sliced
     |      <pyarrow.lib.ListArray object at ...>
     |      [
     |        null,
     |        [
     |          3,
     |          4,
     |          null,
     |          6
     |        ]
     |      ]
     |      >>> sliced.values
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        2,
     |        3,
     |        4,
     |        null,
     |        6
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseListArray:
     |
     |  flatten(self, recursive=False)
     |      BaseListArray.flatten(self, recursive=False)
     |
     |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
     |      according to 'recursive'.
     |
     |      Note that this method is different from ``self.values`` in that
     |      it takes care of the slicing offset as well as null elements backed
     |      by non-empty sub-lists.
     |
     |      Parameters
     |      ----------
     |      recursive : bool, default False, optional
     |          When True, flatten this logical list-array recursively until an
     |          array of non-list values is formed.
     |
     |          When False, flatten only the top level.
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |      Examples
     |      --------
     |
     |      Basic logical list-array's flatten
     |
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, 3, 4]
     |      >>> offsets = [2, 1, 0]
     |      >>> sizes = [2, 2, 2]
     |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array
     |      <pyarrow.lib.ListViewArray object at ...>
     |      [
     |        [
     |          3,
     |          4
     |        ],
     |        [
     |          2,
     |          3
     |        ],
     |        [
     |          1,
     |          2
     |        ]
     |      ]
     |      >>> array.flatten()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        3,
     |        4,
     |        2,
     |        3,
     |        1,
     |        2
     |      ]
     |
     |      When recursive=True, nested list arrays are flattened recursively
     |      until an array of non-list values is formed.
     |
     |      >>> array = pa.array([
     |      ...    None,
     |      ...    [
     |      ...        [1, None, 2],
     |      ...        None,
     |      ...        [3, 4]
     |      ...    ],
     |      ...    [],
     |      ...    [
     |      ...        [],
     |      ...        [5, 6],
     |      ...        None
     |      ...    ],
     |      ...    [
     |      ...        [7, 8]
     |      ...    ]
     |      ... ], type=pa.list_(pa.list_(pa.int64())))
     |      >>> array.flatten(True)
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        null,
     |        2,
     |        3,
     |        4,
     |        5,
     |        6,
     |        7,
     |        8
     |      ]
     |
     |  value_lengths(self)
     |      BaseListArray.value_lengths(self)
     |
     |      Return integers array with values equal to the respective length of
     |      each list element. Null list values are null in the output.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_lengths()
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        3,
     |        0,
     |        null,
     |        1
     |      ]
     |
     |  value_parent_indices(self)
     |      BaseListArray.value_parent_indices(self)
     |
     |      Return array of same length as list child values array where each
     |      output value is the index of the parent list array slot containing each
     |      child value.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_parent_indices()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        0,
     |        0,
     |        0,
     |        3
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class ListScalar(Scalar, collections.abc.Sequence)
     |  Concrete class for list-like scalars.
     |
     |  Method resolution order:
     |      ListScalar
     |      Scalar
     |      _Weakrefable
     |      collections.abc.Sequence
     |      collections.abc.Reversible
     |      collections.abc.Collection
     |      collections.abc.Sized
     |      collections.abc.Iterable
     |      collections.abc.Container
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __getitem__(...)
     |      ListScalar.__getitem__(self, i)
     |
     |      Return the value at the given index.
     |
     |  __iter__(...)
     |      ListScalar.__iter__(self)
     |
     |      Iterate over this element's values.
     |
     |  __len__(...)
     |      ListScalar.__len__(self)
     |
     |      Return the number of values.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      ListScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python list.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  values
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from collections.abc.Sequence:
     |
     |  __contains__(self, value)
     |
     |  __reversed__(self)
     |
     |  count(self, value)
     |      S.count(value) -> integer -- return number of occurrences of value
     |
     |  index(self, value, start=0, stop=None)
     |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
     |      Raises ValueError if the value is not present.
     |
     |      Supporting start and stop arguments is optional, but
     |      recommended.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from collections.abc.Sequence:
     |
     |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Reversible:
     |
     |  __subclasshook__(C)
     |      Abstract classes can override this to customize issubclass().
     |
     |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
     |      It should return True, False or NotImplemented.  If it returns
     |      NotImplemented, the normal algorithm is used.  Otherwise, it
     |      overrides the normal algorithm (and the outcome is cached).
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Iterable:
     |
     |  __class_getitem__ = GenericAlias(...)
     |      Represent a PEP 585 generic type
     |
     |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

    class ListType(DataType)
     |  Concrete class for list data types.
     |
     |  Examples
     |  --------
     |  Create an instance of ListType:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.list_(pa.string())
     |  ListType(list<item: string>)
     |
     |  Method resolution order:
     |      ListType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      ListType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value_field
     |      The field for list values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.list_(pa.string()).value_field
     |      pyarrow.Field<item: string>
     |
     |  value_type
     |      The data type of list values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.list_(pa.string()).value_type
     |      DataType(string)
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class ListViewArray(BaseListArray)
     |  Concrete class for Arrow arrays of a list view data type.
     |
     |  Method resolution order:
     |      ListViewArray
     |      BaseListArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_arrays(offsets, sizes, values, type=None, pool=None, mask=None)
     |      ListViewArray.from_arrays(offsets, sizes, values, DataType type=None, MemoryPool pool=None, mask=None)
     |
     |      Construct ListViewArray from arrays of int32 offsets, sizes, and values.
     |
     |      Parameters
     |      ----------
     |      offsets : Array (int32 type)
     |      sizes : Array (int32 type)
     |      values : Array (any type)
     |      type : DataType, optional
     |          If not specified, a default ListType with the values' type is
     |          used.
     |      pool : MemoryPool, optional
     |      mask : Array (boolean type), optional
     |          Indicate which values are null (True) or not null (False).
     |
     |      Returns
     |      -------
     |      list_view_array : ListViewArray
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> values = pa.array([1, 2, 3, 4])
     |      >>> offsets = pa.array([0, 1, 2])
     |      >>> sizes = pa.array([2, 2, 2])
     |      >>> pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      <pyarrow.lib.ListViewArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        [
     |          2,
     |          3
     |        ],
     |        [
     |          3,
     |          4
     |        ]
     |      ]
     |      >>> # use a null mask to represent null values
     |      >>> mask = pa.array([False, True, False])
     |      >>> pa.ListViewArray.from_arrays(offsets, sizes, values, mask=mask)
     |      <pyarrow.lib.ListViewArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        null,
     |        [
     |          3,
     |          4
     |        ]
     |      ]
     |      >>> # null values can be defined in either offsets or sizes arrays
     |      >>> # WARNING: this will result in a copy of the offsets or sizes arrays
     |      >>> offsets = pa.array([0, None, 2])
     |      >>> pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      <pyarrow.lib.ListViewArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        null,
     |        [
     |          3,
     |          4
     |        ]
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  offsets
     |      Return the list offsets as an int32 array.
     |
     |      The returned array will not have a validity bitmap, so you cannot
     |      expect to pass it to `ListViewArray.from_arrays` and get back the same
     |      list array if the original one has nulls.
     |
     |      Returns
     |      -------
     |      offsets : Int32Array
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, None, 3, 4]
     |      >>> offsets = [0, 0, 1]
     |      >>> sizes = [2, 0, 4]
     |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array.offsets
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        0,
     |        0,
     |        1
     |      ]
     |
     |  sizes
     |      Return the list sizes as an int32 array.
     |
     |      The returned array will not have a validity bitmap, so you cannot
     |      expect to pass it to `ListViewArray.from_arrays` and get back the same
     |      list array if the original one has nulls.
     |
     |      Returns
     |      -------
     |      sizes : Int32Array
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, None, 3, 4]
     |      >>> offsets = [0, 0, 1]
     |      >>> sizes = [2, 0, 4]
     |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array.sizes
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        2,
     |        0,
     |        4
     |      ]
     |
     |  values
     |      Return the underlying array of values which backs the ListViewArray
     |      ignoring the array's offset and sizes.
     |
     |      The values array may be out of order and/or contain additional values
     |      that are not found in the logical representation of the array. The only
     |      guarantee is that each non-null value in the ListView Array is contiguous.
     |
     |      Compare with :meth:`flatten`, which returns only the non-null
     |      values taking into consideration the array's order and offset.
     |
     |      Returns
     |      -------
     |      values : Array
     |
     |      Examples
     |      --------
     |      The values include null elements from sub-lists:
     |
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, None, 3, 4]
     |      >>> offsets = [0, 0, 1]
     |      >>> sizes = [2, 0, 4]
     |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array
     |      <pyarrow.lib.ListViewArray object at ...>
     |      [
     |        [
     |          1,
     |          2
     |        ],
     |        [],
     |        [
     |          2,
     |          null,
     |          3,
     |          4
     |        ]
     |      ]
     |      >>> array.values
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        2,
     |        null,
     |        3,
     |        4
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseListArray:
     |
     |  flatten(self, recursive=False)
     |      BaseListArray.flatten(self, recursive=False)
     |
     |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
     |      according to 'recursive'.
     |
     |      Note that this method is different from ``self.values`` in that
     |      it takes care of the slicing offset as well as null elements backed
     |      by non-empty sub-lists.
     |
     |      Parameters
     |      ----------
     |      recursive : bool, default False, optional
     |          When True, flatten this logical list-array recursively until an
     |          array of non-list values is formed.
     |
     |          When False, flatten only the top level.
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |      Examples
     |      --------
     |
     |      Basic logical list-array's flatten
     |
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, 3, 4]
     |      >>> offsets = [2, 1, 0]
     |      >>> sizes = [2, 2, 2]
     |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array
     |      <pyarrow.lib.ListViewArray object at ...>
     |      [
     |        [
     |          3,
     |          4
     |        ],
     |        [
     |          2,
     |          3
     |        ],
     |        [
     |          1,
     |          2
     |        ]
     |      ]
     |      >>> array.flatten()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        3,
     |        4,
     |        2,
     |        3,
     |        1,
     |        2
     |      ]
     |
     |      When recursive=True, nested list arrays are flattened recursively
     |      until an array of non-list values is formed.
     |
     |      >>> array = pa.array([
     |      ...    None,
     |      ...    [
     |      ...        [1, None, 2],
     |      ...        None,
     |      ...        [3, 4]
     |      ...    ],
     |      ...    [],
     |      ...    [
     |      ...        [],
     |      ...        [5, 6],
     |      ...        None
     |      ...    ],
     |      ...    [
     |      ...        [7, 8]
     |      ...    ]
     |      ... ], type=pa.list_(pa.list_(pa.int64())))
     |      >>> array.flatten(True)
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        null,
     |        2,
     |        3,
     |        4,
     |        5,
     |        6,
     |        7,
     |        8
     |      ]
     |
     |  value_lengths(self)
     |      BaseListArray.value_lengths(self)
     |
     |      Return integers array with values equal to the respective length of
     |      each list element. Null list values are null in the output.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_lengths()
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        3,
     |        0,
     |        null,
     |        1
     |      ]
     |
     |  value_parent_indices(self)
     |      BaseListArray.value_parent_indices(self)
     |
     |      Return array of same length as list child values array where each
     |      output value is the index of the parent list array slot containing each
     |      child value.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_parent_indices()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        0,
     |        0,
     |        0,
     |        3
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class ListViewScalar(ListScalar)
     |  Method resolution order:
     |      ListViewScalar
     |      ListScalar
     |      Scalar
     |      _Weakrefable
     |      collections.abc.Sequence
     |      collections.abc.Reversible
     |      collections.abc.Collection
     |      collections.abc.Sized
     |      collections.abc.Iterable
     |      collections.abc.Container
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from ListScalar:
     |
     |  __getitem__(...)
     |      ListScalar.__getitem__(self, i)
     |
     |      Return the value at the given index.
     |
     |  __iter__(...)
     |      ListScalar.__iter__(self)
     |
     |      Iterate over this element's values.
     |
     |  __len__(...)
     |      ListScalar.__len__(self)
     |
     |      Return the number of values.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      ListScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python list.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ListScalar:
     |
     |  values
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from collections.abc.Sequence:
     |
     |  __contains__(self, value)
     |
     |  __reversed__(self)
     |
     |  count(self, value)
     |      S.count(value) -> integer -- return number of occurrences of value
     |
     |  index(self, value, start=0, stop=None)
     |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
     |      Raises ValueError if the value is not present.
     |
     |      Supporting start and stop arguments is optional, but
     |      recommended.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from collections.abc.Sequence:
     |
     |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Reversible:
     |
     |  __subclasshook__(C)
     |      Abstract classes can override this to customize issubclass().
     |
     |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
     |      It should return True, False or NotImplemented.  If it returns
     |      NotImplemented, the normal algorithm is used.  Otherwise, it
     |      overrides the normal algorithm (and the outcome is cached).
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Iterable:
     |
     |  __class_getitem__ = GenericAlias(...)
     |      Represent a PEP 585 generic type
     |
     |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

    class ListViewType(DataType)
     |  Concrete class for list view data types.
     |
     |  Examples
     |  --------
     |  Create an instance of ListViewType:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.list_view(pa.string())
     |  ListViewType(list_view<item: string>)
     |
     |  Method resolution order:
     |      ListViewType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      ListViewType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value_field
     |      The field for list view values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.list_view(pa.string()).value_field
     |      pyarrow.Field<item: string>
     |
     |  value_type
     |      The data type of list view values.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.list_view(pa.string()).value_type
     |      DataType(string)
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class LoggingMemoryPool(MemoryPool)
     |  LoggingMemoryPool()
     |
     |  Method resolution order:
     |      LoggingMemoryPool
     |      MemoryPool
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      LoggingMemoryPool.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      LoggingMemoryPool.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from MemoryPool:
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  bytes_allocated(self)
     |      MemoryPool.bytes_allocated(self)
     |
     |      Return the number of bytes that are currently allocated from this
     |      memory pool.
     |
     |  max_memory(self)
     |      MemoryPool.max_memory(self)
     |
     |      Return the peak memory allocation in this memory pool.
     |      This can be an approximate number in multi-threaded applications.
     |
     |      None is returned if the pool implementation doesn't know how to
     |      compute this number.
     |
     |  num_allocations(self)
     |      MemoryPool.num_allocations(self)
     |
     |      Return the number of allocations or reallocations that were made
     |      using this memory pool.
     |
     |  print_stats(self)
     |      MemoryPool.print_stats(self)
     |
     |      Print statistics about this memory pool.
     |
     |      The output format is implementation-specific. Not all memory pools
     |      implement this method.
     |
     |  release_unused(self)
     |      MemoryPool.release_unused(self)
     |
     |      Attempt to return to the OS any memory being held onto by the pool.
     |
     |      This function should not be called except potentially for
     |      benchmarking or debugging as it could be expensive and detrimental to
     |      performance.
     |
     |      This is best effort and may not have any effect on some memory pools
     |      or in some situations (e.g. fragmentation).
     |
     |  total_bytes_allocated(self)
     |      MemoryPool.total_bytes_allocated(self)
     |
     |      Return the total number of bytes that have been allocated from this
     |      memory pool.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from MemoryPool:
     |
     |  backend_name
     |      The name of the backend used by this MemoryPool (e.g. "jemalloc").

    class MapArray(ListArray)
     |  Concrete class for Arrow arrays of a map data type.
     |
     |  Method resolution order:
     |      MapArray
     |      ListArray
     |      BaseListArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_arrays(offsets, keys, items, type=None, pool=None, mask=None)
     |      MapArray.from_arrays(offsets, keys, items, DataType type=None, MemoryPool pool=None, mask=None)
     |
     |      Construct MapArray from arrays of int32 offsets and key, item arrays.
     |
     |      Parameters
     |      ----------
     |      offsets : array-like or sequence (int32 type)
     |      keys : array-like or sequence (any type)
     |      items : array-like or sequence (any type)
     |      type : DataType, optional
     |          If not specified, a default MapArray with the keys' and items' type is used.
     |      pool : MemoryPool
     |      mask : Array (boolean type), optional
     |          Indicate which values are null (True) or not null (False).
     |
     |      Returns
     |      -------
     |      map_array : MapArray
     |
     |      Examples
     |      --------
     |      First, let's understand the structure of our dataset when viewed in a rectangular data model.
     |      The total of 5 respondents answered the question "How much did you like the movie x?".
     |      The value -1 in the integer array means that the value is missing. The boolean array
     |      represents the null bitmask corresponding to the missing values in the integer array.
     |
     |      >>> import pyarrow as pa
     |      >>> movies_rectangular = np.ma.masked_array([
     |      ...     [10, -1, -1],
     |      ...     [8, 4, 5],
     |      ...     [-1, 10, 3],
     |      ...     [-1, -1, -1],
     |      ...     [-1, -1, -1]
     |      ... ],
     |      ... [
     |      ...     [False, True, True],
     |      ...     [False, False, False],
     |      ...     [True, False, False],
     |      ...     [True, True, True],
     |      ...     [True, True, True],
     |      ... ])
     |
     |      To represent the same data with the MapArray and from_arrays, the data is
     |      formed like this:
     |
     |      >>> offsets = [
     |      ...     0, #  -- row 1 start
     |      ...     1, #  -- row 2 start
     |      ...     4, #  -- row 3 start
     |      ...     6, #  -- row 4 start
     |      ...     6, #  -- row 5 start
     |      ...     6, #  -- row 5 end
     |      ... ]
     |      >>> movies = [
     |      ...     "Dark Knight", #  ---------------------------------- row 1
     |      ...     "Dark Knight", "Meet the Parents", "Superman", #  -- row 2
     |      ...     "Meet the Parents", "Superman", #  ----------------- row 3
     |      ... ]
     |      >>> likings = [
     |      ...     10, #  -------- row 1
     |      ...     8, 4, 5, #  --- row 2
     |      ...     10, 3 #  ------ row 3
     |      ... ]
     |      >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas()
     |      0                                  [(Dark Knight, 10)]
     |      1    [(Dark Knight, 8), (Meet the Parents, 4), (Sup...
     |      2              [(Meet the Parents, 10), (Superman, 3)]
     |      3                                                   []
     |      4                                                   []
     |      dtype: object
     |
     |      If the data in the empty rows needs to be marked as missing, it's possible
     |      to do so by modifying the offsets argument, so that we specify `None` as
     |      the starting positions of the rows we want marked as missing. The end row
     |      offset still has to refer to the existing value from keys (and values):
     |
     |      >>> offsets = [
     |      ...     0, #  ----- row 1 start
     |      ...     1, #  ----- row 2 start
     |      ...     4, #  ----- row 3 start
     |      ...     None, #  -- row 4 start
     |      ...     None, #  -- row 5 start
     |      ...     6, #  ----- row 5 end
     |      ... ]
     |      >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas()
     |      0                                  [(Dark Knight, 10)]
     |      1    [(Dark Knight, 8), (Meet the Parents, 4), (Sup...
     |      2              [(Meet the Parents, 10), (Superman, 3)]
     |      3                                                 None
     |      4                                                 None
     |      dtype: object
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  items
     |      Flattened array of items across all maps in array
     |
     |  keys
     |      Flattened array of keys across all maps in array
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ListArray:
     |
     |  offsets
     |      Return the list offsets as an int32 array.
     |
     |      The returned array will not have a validity bitmap, so you cannot
     |      expect to pass it to `ListArray.from_arrays` and get back the same
     |      list array if the original one has nulls.
     |
     |      Returns
     |      -------
     |      offsets : Int32Array
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> array = pa.array([[1, 2], None, [3, 4, 5]])
     |      >>> array.offsets
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        0,
     |        2,
     |        2,
     |        5
     |      ]
     |
     |  values
     |      Return the underlying array of values which backs the ListArray
     |      ignoring the array's offset.
     |
     |      If any of the list elements are null, but are backed by a
     |      non-empty sub-list, those elements will be included in the
     |      output.
     |
     |      Compare with :meth:`flatten`, which returns only the non-null
     |      values taking into consideration the array's offset.
     |
     |      Returns
     |      -------
     |      values : Array
     |
     |      See Also
     |      --------
     |      ListArray.flatten : ...
     |
     |      Examples
     |      --------
     |
     |      The values include null elements from sub-lists:
     |
     |      >>> import pyarrow as pa
     |      >>> array = pa.array([[1, 2], None, [3, 4, None, 6]])
     |      >>> array.values
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        2,
     |        3,
     |        4,
     |        null,
     |        6
     |      ]
     |
     |      If an array is sliced, the slice still uses the same
     |      underlying data as the original array, just with an
     |      offset. Since values ignores the offset, the values are the
     |      same:
     |
     |      >>> sliced = array.slice(1, 2)
     |      >>> sliced
     |      <pyarrow.lib.ListArray object at ...>
     |      [
     |        null,
     |        [
     |          3,
     |          4,
     |          null,
     |          6
     |        ]
     |      ]
     |      >>> sliced.values
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        2,
     |        3,
     |        4,
     |        null,
     |        6
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseListArray:
     |
     |  flatten(self, recursive=False)
     |      BaseListArray.flatten(self, recursive=False)
     |
     |      Unnest this [Large]ListArray/[Large]ListViewArray/FixedSizeListArray
     |      according to 'recursive'.
     |
     |      Note that this method is different from ``self.values`` in that
     |      it takes care of the slicing offset as well as null elements backed
     |      by non-empty sub-lists.
     |
     |      Parameters
     |      ----------
     |      recursive : bool, default False, optional
     |          When True, flatten this logical list-array recursively until an
     |          array of non-list values is formed.
     |
     |          When False, flatten only the top level.
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |      Examples
     |      --------
     |
     |      Basic logical list-array's flatten
     |
     |      >>> import pyarrow as pa
     |      >>> values = [1, 2, 3, 4]
     |      >>> offsets = [2, 1, 0]
     |      >>> sizes = [2, 2, 2]
     |      >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values)
     |      >>> array
     |      <pyarrow.lib.ListViewArray object at ...>
     |      [
     |        [
     |          3,
     |          4
     |        ],
     |        [
     |          2,
     |          3
     |        ],
     |        [
     |          1,
     |          2
     |        ]
     |      ]
     |      >>> array.flatten()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        3,
     |        4,
     |        2,
     |        3,
     |        1,
     |        2
     |      ]
     |
     |      When recursive=True, nested list arrays are flattened recursively
     |      until an array of non-list values is formed.
     |
     |      >>> array = pa.array([
     |      ...    None,
     |      ...    [
     |      ...        [1, None, 2],
     |      ...        None,
     |      ...        [3, 4]
     |      ...    ],
     |      ...    [],
     |      ...    [
     |      ...        [],
     |      ...        [5, 6],
     |      ...        None
     |      ...    ],
     |      ...    [
     |      ...        [7, 8]
     |      ...    ]
     |      ... ], type=pa.list_(pa.list_(pa.int64())))
     |      >>> array.flatten(True)
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        1,
     |        null,
     |        2,
     |        3,
     |        4,
     |        5,
     |        6,
     |        7,
     |        8
     |      ]
     |
     |  value_lengths(self)
     |      BaseListArray.value_lengths(self)
     |
     |      Return integers array with values equal to the respective length of
     |      each list element. Null list values are null in the output.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_lengths()
     |      <pyarrow.lib.Int32Array object at ...>
     |      [
     |        3,
     |        0,
     |        null,
     |        1
     |      ]
     |
     |  value_parent_indices(self)
     |      BaseListArray.value_parent_indices(self)
     |
     |      Return array of same length as list child values array where each
     |      output value is the index of the parent list array slot containing each
     |      child value.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr = pa.array([[1, 2, 3], [], None, [4]],
     |      ...                type=pa.list_(pa.int32()))
     |      >>> arr.value_parent_indices()
     |      <pyarrow.lib.Int64Array object at ...>
     |      [
     |        0,
     |        0,
     |        0,
     |        3
     |      ]
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class MapScalar(ListScalar, collections.abc.Mapping)
     |  Concrete class for map scalars.
     |
     |  Method resolution order:
     |      MapScalar
     |      ListScalar
     |      Scalar
     |      _Weakrefable
     |      collections.abc.Sequence
     |      collections.abc.Reversible
     |      collections.abc.Mapping
     |      collections.abc.Collection
     |      collections.abc.Sized
     |      collections.abc.Iterable
     |      collections.abc.Container
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __getitem__(...)
     |      MapScalar.__getitem__(self, i)
     |
     |      Return the value at the given index or key.
     |
     |  __iter__(...)
     |      MapScalar.__iter__(self)
     |
     |      Iterate over this element's values.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      MapScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python list or dict, depending on 'maps_as_pydicts'.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  keys(self)
     |      MapScalar.keys(self)
     |
     |      Return the keys of the map as a list.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from ListScalar:
     |
     |  __len__(...)
     |      ListScalar.__len__(self)
     |
     |      Return the number of values.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ListScalar:
     |
     |  values
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from collections.abc.Sequence:
     |
     |  __contains__(self, value)
     |
     |  __reversed__(self)
     |
     |  count(self, value)
     |      S.count(value) -> integer -- return number of occurrences of value
     |
     |  index(self, value, start=0, stop=None)
     |      S.index(value, [start, [stop]]) -> integer -- return first index of value.
     |      Raises ValueError if the value is not present.
     |
     |      Supporting start and stop arguments is optional, but
     |      recommended.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from collections.abc.Sequence:
     |
     |  __abstractmethods__ = frozenset({'__getitem__', '__len__'})
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Reversible:
     |
     |  __subclasshook__(C)
     |      Abstract classes can override this to customize issubclass().
     |
     |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
     |      It should return True, False or NotImplemented.  If it returns
     |      NotImplemented, the normal algorithm is used.  Otherwise, it
     |      overrides the normal algorithm (and the outcome is cached).
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from collections.abc.Mapping:
     |
     |  get(self, key, default=None)
     |      D.get(k[,d]) -> D[k] if k in D, else d.  d defaults to None.
     |
     |  items(self)
     |      D.items() -> a set-like object providing a view on D's items
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Iterable:
     |
     |  __class_getitem__ = GenericAlias(...)
     |      Represent a PEP 585 generic type
     |
     |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

    class MapType(DataType)
     |  Concrete class for map data types.
     |
     |  Examples
     |  --------
     |  Create an instance of MapType:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.map_(pa.string(), pa.int32())
     |  MapType(map<string, int32>)
     |  >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True)
     |  MapType(map<string, int32, keys_sorted>)
     |
     |  Method resolution order:
     |      MapType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      MapType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  item_field
     |      The field for items in the map entries.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.map_(pa.string(), pa.int32()).item_field
     |      pyarrow.Field<value: int32>
     |
     |  item_type
     |      The data type of items in the map entries.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.map_(pa.string(), pa.int32()).item_type
     |      DataType(int32)
     |
     |  key_field
     |      The field for keys in the map entries.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.map_(pa.string(), pa.int32()).key_field
     |      pyarrow.Field<key: string not null>
     |
     |  key_type
     |      The data type of keys in the map entries.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.map_(pa.string(), pa.int32()).key_type
     |      DataType(string)
     |
     |  keys_sorted
     |      Should the entries be sorted according to keys.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True).keys_sorted
     |      True
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class MemoryManager(_Weakrefable)
     |  MemoryManager()
     |
     |  An object that provides memory management primitives.
     |
     |  A MemoryManager is always tied to a particular Device instance.
     |  It can also have additional parameters (such as a MemoryPool to
     |  allocate CPU memory).
     |
     |  Method resolution order:
     |      MemoryManager
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      MemoryManager.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      MemoryManager.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  device
     |      The device this MemoryManager is tied to.
     |
     |  is_cpu
     |      Whether this MemoryManager is tied to the main CPU device.
     |
     |      This shorthand method is very useful when deciding whether a memory
     |      address is CPU-accessible.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class MemoryMappedFile(NativeFile)
     |  A stream that represents a memory-mapped file.
     |
     |  Supports 'r', 'r+', 'w' modes.
     |
     |  Examples
     |  --------
     |  Create a new file with memory map:
     |
     |  >>> import pyarrow as pa
     |  >>> mmap = pa.create_memory_map('example_mmap.dat', 10)
     |  >>> mmap
     |  <pyarrow.MemoryMappedFile closed=False own_file=False is_seekable=True is_writable=True is_readable=True>
     |  >>> mmap.close()
     |
     |  Open an existing file with memory map:
     |
     |  >>> with pa.memory_map('example_mmap.dat') as mmap:
     |  ...     mmap
     |  ...
     |  <pyarrow.MemoryMappedFile closed=False own_file=False is_seekable=True is_writable=False is_readable=True>
     |
     |  Method resolution order:
     |      MemoryMappedFile
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__ = __reduce_cython__(...)
     |      MemoryMappedFile.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      MemoryMappedFile.__setstate_cython__(self, __pyx_state)
     |
     |  fileno(self)
     |      MemoryMappedFile.fileno(self)
     |
     |  resize(self, new_size)
     |      MemoryMappedFile.resize(self, new_size)
     |
     |      Resize the map and underlying file.
     |
     |      Parameters
     |      ----------
     |      new_size : new size in bytes
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  create(path, size)
     |      MemoryMappedFile.create(path, size)
     |
     |      Create a MemoryMappedFile
     |
     |      Parameters
     |      ----------
     |      path : str
     |          Where to create the file.
     |      size : int
     |          Size of the memory mapped file.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class MemoryPool(_Weakrefable)
     |  MemoryPool()
     |
     |  Base class for memory allocation.
     |
     |  Besides tracking its number of allocated bytes, a memory pool also
     |  takes care of the required 64-byte alignment for Arrow data.
     |
     |  Method resolution order:
     |      MemoryPool
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      MemoryPool.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      MemoryPool.__setstate_cython__(self, __pyx_state)
     |
     |  bytes_allocated(self)
     |      MemoryPool.bytes_allocated(self)
     |
     |      Return the number of bytes that are currently allocated from this
     |      memory pool.
     |
     |  max_memory(self)
     |      MemoryPool.max_memory(self)
     |
     |      Return the peak memory allocation in this memory pool.
     |      This can be an approximate number in multi-threaded applications.
     |
     |      None is returned if the pool implementation doesn't know how to
     |      compute this number.
     |
     |  num_allocations(self)
     |      MemoryPool.num_allocations(self)
     |
     |      Return the number of allocations or reallocations that were made
     |      using this memory pool.
     |
     |  print_stats(self)
     |      MemoryPool.print_stats(self)
     |
     |      Print statistics about this memory pool.
     |
     |      The output format is implementation-specific. Not all memory pools
     |      implement this method.
     |
     |  release_unused(self)
     |      MemoryPool.release_unused(self)
     |
     |      Attempt to return to the OS any memory being held onto by the pool.
     |
     |      This function should not be called except potentially for
     |      benchmarking or debugging as it could be expensive and detrimental to
     |      performance.
     |
     |      This is best effort and may not have any effect on some memory pools
     |      or in some situations (e.g. fragmentation).
     |
     |  total_bytes_allocated(self)
     |      MemoryPool.total_bytes_allocated(self)
     |
     |      Return the total number of bytes that have been allocated from this
     |      memory pool.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  backend_name
     |      The name of the backend used by this MemoryPool (e.g. "jemalloc").
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class Message(_Weakrefable)
     |  Message()
     |
     |  Container for an Arrow IPC message with metadata and optional body
     |
     |  Method resolution order:
     |      Message
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      Message.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      Message.__setstate_cython__(self, __pyx_state)
     |
     |  equals(self, other)
     |      Message.equals(self, Message other)
     |
     |      Returns True if the message contents (metadata and body) are identical
     |
     |      Parameters
     |      ----------
     |      other : Message
     |
     |      Returns
     |      -------
     |      are_equal : bool
     |
     |  serialize(self, alignment=8, memory_pool=None)
     |      Message.serialize(self, alignment=8, memory_pool=None)
     |
     |      Write message as encapsulated IPC message
     |
     |      Parameters
     |      ----------
     |      alignment : int, default 8
     |          Byte alignment for metadata and body
     |      memory_pool : MemoryPool, default None
     |          Uses default memory pool if not specified
     |
     |      Returns
     |      -------
     |      serialized : Buffer
     |
     |  serialize_to(self, sink, alignment=8, memory_pool=None)
     |      Message.serialize_to(self, NativeFile sink, alignment=8, memory_pool=None)
     |
     |      Write message to generic OutputStream
     |
     |      Parameters
     |      ----------
     |      sink : NativeFile
     |      alignment : int, default 8
     |          Byte alignment for metadata and body
     |      memory_pool : MemoryPool, default None
     |          Uses default memory pool if not specified
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  body
     |
     |  metadata
     |
     |  metadata_version
     |
     |  type

    class MessageReader(_Weakrefable)
     |  MessageReader()
     |
     |  Interface for reading Message objects from some source (like an
     |  InputStream)
     |
     |  Method resolution order:
     |      MessageReader
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __reduce__ = __reduce_cython__(...)
     |      MessageReader.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      MessageReader.__setstate_cython__(self, __pyx_state)
     |
     |  read_next_message(self)
     |      MessageReader.read_next_message(self)
     |
     |      Read next Message from the stream.
     |
     |      Raises
     |      ------
     |      StopIteration
     |          At end of stream
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  open_stream(source)
     |      MessageReader.open_stream(source)
     |
     |      Open stream from source, if you want to use memory map use
     |      MemoryMappedFile as source.
     |
     |      Parameters
     |      ----------
     |      source : bytes/buffer-like, pyarrow.NativeFile, or file-like Python object
     |          A readable source, like an InputStream

    class MetadataVersion(enum.IntFlag)
     |  MetadataVersion(*values)
     |
     |  Method resolution order:
     |      MetadataVersion
     |      enum.IntFlag
     |      builtins.int
     |      enum.ReprEnum
     |      enum.Flag
     |      enum.Enum
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __and__(self, other) from enum.Flag
     |
     |  __format__(self, format_spec, /) from builtins.int
     |      Convert to a string according to format_spec.
     |
     |  __invert__(self) from enum.Flag
     |
     |  __new__(cls, value) from enum.Enum
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  __or__(self, other) from enum.Flag
     |      Return self|value.
     |
     |  __rand__ = __and__(self, other)
     |
     |  __ror__ = __or__(self, other)
     |
     |  __rxor__ = __xor__(self, other)
     |
     |  __xor__(self, other) from enum.Flag
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  V1 = <MetadataVersion.V1: 0>
     |
     |  V2 = <MetadataVersion.V2: 1>
     |
     |  V3 = <MetadataVersion.V3: 2>
     |
     |  V4 = <MetadataVersion.V4: 3>
     |
     |  V5 = <MetadataVersion.V5: 4>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from enum.IntFlag:
     |
     |  __repr__(self) from enum.Flag
     |      Return repr(self).
     |
     |  __str__ = __repr__(self, /) from builtins.int
     |      Return repr(self).
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.int:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(self, value, /)
     |      Return self+value.
     |
     |  __bool__(self, /)
     |      True if self else False
     |
     |  __ceil__(...)
     |      Ceiling of an Integral returns itself.
     |
     |  __divmod__(self, value, /)
     |      Return divmod(self, value).
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __float__(self, /)
     |      float(self)
     |
     |  __floor__(...)
     |      Flooring an Integral returns itself.
     |
     |  __floordiv__(self, value, /)
     |      Return self//value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __getnewargs__(self, /)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  __int__(self, /)
     |      int(self)
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(self, value, /)
     |      Return self<<value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mod__(self, value, /)
     |      Return self%value.
     |
     |  __mul__(self, value, /)
     |      Return self*value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __pos__(self, /)
     |      +self
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rdivmod__(self, value, /)
     |      Return divmod(value, self).
     |
     |  __rfloordiv__(self, value, /)
     |      Return value//self.
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmod__(self, value, /)
     |      Return value%self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __round__(...)
     |      Rounding an Integral returns itself.
     |
     |      Rounding with an ndigits argument also returns an integer.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(self, value, /)
     |      Return self>>value.
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __sizeof__(self, /)
     |      Returns size in memory, in bytes.
     |
     |  __sub__(self, value, /)
     |      Return self-value.
     |
     |  __truediv__(self, value, /)
     |      Return self/value.
     |
     |  __trunc__(...)
     |      Truncating an Integral returns itself.
     |
     |  as_integer_ratio(self, /)
     |      Return a pair of integers, whose ratio is equal to the original int.
     |
     |      The ratio is in lowest terms and has a positive denominator.
     |
     |      >>> (10).as_integer_ratio()
     |      (10, 1)
     |      >>> (-10).as_integer_ratio()
     |      (-10, 1)
     |      >>> (0).as_integer_ratio()
     |      (0, 1)
     |
     |  bit_count(self, /)
     |      Number of ones in the binary representation of the absolute value of self.
     |
     |      Also known as the population count.
     |
     |      >>> bin(13)
     |      '0b1101'
     |      >>> (13).bit_count()
     |      3
     |
     |  bit_length(self, /)
     |      Number of bits necessary to represent self in binary.
     |
     |      >>> bin(37)
     |      '0b100101'
     |      >>> (37).bit_length()
     |      6
     |
     |  conjugate(...)
     |      Returns self, the complex conjugate of any int.
     |
     |  is_integer(self, /)
     |      Returns True. Exists for duck type compatibility with float.is_integer.
     |
     |  to_bytes(self, /, length=1, byteorder='big', *, signed=False)
     |      Return an array of bytes representing an integer.
     |
     |      length
     |        Length of bytes object to use.  An OverflowError is raised if the
     |        integer is not representable with the given number of bytes.  Default
     |        is length 1.
     |      byteorder
     |        The byte order used to represent the integer.  If byteorder is 'big',
     |        the most significant byte is at the beginning of the byte array.  If
     |        byteorder is 'little', the most significant byte is at the end of the
     |        byte array.  To request the native byte order of the host system, use
     |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
     |      signed
     |        Determines whether two's complement is used to represent the integer.
     |        If signed is False and a negative integer is given, an OverflowError
     |        is raised.
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from builtins.int:
     |
     |  from_bytes(bytes, byteorder='big', *, signed=False)
     |      Return the integer represented by the given array of bytes.
     |
     |      bytes
     |        Holds the array of bytes to convert.  The argument must either
     |        support the buffer protocol or be an iterable object producing bytes.
     |        Bytes and bytearray are examples of built-in objects that support the
     |        buffer protocol.
     |      byteorder
     |        The byte order used to represent the integer.  If byteorder is 'big',
     |        the most significant byte is at the beginning of the byte array.  If
     |        byteorder is 'little', the most significant byte is at the end of the
     |        byte array.  To request the native byte order of the host system, use
     |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
     |      signed
     |        Indicates whether two's complement is used to represent the integer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.int:
     |
     |  denominator
     |      the denominator of a rational number in lowest terms
     |
     |  imag
     |      the imaginary part of a complex number
     |
     |  numerator
     |      the numerator of a rational number in lowest terms
     |
     |  real
     |      the real part of a complex number
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from enum.Flag:
     |
     |  __contains__(self, other)
     |      Returns True if self has at least the same flags set as other.
     |
     |  __iter__(self)
     |      Returns flags in definition order.
     |
     |  __len__(self)
     |      Return the number of members (no aliases)
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from enum.Enum:
     |
     |  __dir__(self)
     |      Returns public methods and other interesting attributes.
     |
     |  __init__(self, *args, **kwds)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce_ex__(self, proto)
     |      Helper for pickle.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from enum.Enum:
     |
     |  name
     |      The name of the Enum member.
     |
     |  value
     |      The value of the Enum member.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from enum.EnumType:
     |
     |  __getitem__(name)
     |      Return the member matching `name`.
     |
     |  ----------------------------------------------------------------------
     |  Readonly properties inherited from enum.EnumType:
     |
     |  __members__
     |      Returns a mapping of member name->value.
     |
     |      This mapping lists all enum members, including aliases. Note that this
     |      is a read-only view of the internal mapping.

    class MockOutputStream(NativeFile)
     |  Method resolution order:
     |      MockOutputStream
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__ = __reduce_cython__(...)
     |      MockOutputStream.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      MockOutputStream.__setstate_cython__(self, __pyx_state)
     |
     |  size(self)
     |      MockOutputStream.size(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  fileno(self)
     |      NativeFile.fileno(self)
     |
     |      NOT IMPLEMENTED
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class MonthDayNanoIntervalArray(Array)
     |  Concrete class for Arrow arrays of interval[MonthDayNano] type.
     |
     |  Method resolution order:
     |      MonthDayNanoIntervalArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      MonthDayNanoIntervalArray.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      pyarrow.MonthDayNano is used as the native representation.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class MonthDayNanoIntervalScalar(Scalar)
     |  Concrete class for month, day, nanosecond interval scalars.
     |
     |  Method resolution order:
     |      MonthDayNanoIntervalScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      MonthDayNanoIntervalScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a pyarrow.MonthDayNano.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value
     |      Same as self.as_py()
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class NativeFile(_Weakrefable)
     |  The base class for all Arrow streams.
     |
     |  Streams are either readable, writable, or both.
     |  They optionally support seeking.
     |
     |  While this class exposes methods to read or write data from Python, the
     |  primary intent of using a Arrow stream is to pass it to other Arrow
     |  facilities that will make use of it, such as Arrow IPC routines.
     |
     |  Be aware that there are subtle differences with regular Python files,
     |  e.g. destroying a writable Arrow stream without closing it explicitly
     |  will not flush any pending data.
     |
     |  Method resolution order:
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __reduce__ = __reduce_cython__(...)
     |      NativeFile.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      NativeFile.__setstate_cython__(self, __pyx_state)
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  fileno(self)
     |      NativeFile.fileno(self)
     |
     |      NOT IMPLEMENTED
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class NullArray(Array)
     |  Concrete class for Arrow arrays of null data type.
     |
     |  Method resolution order:
     |      NullArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class NullScalar(Scalar)
     |  NullScalar()
     |
     |  Concrete class for null scalars.
     |
     |  Method resolution order:
     |      NullScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      NullScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python None.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class NumericArray(Array)
     |  A base class for Arrow numeric arrays.
     |
     |  Method resolution order:
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class OSFile(NativeFile)
     |  A stream backed by a regular file descriptor.
     |
     |  Examples
     |  --------
     |  Create a new file to write to:
     |
     |  >>> import pyarrow as pa
     |  >>> with pa.OSFile('example_osfile.arrow', mode='w') as f:
     |  ...     f.writable()
     |  ...     f.write(b'OSFile')
     |  ...     f.seekable()
     |  ...
     |  True
     |  6
     |  False
     |
     |  Open the file to read:
     |
     |  >>> with pa.OSFile('example_osfile.arrow', mode='r') as f:
     |  ...     f.mode
     |  ...     f.read()
     |  ...
     |  'rb'
     |  b'OSFile'
     |
     |  Open the file to append:
     |
     |  >>> with pa.OSFile('example_osfile.arrow', mode='ab') as f:
     |  ...     f.mode
     |  ...     f.write(b' is super!')
     |  ...
     |  'ab'
     |  10
     |  >>> with pa.OSFile('example_osfile.arrow') as f:
     |  ...     f.read()
     |  ...
     |  b'OSFile is super!'
     |
     |  Inspect created OSFile:
     |
     |  >>> pa.OSFile('example_osfile.arrow')
     |  <pyarrow.OSFile closed=False own_file=False is_seekable=True is_writable=False is_readable=True>
     |
     |  Method resolution order:
     |      OSFile
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__ = __reduce_cython__(...)
     |      OSFile.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      OSFile.__setstate_cython__(self, __pyx_state)
     |
     |  fileno(self)
     |      OSFile.fileno(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class OpaqueArray(ExtensionArray)
     |  Concrete class for opaque extension arrays.
     |
     |  Examples
     |  --------
     |  Define the extension type for an opaque array
     |
     |  >>> import pyarrow as pa
     |  >>> opaque_type = pa.opaque(
     |  ...     pa.binary(),
     |  ...     type_name="geometry",
     |  ...     vendor_name="postgis",
     |  ... )
     |
     |  Create an extension array
     |
     |  >>> arr = [None, b"data"]
     |  >>> storage = pa.array(arr, pa.binary())
     |  >>> pa.ExtensionArray.from_storage(opaque_type, storage)
     |  <pyarrow.lib.OpaqueArray object at ...>
     |  [
     |    null,
     |    64617461
     |  ]
     |
     |  Method resolution order:
     |      OpaqueArray
     |      ExtensionArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from ExtensionArray:
     |
     |  from_storage(typ, storage)
     |      ExtensionArray.from_storage(BaseExtensionType typ, Array storage)
     |
     |      Construct ExtensionArray from type and storage array.
     |
     |      Parameters
     |      ----------
     |      typ : DataType
     |          The extension type for the result array.
     |      storage : Array
     |          The underlying storage for the result array.
     |
     |      Returns
     |      -------
     |      ext_array : ExtensionArray
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ExtensionArray:
     |
     |  storage
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class OpaqueScalar(ExtensionScalar)
     |  Concrete class for opaque extension scalar.
     |
     |  Method resolution order:
     |      OpaqueScalar
     |      ExtensionScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from ExtensionScalar:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      ExtensionScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this scalar as a Python object.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from ExtensionScalar:
     |
     |  from_storage(typ, value)
     |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
     |
     |      Construct ExtensionScalar from type and storage value.
     |
     |      Parameters
     |      ----------
     |      typ : DataType
     |          The extension type for the result scalar.
     |      value : object
     |          The storage value for the result scalar.
     |
     |      Returns
     |      -------
     |      ext_scalar : ExtensionScalar
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ExtensionScalar:
     |
     |  value
     |      Return storage value as a scalar.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class OpaqueType(BaseExtensionType)
     |  Concrete class for opaque extension type.
     |
     |  Opaque is a placeholder for a type from an external (often non-Arrow)
     |  system that could not be interpreted.
     |
     |  Examples
     |  --------
     |  Create an instance of opaque extension type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.opaque(pa.int32(), "geometry", "postgis")
     |  OpaqueType(extension<arrow.opaque[storage_type=int32, type_name=geometry, vendor_name=postgis]>)
     |
     |  Method resolution order:
     |      OpaqueType
     |      BaseExtensionType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_ext_class__(self)
     |      OpaqueType.__arrow_ext_class__(self)
     |
     |  __arrow_ext_scalar_class__(self)
     |      OpaqueType.__arrow_ext_scalar_class__(self)
     |
     |  __reduce__(self)
     |      OpaqueType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  type_name
     |      The name of the type in the external system.
     |
     |  vendor_name
     |      The name of the external system.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseExtensionType:
     |
     |  wrap_array(self, storage)
     |      BaseExtensionType.wrap_array(self, storage)
     |
     |      Wrap the given storage array as an extension array.
     |
     |      Parameters
     |      ----------
     |      storage : Array or ChunkedArray
     |
     |      Returns
     |      -------
     |      array : Array or ChunkedArray
     |          Extension array wrapping the storage array
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from BaseExtensionType:
     |
     |  bit_width
     |      The bit width of the extension type.
     |
     |  byte_width
     |      The byte width of the extension type.
     |
     |  extension_name
     |      The extension type name.
     |
     |  storage_type
     |      The underlying storage type.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class ProxyMemoryPool(MemoryPool)
     |  ProxyMemoryPool()
     |
     |  Memory pool implementation that tracks the number of bytes and
     |  maximum memory allocated through its direct calls, while redirecting
     |  to another memory pool.
     |
     |  Method resolution order:
     |      ProxyMemoryPool
     |      MemoryPool
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      ProxyMemoryPool.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      ProxyMemoryPool.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from MemoryPool:
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  bytes_allocated(self)
     |      MemoryPool.bytes_allocated(self)
     |
     |      Return the number of bytes that are currently allocated from this
     |      memory pool.
     |
     |  max_memory(self)
     |      MemoryPool.max_memory(self)
     |
     |      Return the peak memory allocation in this memory pool.
     |      This can be an approximate number in multi-threaded applications.
     |
     |      None is returned if the pool implementation doesn't know how to
     |      compute this number.
     |
     |  num_allocations(self)
     |      MemoryPool.num_allocations(self)
     |
     |      Return the number of allocations or reallocations that were made
     |      using this memory pool.
     |
     |  print_stats(self)
     |      MemoryPool.print_stats(self)
     |
     |      Print statistics about this memory pool.
     |
     |      The output format is implementation-specific. Not all memory pools
     |      implement this method.
     |
     |  release_unused(self)
     |      MemoryPool.release_unused(self)
     |
     |      Attempt to return to the OS any memory being held onto by the pool.
     |
     |      This function should not be called except potentially for
     |      benchmarking or debugging as it could be expensive and detrimental to
     |      performance.
     |
     |      This is best effort and may not have any effect on some memory pools
     |      or in some situations (e.g. fragmentation).
     |
     |  total_bytes_allocated(self)
     |      MemoryPool.total_bytes_allocated(self)
     |
     |      Return the total number of bytes that have been allocated from this
     |      memory pool.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from MemoryPool:
     |
     |  backend_name
     |      The name of the backend used by this MemoryPool (e.g. "jemalloc").

    class PythonFile(NativeFile)
     |  A stream backed by a Python file object.
     |
     |  This class allows using Python file objects with arbitrary Arrow
     |  functions, including functions written in another language than Python.
     |
     |  As a downside, there is a non-zero redirection cost in translating
     |  Arrow stream calls to Python method calls.  Furthermore, Python's
     |  Global Interpreter Lock may limit parallelism in some situations.
     |
     |  Examples
     |  --------
     |  >>> import io
     |  >>> import pyarrow as pa
     |  >>> pa.PythonFile(io.BytesIO())
     |  <pyarrow.PythonFile closed=False own_file=False is_seekable=False is_writable=True is_readable=False>
     |
     |  Create a stream for writing:
     |
     |  >>> buf = io.BytesIO()
     |  >>> f =  pa.PythonFile(buf, mode = 'w')
     |  >>> f.writable()
     |  True
     |  >>> f.write(b'PythonFile')
     |  10
     |  >>> buf.getvalue()
     |  b'PythonFile'
     |  >>> f.close()
     |  >>> f
     |  <pyarrow.PythonFile closed=True own_file=False is_seekable=False is_writable=True is_readable=False>
     |
     |  Create a stream for reading:
     |
     |  >>> buf = io.BytesIO(b'PythonFile')
     |  >>> f =  pa.PythonFile(buf, mode = 'r')
     |  >>> f.mode
     |  'rb'
     |  >>> f.read()
     |  b'PythonFile'
     |  >>> f
     |  <pyarrow.PythonFile closed=False own_file=False is_seekable=True is_writable=False is_readable=True>
     |  >>> f.close()
     |  >>> f
     |  <pyarrow.PythonFile closed=True own_file=False is_seekable=True is_writable=False is_readable=True>
     |
     |  Method resolution order:
     |      PythonFile
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__ = __reduce_cython__(...)
     |      PythonFile.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      PythonFile.__setstate_cython__(self, __pyx_state)
     |
     |  readline(self, size=None)
     |      PythonFile.readline(self, size=None)
     |
     |      Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Parameters
     |      ----------
     |      size : int
     |          Maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      PythonFile.readlines(self, hint=None)
     |
     |      Read lines of the file.
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          Maximum number of bytes read until we stop
     |
     |  truncate(self, pos=None)
     |      PythonFile.truncate(self, pos=None)
     |
     |      Parameters
     |      ----------
     |      pos : int, optional
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  fileno(self)
     |      NativeFile.fileno(self)
     |
     |      NOT IMPLEMENTED
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class ReadStats(_frozen_importlib.ReadStats)
     |  ReadStats(num_messages, num_record_batches, num_dictionary_batches, num_dictionary_deltas, num_replaced_dictionaries)
     |
     |  IPC read statistics
     |
     |  Parameters
     |  ----------
     |  num_messages : int
     |      Number of messages.
     |  num_record_batches : int
     |      Number of record batches.
     |  num_dictionary_batches : int
     |      Number of dictionary batches.
     |  num_dictionary_deltas : int
     |      Delta of dictionaries.
     |  num_replaced_dictionaries : int
     |      Number of replaced dictionaries.
     |
     |  Method resolution order:
     |      ReadStats
     |      _frozen_importlib.ReadStats
     |      builtins.tuple
     |      builtins.object
     |
     |  Methods inherited from _frozen_importlib.ReadStats:
     |
     |  __getnewargs__(self) from collections.ReadStats
     |      Return self as a plain tuple.  Used by copy and pickle.
     |
     |  __repr__(self) from collections.ReadStats
     |      Return a nicely formatted representation string
     |
     |  _asdict(self) from collections.ReadStats
     |      Return a new dict which maps field names to their values.
     |
     |  _replace(self, /, **kwds) from collections.ReadStats
     |      Return a new ReadStats object replacing specified fields with new values
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from _frozen_importlib.ReadStats:
     |
     |  _make(iterable) from collections.ReadStats
     |      Make a new ReadStats object from a sequence or iterable
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from _frozen_importlib.ReadStats:
     |
     |  __new__(_cls, num_messages, num_record_batches, num_dictionary_batches, num_dictionary_deltas, num_replaced_dictionaries) from namedtuple_ReadStats.ReadStats
     |      Create new instance of ReadStats(num_messages, num_record_batches, num_dictionary_batches, num_dictionary_deltas, num_replaced_dictionaries)
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from _frozen_importlib.ReadStats:
     |
     |  num_messages
     |      Alias for field number 0
     |
     |  num_record_batches
     |      Alias for field number 1
     |
     |  num_dictionary_batches
     |      Alias for field number 2
     |
     |  num_dictionary_deltas
     |      Alias for field number 3
     |
     |  num_replaced_dictionaries
     |      Alias for field number 4
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from _frozen_importlib.ReadStats:
     |
     |  __match_args__ = ('num_messages', 'num_record_batches', 'num_dictionar...
     |
     |  _field_defaults = {}
     |
     |  _fields = ('num_messages', 'num_record_batches', 'num_dictionary_batch...
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.tuple:
     |
     |  __add__(self, value, /)
     |      Return self+value.
     |
     |  __contains__(self, key, /)
     |      Return bool(key in self).
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __getitem__(self, key, /)
     |      Return self[key].
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(self, value, /)
     |      Return self*value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  count(self, value, /)
     |      Return number of occurrences of value.
     |
     |  index(self, value, start=0, stop=9223372036854775807, /)
     |      Return first index of value.
     |
     |      Raises ValueError if the value is not present.
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from builtins.tuple:
     |
     |  __class_getitem__(...)
     |      See PEP 585

    class RecordBatch(_Tabular)
     |  Batch of rows of columns of equal length
     |
     |  Warnings
     |  --------
     |  Do not call this class's constructor directly, use one of the
     |  ``RecordBatch.from_*`` functions instead.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |  >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |  >>> names = ["n_legs", "animals"]
     |
     |  Constructing a RecordBatch from arrays:
     |
     |  >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names)
     |  pyarrow.RecordBatch
     |  n_legs: int64
     |  animals: string
     |  ----
     |  n_legs: [2,2,4,4,5,100]
     |  animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
     |  >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas()
     |     n_legs        animals
     |  0       2       Flamingo
     |  1       2         Parrot
     |  2       4            Dog
     |  3       4          Horse
     |  4       5  Brittle stars
     |  5     100      Centipede
     |
     |  Constructing a RecordBatch from pandas DataFrame:
     |
     |  >>> import pandas as pd
     |  >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022],
     |  ...                    'month': [3, 5, 7, 9],
     |  ...                    'day': [1, 5, 9, 13],
     |  ...                    'n_legs': [2, 4, 5, 100],
     |  ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |  >>> pa.RecordBatch.from_pandas(df)
     |  pyarrow.RecordBatch
     |  year: int64
     |  month: int64
     |  day: int64
     |  n_legs: int64
     |  animals: large_string
     |  ----
     |  year: [2020,2022,2021,2022]
     |  month: [3,5,7,9]
     |  day: [1,5,9,13]
     |  n_legs: [2,4,5,100]
     |  animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |  >>> pa.RecordBatch.from_pandas(df).to_pandas()
     |     year  month  day  n_legs        animals
     |  0  2020      3    1       2       Flamingo
     |  1  2022      5    5       4          Horse
     |  2  2021      7    9       5  Brittle stars
     |  3  2022      9   13     100      Centipede
     |
     |  Constructing a RecordBatch from pylist:
     |
     |  >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'},
     |  ...           {'n_legs': 4, 'animals': 'Dog'}]
     |  >>> pa.RecordBatch.from_pylist(pylist).to_pandas()
     |     n_legs   animals
     |  0       2  Flamingo
     |  1       4       Dog
     |
     |  You can also construct a RecordBatch using :func:`pyarrow.record_batch`:
     |
     |  >>> pa.record_batch([n_legs, animals], names=names).to_pandas()
     |     n_legs        animals
     |  0       2       Flamingo
     |  1       2         Parrot
     |  2       4            Dog
     |  3       4          Horse
     |  4       5  Brittle stars
     |  5     100      Centipede
     |
     |  >>> pa.record_batch(df)
     |  pyarrow.RecordBatch
     |  year: int64
     |  month: int64
     |  day: int64
     |  n_legs: int64
     |  animals: large_string
     |  ----
     |  year: [2020,2022,2021,2022]
     |  month: [3,5,7,9]
     |  day: [1,5,9,13]
     |  n_legs: [2,4,5,100]
     |  animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |
     |  Method resolution order:
     |      RecordBatch
     |      _Tabular
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      RecordBatch.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the batch to this schema.
     |          If None, the batch will be returned as-is, with a schema matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      RecordBatch.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the batch to this data type.
     |          If None, the batch will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __arrow_c_stream__(self, requested_schema=None)
     |      RecordBatch.__arrow_c_stream__(self, requested_schema=None)
     |
     |      Export the batch as an Arrow C stream PyCapsule.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule, default None
     |          The schema to which the stream should be casted, passed as a
     |          PyCapsule containing a C ArrowSchema representation of the
     |          requested schema.
     |          Currently, this is not supported and will raise a
     |          NotImplementedError if the schema doesn't match the current schema.
     |
     |      Returns
     |      -------
     |      PyCapsule
     |
     |  __reduce__(self)
     |      RecordBatch.__reduce__(self)
     |
     |  __sizeof__(self)
     |      RecordBatch.__sizeof__(self)
     |
     |  add_column(self, i, field_, column)
     |      RecordBatch.add_column(self, int i, field_, column)
     |
     |      Add column to RecordBatch at position i.
     |
     |      A new record batch is returned with the column added, the original record batch
     |      object is left unchanged.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          Index to place the column at.
     |      field_ : str or Field
     |          If a string is passed then the type is deduced from the column
     |          data.
     |      column : Array or value coercible to array
     |          Column data.
     |
     |      Returns
     |      -------
     |      RecordBatch
     |          New record batch with the passed column added.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> batch = pa.RecordBatch.from_arrays(
     |      ...     [[2, 4, 5, 100], ["Flamingo", "Horse", "Brittle stars", "Centipede"]],
     |      ...     names=['n_legs', 'animals'])
     |
     |      Add column:
     |
     |      >>> year = [2021, 2022, 2019, 2021]
     |      >>> batch.add_column(0,"year", year)
     |      pyarrow.RecordBatch
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [2021,2022,2019,2021]
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |
     |      Original record batch is left unchanged:
     |
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |
     |  cast(self, target_schema, safe=None, options=None)
     |      RecordBatch.cast(self, Schema target_schema, safe=None, options=None)
     |
     |      Cast record batch values to another schema.
     |
     |      Parameters
     |      ----------
     |      target_schema : Schema
     |          Schema to cast to, the names and order of fields must match.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |
     |      Returns
     |      -------
     |      RecordBatch
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> batch = pa.RecordBatch.from_arrays(
     |      ...     [[2, 4, 5, 100], ["Flamingo", "Horse", "Brittle stars", "Centipede"]],
     |      ...     names=['n_legs', 'animals'])
     |      >>> batch.schema
     |      n_legs: int64
     |      animals: string
     |
     |      Define new schema and cast batch values:
     |
     |      >>> my_schema = pa.schema([
     |      ...     pa.field('n_legs', pa.duration('s')),
     |      ...     pa.field('animals', pa.string())]
     |      ...     )
     |      >>> batch.cast(target_schema=my_schema)
     |      pyarrow.RecordBatch
     |      n_legs: duration[s]
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |
     |  copy_to(self, destination)
     |      RecordBatch.copy_to(self, destination)
     |
     |      Copy the entire RecordBatch to destination device.
     |
     |      This copies each column of the record batch to create
     |      a new record batch where all underlying buffers for the columns have
     |      been copied to the destination MemoryManager.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      RecordBatch
     |
     |  equals(self, other, check_metadata=False)
     |      RecordBatch.equals(self, other, bool check_metadata=False)
     |
     |      Check if contents of two record batches are equal.
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.RecordBatch
     |          RecordBatch to compare against.
     |      check_metadata : bool, default False
     |          Whether schema metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      are_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
     |      ...                                     names=["n_legs", "animals"])
     |      >>> batch_0 = pa.record_batch([])
     |      >>> batch_1 = pa.RecordBatch.from_arrays([n_legs, animals],
     |      ...                                       names=["n_legs", "animals"],
     |      ...                                       metadata={"n_legs": "Number of legs per animal"})
     |      >>> batch.equals(batch)
     |      True
     |      >>> batch.equals(batch_0)
     |      False
     |      >>> batch.equals(batch_1)
     |      True
     |      >>> batch.equals(batch_1, check_metadata=True)
     |      False
     |
     |  get_total_buffer_size(self)
     |      RecordBatch.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the record batch
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
     |      ...                                     names=["n_legs", "animals"])
     |      >>> batch.get_total_buffer_size()
     |      120
     |
     |  remove_column(self, i)
     |      RecordBatch.remove_column(self, int i)
     |
     |      Create new RecordBatch with the indicated column removed.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          Index of column to remove.
     |
     |      Returns
     |      -------
     |      Table
     |          New record batch without the column.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> batch = pa.RecordBatch.from_arrays(
     |      ...     [[2, 4, 5, 100], ["Flamingo", "Horse", "Brittle stars", "Centipede"]],
     |      ...     names=['n_legs', 'animals'])
     |      >>> batch.remove_column(1)
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      ----
     |      n_legs: [2,4,5,100]
     |
     |  rename_columns(self, names)
     |      RecordBatch.rename_columns(self, names)
     |
     |      Create new record batch with columns renamed to provided names.
     |
     |      Parameters
     |      ----------
     |      names : list[str] or dict[str, str]
     |          List of new column names or mapping of old column names to new column names.
     |
     |          If a mapping of old to new column names is passed, then all columns which are
     |          found to match a provided old column name will be renamed to the new column name.
     |          If any column names are not found in the mapping, a KeyError will be raised.
     |
     |      Raises
     |      ------
     |      KeyError
     |          If any of the column names passed in the names mapping do not exist.
     |
     |      Returns
     |      -------
     |      RecordBatch
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> batch = pa.RecordBatch.from_arrays(
     |      ...     [[2, 4, 5, 100], ["Flamingo", "Horse", "Brittle stars", "Centipede"]],
     |      ...     names=['n_legs', 'animals'])
     |      >>> new_names = ["n", "name"]
     |      >>> batch.rename_columns(new_names)
     |      pyarrow.RecordBatch
     |      n: int64
     |      name: string
     |      ----
     |      n: [2,4,5,100]
     |      name: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> new_names = {"n_legs": "n", "animals": "name"}
     |      >>> batch.rename_columns(new_names)
     |      pyarrow.RecordBatch
     |      n: int64
     |      name: string
     |      ----
     |      n: [2,4,5,100]
     |      name: ["Flamingo","Horse","Brittle stars","Centipede"]
     |
     |  replace_schema_metadata(self, metadata=None)
     |      RecordBatch.replace_schema_metadata(self, metadata=None)
     |
     |      Create shallow copy of record batch by replacing schema
     |      key-value metadata with the indicated new metadata (which may be None,
     |      which deletes any existing metadata
     |
     |      Parameters
     |      ----------
     |      metadata : dict, default None
     |
     |      Returns
     |      -------
     |      shallow_copy : RecordBatch
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |
     |      Constructing a RecordBatch with schema and metadata:
     |
     |      >>> my_schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |      >>> batch = pa.RecordBatch.from_arrays([n_legs], schema=my_schema)
     |      >>> batch.schema
     |      n_legs: int64
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |      Shallow copy of a RecordBatch with deleted schema metadata:
     |
     |      >>> batch.replace_schema_metadata().schema
     |      n_legs: int64
     |
     |  select(self, columns)
     |      RecordBatch.select(self, columns)
     |
     |      Select columns of the RecordBatch.
     |
     |      Returns a new RecordBatch with the specified columns, and metadata
     |      preserved.
     |
     |      Parameters
     |      ----------
     |      columns : list-like
     |          The column names or integer indices to select.
     |
     |      Returns
     |      -------
     |      RecordBatch
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                          names=["n_legs", "animals"])
     |
     |      Select columns my indices:
     |
     |      >>> batch.select([1])
     |      pyarrow.RecordBatch
     |      animals: string
     |      ----
     |      animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
     |
     |      Select columns by names:
     |
     |      >>> batch.select(["n_legs"])
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      ----
     |      n_legs: [2,2,4,4,5,100]
     |
     |  serialize(self, memory_pool=None)
     |      RecordBatch.serialize(self, memory_pool=None)
     |
     |      Write RecordBatch to Buffer as encapsulated IPC message, which does not
     |      include a Schema.
     |
     |      To reconstruct a RecordBatch from the encapsulated IPC message Buffer
     |      returned by this function, a Schema must be passed separately. See
     |      Examples.
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Uses default memory pool if not specified
     |
     |      Returns
     |      -------
     |      serialized : Buffer
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
     |      ...                                     names=["n_legs", "animals"])
     |      >>> buf = batch.serialize()
     |      >>> buf
     |      <pyarrow.Buffer address=0x... size=... is_cpu=True is_mutable=True>
     |
     |      Reconstruct RecordBatch from IPC message Buffer and original Schema
     |
     |      >>> pa.ipc.read_record_batch(buf, batch.schema)
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,2,4,4,5,100]
     |      animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
     |
     |  set_column(self, i, field_, column)
     |      RecordBatch.set_column(self, int i, field_, column)
     |
     |      Replace column in RecordBatch at position.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          Index to place the column at.
     |      field_ : str or Field
     |          If a string is passed then the type is deduced from the column
     |          data.
     |      column : Array or value coercible to array
     |          Column data.
     |
     |      Returns
     |      -------
     |      RecordBatch
     |          New record batch with the passed column set.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> batch = pa.RecordBatch.from_arrays(
     |      ...     [[2, 4, 5, 100], ["Flamingo", "Horse", "Brittle stars", "Centipede"]],
     |      ...     names=['n_legs', 'animals'])
     |
     |      Replace a column:
     |
     |      >>> year = [2021, 2022, 2019, 2021]
     |      >>> batch.set_column(1,'year', year)
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      year: int64
     |      ----
     |      n_legs: [2,4,5,100]
     |      year: [2021,2022,2019,2021]
     |
     |  slice(self, offset=0, length=None)
     |      RecordBatch.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this RecordBatch
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of record batch to slice
     |      length : int, default None
     |          Length of slice (default is until end of batch starting from
     |          offset)
     |
     |      Returns
     |      -------
     |      sliced : RecordBatch
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
     |      ...                                     names=["n_legs", "animals"])
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       2         Parrot
     |      2       4            Dog
     |      3       4          Horse
     |      4       5  Brittle stars
     |      5     100      Centipede
     |      >>> batch.slice(offset=3).to_pandas()
     |         n_legs        animals
     |      0       4          Horse
     |      1       5  Brittle stars
     |      2     100      Centipede
     |      >>> batch.slice(length=2).to_pandas()
     |         n_legs   animals
     |      0       2  Flamingo
     |      1       2    Parrot
     |      >>> batch.slice(offset=3, length=1).to_pandas()
     |         n_legs animals
     |      0       4   Horse
     |
     |  to_struct_array(self)
     |      RecordBatch.to_struct_array(self)
     |
     |      Convert to a struct array.
     |
     |  to_tensor(self, null_to_nan=False, row_major=True, memory_pool=None)
     |      RecordBatch.to_tensor(self, bool null_to_nan=False, bool row_major=True, MemoryPool memory_pool=None)
     |
     |      Convert to a :class:`~pyarrow.Tensor`.
     |
     |      RecordBatches that can be converted have fields of type signed or unsigned
     |      integer or float, including all bit-widths.
     |
     |      ``null_to_nan`` is ``False`` by default and this method will raise an error in case
     |      any nulls are present. RecordBatches with nulls can be converted with ``null_to_nan``
     |      set to ``True``. In this case null values are converted to ``NaN`` and integer type
     |      arrays are promoted to the appropriate float type.
     |
     |      Parameters
     |      ----------
     |      null_to_nan : bool, default False
     |          Whether to write null values in the result as ``NaN``.
     |      row_major : bool, default True
     |          Whether resulting Tensor is row-major or column-major
     |      memory_pool : MemoryPool, default None
     |          For memory allocations, if required, otherwise use default pool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> batch = pa.record_batch(
     |      ...    [
     |      ...         pa.array([1, 2, 3, 4, None], type=pa.int32()),
     |      ...         pa.array([10, 20, 30, 40, None], type=pa.float32()),
     |      ...     ], names = ["a", "b"]
     |      ... )
     |
     |      >>> batch
     |      pyarrow.RecordBatch
     |      a: int32
     |      b: float
     |      ----
     |      a: [1,2,3,4,null]
     |      b: [10,20,30,40,null]
     |
     |      Convert a RecordBatch to row-major Tensor with null values
     |      written as NaN values
     |
     |      >>> batch.to_tensor(null_to_nan=True)
     |      <pyarrow.Tensor>
     |      type: double
     |      shape: (5, 2)
     |      strides: (16, 8)
     |      >>> batch.to_tensor(null_to_nan=True).to_numpy()
     |      array([[ 1., 10.],
     |             [ 2., 20.],
     |             [ 3., 30.],
     |             [ 4., 40.],
     |             [nan, nan]])
     |
     |      Convert a RecordBatch to column-major Tensor
     |
     |      >>> batch.to_tensor(null_to_nan=True, row_major=False)
     |      <pyarrow.Tensor>
     |      type: double
     |      shape: (5, 2)
     |      strides: (8, 40)
     |      >>> batch.to_tensor(null_to_nan=True, row_major=False).to_numpy()
     |      array([[ 1., 10.],
     |             [ 2., 20.],
     |             [ 3., 30.],
     |             [ 4., 40.],
     |             [nan, nan]])
     |
     |  validate(self, *, full=False)
     |      RecordBatch.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Class methods defined here:
     |
     |  from_pandas(df, schema=None, preserve_index=None, nthreads=None, columns=None)
     |      RecordBatch.from_pandas(cls, df, Schema schema=None, preserve_index=None, nthreads=None, columns=None)
     |
     |      Convert pandas.DataFrame to an Arrow RecordBatch
     |
     |      Parameters
     |      ----------
     |      df : pandas.DataFrame
     |      schema : pyarrow.Schema, optional
     |          The expected schema of the RecordBatch. This can be used to
     |          indicate the type of columns if we cannot infer it automatically.
     |          If passed, the output will have exactly this schema. Columns
     |          specified in the schema that are not found in the DataFrame columns
     |          or its index will raise an error. Additional columns or index
     |          levels in the DataFrame which are not specified in the schema will
     |          be ignored.
     |      preserve_index : bool, optional
     |          Whether to store the index as an additional column in the resulting
     |          ``RecordBatch``. The default of None will store the index as a
     |          column, except for RangeIndex which is stored as metadata only. Use
     |          ``preserve_index=True`` to force it to be stored as a column.
     |      nthreads : int, default None
     |          If greater than 1, convert columns to Arrow in parallel using
     |          indicated number of threads. By default, this follows
     |          :func:`pyarrow.cpu_count` (may use up to system CPU count threads).
     |      columns : list, optional
     |         List of column to be converted. If None, use all columns.
     |
     |      Returns
     |      -------
     |      pyarrow.RecordBatch
     |
     |
     |      Examples
     |      --------
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022],
     |      ...                    'month': [3, 5, 7, 9],
     |      ...                    'day': [1, 5, 9, 13],
     |      ...                    'n_legs': [2, 4, 5, 100],
     |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |
     |      Convert pandas DataFrame to RecordBatch:
     |
     |      >>> import pyarrow as pa
     |      >>> pa.RecordBatch.from_pandas(df)
     |      pyarrow.RecordBatch
     |      year: int64
     |      month: int64
     |      day: int64
     |      n_legs: int64
     |      animals: large_string
     |      ----
     |      year: [2020,2022,2021,2022]
     |      month: [3,5,7,9]
     |      day: [1,5,9,13]
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |
     |      Convert pandas DataFrame to RecordBatch using schema:
     |
     |      >>> my_schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |      >>> pa.RecordBatch.from_pandas(df, schema=my_schema)
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |
     |      Convert pandas DataFrame to RecordBatch specifying columns:
     |
     |      >>> pa.RecordBatch.from_pandas(df, columns=["n_legs"])
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      ----
     |      n_legs: [2,4,5,100]
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_arrays(arrays, names=None, schema=None, metadata=None)
     |      RecordBatch.from_arrays(list arrays, names=None, schema=None, metadata=None)
     |
     |      Construct a RecordBatch from multiple pyarrow.Arrays
     |
     |      Parameters
     |      ----------
     |      arrays : list of pyarrow.Array
     |          One for each field in RecordBatch
     |      names : list of str, optional
     |          Names for the batch fields. If not passed, schema must be passed
     |      schema : Schema, default None
     |          Schema for the created batch. If not passed, names must be passed
     |      metadata : dict or Mapping, default None
     |          Optional metadata for the schema (if inferred).
     |
     |      Returns
     |      -------
     |      pyarrow.RecordBatch
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> names = ["n_legs", "animals"]
     |
     |      Construct a RecordBatch from pyarrow Arrays using names:
     |
     |      >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names)
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,2,4,4,5,100]
     |      animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
     |      >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       2         Parrot
     |      2       4            Dog
     |      3       4          Horse
     |      4       5  Brittle stars
     |      5     100      Centipede
     |
     |      Construct a RecordBatch from pyarrow Arrays using schema:
     |
     |      >>> my_schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |      >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       2         Parrot
     |      2       4            Dog
     |      3       4          Horse
     |      4       5  Brittle stars
     |      5     100      Centipede
     |      >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |  from_struct_array(struct_array)
     |      RecordBatch.from_struct_array(StructArray struct_array)
     |
     |      Construct a RecordBatch from a StructArray.
     |
     |      Each field in the StructArray will become a column in the resulting
     |      ``RecordBatch``.
     |
     |      Parameters
     |      ----------
     |      struct_array : StructArray
     |          Array to construct the record batch from.
     |
     |      Returns
     |      -------
     |      pyarrow.RecordBatch
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
     |      ...                    {'year': 2022, 'n_legs': 4, 'animals': 'Goat'}])
     |      >>> pa.RecordBatch.from_struct_array(struct).to_pandas()
     |         n_legs animals    year
     |      0       2  Parrot     NaN
     |      1       4    Goat  2022.0
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  device_type
     |      The device type where the arrays in the RecordBatch reside.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the RecordBatch's arrays are CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the record batch.
     |
     |      In other words, the sum of bytes from all buffer ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will only be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
     |      ...                                     names=["n_legs", "animals"])
     |      >>> batch.nbytes
     |      116
     |
     |  num_columns
     |      Number of columns
     |
     |      Returns
     |      -------
     |      int
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
     |      ...                                     names=["n_legs", "animals"])
     |      >>> batch.num_columns
     |      2
     |
     |  num_rows
     |      Number of rows
     |
     |      Due to the definition of a RecordBatch, all columns have the same
     |      number of rows.
     |
     |      Returns
     |      -------
     |      int
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
     |      ...                                     names=["n_legs", "animals"])
     |      >>> batch.num_rows
     |      6
     |
     |  schema
     |      Schema of the RecordBatch and its columns
     |
     |      Returns
     |      -------
     |      pyarrow.Schema
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
     |      ...                                     names=["n_legs", "animals"])
     |      >>> batch.schema
     |      n_legs: int64
     |      animals: string
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _Tabular:
     |
     |  __array__(self, dtype=None, copy=None)
     |      _Tabular.__array__(self, dtype=None, copy=None)
     |
     |  __dataframe__(self, nan_as_null: 'bool' = False, allow_copy: 'bool' = True)
     |      _Tabular.__dataframe__(self, bool nan_as_null: bool = False, bool allow_copy: bool = True)
     |
     |      Return the dataframe interchange object implementing the interchange protocol.
     |
     |      Parameters
     |      ----------
     |      nan_as_null : bool, default False
     |          Whether to tell the DataFrame to overwrite null values in the data
     |          with ``NaN`` (or ``NaT``).
     |      allow_copy : bool, default True
     |          Whether to allow memory copying when exporting. If set to False
     |          it would cause non-zero-copy exports to fail.
     |
     |      Returns
     |      -------
     |      DataFrame interchange object
     |          The object which consuming library can use to ingress the dataframe.
     |
     |      Notes
     |      -----
     |      Details on the interchange protocol:
     |      https://data-apis.org/dataframe-protocol/latest/index.html
     |      `nan_as_null` currently has no effect; once support for nullable extension
     |      dtypes is added, this value should be propagated to columns.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      _Tabular.__getitem__(self, key)
     |
     |      Slice or return column at given index or column name
     |
     |      Parameters
     |      ----------
     |      key : integer, str, or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      Array (from RecordBatch) or ChunkedArray (from Table) for column input.
     |      RecordBatch or Table for slice input.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce_cython__(self)
     |      _Tabular.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _Tabular.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _Tabular.__setstate_cython__(self, __pyx_state)
     |
     |  append_column(self, field_, column)
     |      _Tabular.append_column(self, field_, column)
     |
     |      Append column at end of columns.
     |
     |      Parameters
     |      ----------
     |      field_ : str or Field
     |          If a string is passed then the type is deduced from the column
     |          data.
     |      column : Array or value coercible to array
     |          Column data.
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |          New table or record batch with the passed column added.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |
     |      Append column at the end:
     |
     |      >>> year = [2021, 2022, 2019, 2021]
     |      >>> table.append_column('year', [year])
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      year: int64
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |      year: [[2021,2022,2019,2021]]
     |
     |  column(self, i)
     |      _Tabular.column(self, i)
     |
     |      Select single column from Table or RecordBatch.
     |
     |      Parameters
     |      ----------
     |      i : int or string
     |          The index or name of the column to retrieve.
     |
     |      Returns
     |      -------
     |      column : Array (for RecordBatch) or ChunkedArray (for Table)
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
     |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table = pa.Table.from_pandas(df)
     |
     |      Select a column by numeric index:
     |
     |      >>> table.column(0)
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |
     |      Select a column by its name:
     |
     |      >>> table.column("animals")
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          "Flamingo",
     |          "Horse",
     |          "Brittle stars",
     |          "Centipede"
     |        ]
     |      ]
     |
     |  drop_columns(self, columns)
     |      _Tabular.drop_columns(self, columns)
     |
     |      Drop one or more columns and return a new Table or RecordBatch.
     |
     |      Parameters
     |      ----------
     |      columns : str or list[str]
     |          Field name(s) referencing existing column(s).
     |
     |      Raises
     |      ------
     |      KeyError
     |          If any of the passed column names do not exist.
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |          A tabular object without the column(s).
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
     |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table = pa.Table.from_pandas(df)
     |
     |      Drop one column:
     |
     |      >>> table.drop_columns("animals")
     |      pyarrow.Table
     |      n_legs: int64
     |      ----
     |      n_legs: [[2,4,5,100]]
     |
     |      Drop one or more columns:
     |
     |      >>> table.drop_columns(["n_legs", "animals"])
     |      pyarrow.Table
     |      ...
     |      ----
     |
     |  drop_null(self)
     |      _Tabular.drop_null(self)
     |
     |      Remove rows that contain missing values from a Table or RecordBatch.
     |
     |      See :func:`pyarrow.compute.drop_null` for full usage.
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |          A tabular object with the same schema, with rows containing
     |          no missing values.
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'year': [None, 2022, 2019, 2021],
     |      ...                   'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> table.drop_null()
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2022,2021]]
     |      n_legs: [[4,100]]
     |      animals: [["Horse","Centipede"]]
     |
     |  field(self, i)
     |      _Tabular.field(self, i)
     |
     |      Select a schema field by its column name or numeric index.
     |
     |      Parameters
     |      ----------
     |      i : int or string
     |          The index or name of the field to retrieve.
     |
     |      Returns
     |      -------
     |      Field
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table.field(0)
     |      pyarrow.Field<n_legs: int64>
     |      >>> table.field(1)
     |      pyarrow.Field<animals: string>
     |
     |  filter(self, mask, null_selection_behavior='drop')
     |      _Tabular.filter(self, mask, null_selection_behavior='drop')
     |
     |      Select rows from the table or record batch based on a boolean mask.
     |
     |      The Table can be filtered based on a mask, which will be passed to
     |      :func:`pyarrow.compute.filter` to perform the filtering, or it can
     |      be filtered through a boolean :class:`.Expression`
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like or .Expression
     |          The boolean mask or the :class:`.Expression` to filter the table with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled, does nothing if
     |          an :class:`.Expression` is used.
     |
     |      Returns
     |      -------
     |      filtered : Table or RecordBatch
     |          A tabular object of the same schema, with only the rows selected
     |          by applied filtering
     |
     |      Examples
     |      --------
     |      Using a Table (works similarly for RecordBatch):
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
     |      ...                   'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |
     |      Define an expression and select rows:
     |
     |      >>> import pyarrow.compute as pc
     |      >>> expr = pc.field("year") <= 2020
     |      >>> table.filter(expr)
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2020,2019]]
     |      n_legs: [[2,5]]
     |      animals: [["Flamingo","Brittle stars"]]
     |
     |      Define a mask and select rows:
     |
     |      >>> mask=[True, True, False, None]
     |      >>> table.filter(mask)
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2020,2022]]
     |      n_legs: [[2,4]]
     |      animals: [["Flamingo","Horse"]]
     |      >>> table.filter(mask, null_selection_behavior='emit_null')
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2020,2022,null]]
     |      n_legs: [[2,4,null]]
     |      animals: [["Flamingo","Horse",null]]
     |
     |  itercolumns(self)
     |      _Tabular.itercolumns(self)
     |
     |      Iterator over all columns in their numerical order.
     |
     |      Yields
     |      ------
     |      Array (for RecordBatch) or ChunkedArray (for Table)
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
     |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> for i in table.itercolumns():
     |      ...     print(i.null_count)
     |      ...
     |      2
     |      1
     |
     |  sort_by(self, sorting, **kwargs)
     |      _Tabular.sort_by(self, sorting, **kwargs)
     |
     |      Sort the Table or RecordBatch by one or multiple columns.
     |
     |      Parameters
     |      ----------
     |      sorting : str or list[tuple(name, order)]
     |          Name of the column to use to sort (ascending), or
     |          a list of multiple sorting conditions where
     |          each entry is a tuple with column name
     |          and sorting order ("ascending" or "descending")
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |          A new tabular object sorted according to the sort keys.
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],
     |      ...                   'n_legs': [2, 2, 4, 4, 5, 100],
     |      ...                   'animal': ["Flamingo", "Parrot", "Dog", "Horse",
     |      ...                   "Brittle stars", "Centipede"]})
     |      >>> table.sort_by('animal')
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animal: string
     |      ----
     |      year: [[2019,2021,2021,2020,2022,2022]]
     |      n_legs: [[5,100,4,2,4,2]]
     |      animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]]
     |
     |  take(self, indices)
     |      _Tabular.take(self, indices)
     |
     |      Select rows from a Table or RecordBatch.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the tabular object whose rows will be returned.
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |          A tabular object with the same schema, containing the taken rows.
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
     |      ...                   'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table.take([1,3])
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2022,2021]]
     |      n_legs: [[4,100]]
     |      animals: [["Horse","Centipede"]]
     |
     |  to_pydict(self, *, maps_as_pydicts=None)
     |      _Tabular.to_pydict(self, *, maps_as_pydicts=None)
     |
     |      Convert the Table or RecordBatch to a dict or OrderedDict.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      dict
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> table = pa.Table.from_arrays([n_legs, animals], names=["n_legs", "animals"])
     |      >>> table.to_pydict()
     |      {'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']}
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      _Tabular.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert the Table or RecordBatch to a list of rows / dictionaries.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      list
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> data = [[2, 4, 5, 100],
     |      ...         ["Flamingo", "Horse", "Brittle stars", "Centipede"]]
     |      >>> table = pa.table(data, names=["n_legs", "animals"])
     |      >>> table.to_pylist()
     |      [{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ...
     |
     |  to_string(self, *, show_metadata=False, preview_cols=0)
     |      _Tabular.to_string(self, *, show_metadata=False, preview_cols=0)
     |
     |      Return human-readable string representation of Table or RecordBatch.
     |
     |      Parameters
     |      ----------
     |      show_metadata : bool, default False
     |          Display Field-level and Schema-level KeyValueMetadata.
     |      preview_cols : int, default 0
     |          Display values of the columns for the first N columns.
     |
     |      Returns
     |      -------
     |      str
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from _Tabular:
     |
     |  from_pydict(mapping, schema=None, metadata=None)
     |      _Tabular.from_pydict(cls, mapping, schema=None, metadata=None)
     |
     |      Construct a Table or RecordBatch from Arrow arrays or columns.
     |
     |      Parameters
     |      ----------
     |      mapping : dict or Mapping
     |          A mapping of strings to Arrays or Python lists.
     |      schema : Schema, default None
     |          If not passed, will be inferred from the Mapping values.
     |      metadata : dict or Mapping, default None
     |          Optional metadata for the schema (if inferred).
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> pydict = {'n_legs': n_legs, 'animals': animals}
     |
     |      Construct a Table from a dictionary of arrays:
     |
     |      >>> pa.Table.from_pydict(pydict)
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |      >>> pa.Table.from_pydict(pydict).schema
     |      n_legs: int64
     |      animals: string
     |
     |      Construct a Table from a dictionary of arrays with metadata:
     |
     |      >>> my_metadata={"n_legs": "Number of legs per animal"}
     |      >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |      Construct a Table from a dictionary of arrays with pyarrow schema:
     |
     |      >>> my_schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |      >>> pa.Table.from_pydict(pydict, schema=my_schema).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |  from_pylist(mapping, schema=None, metadata=None)
     |      _Tabular.from_pylist(cls, mapping, schema=None, metadata=None)
     |
     |      Construct a Table or RecordBatch from list of rows / dictionaries.
     |
     |      Parameters
     |      ----------
     |      mapping : list of dicts of rows
     |          A mapping of strings to row values.
     |      schema : Schema, default None
     |          If not passed, will be inferred from the first row of the
     |          mapping values.
     |      metadata : dict or Mapping, default None
     |          Optional metadata for the schema (if inferred).
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'},
     |      ...           {'n_legs': 4, 'animals': 'Dog'}]
     |
     |      Construct a Table from a list of rows:
     |
     |      >>> pa.Table.from_pylist(pylist)
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,4]]
     |      animals: [["Flamingo","Dog"]]
     |
     |      Construct a Table from a list of rows with metadata:
     |
     |      >>> my_metadata={"n_legs": "Number of legs per animal"}
     |      >>> pa.Table.from_pylist(pylist, metadata=my_metadata).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |      Construct a Table from a list of rows with pyarrow schema:
     |
     |      >>> my_schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |      >>> pa.Table.from_pylist(pylist, schema=my_schema).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from _Tabular:
     |
     |  column_names
     |      Names of the Table or RecordBatch columns.
     |
     |      Returns
     |      -------
     |      list of str
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table.column_names
     |      ['n_legs', 'animals']
     |
     |  columns
     |      List of all columns in numerical order.
     |
     |      Returns
     |      -------
     |      columns : list of Array (for RecordBatch) or list of ChunkedArray (for Table)
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
     |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> table = pa.Table.from_pandas(df)
     |      >>> table.columns
     |      [<pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          null,
     |          4,
     |          5,
     |          null
     |        ]
     |      ], <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          "Flamingo",
     |          "Horse",
     |          null,
     |          "Centipede"
     |        ]
     |      ]]
     |
     |  shape
     |      Dimensions of the table or record batch: (#rows, #columns).
     |
     |      Returns
     |      -------
     |      (int, int)
     |          Number of rows and number of columns.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
     |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> table.shape
     |      (4, 2)
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from _Tabular:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class RecordBatchReader(_Weakrefable)
     |  RecordBatchReader()
     |
     |  Base class for reading stream of record batches.
     |
     |  Record batch readers function as iterators of record batches that also
     |  provide the schema (without the need to get any batches).
     |
     |  Warnings
     |  --------
     |  Do not call this class's constructor directly, use one of the
     |  ``RecordBatchReader.from_*`` functions instead.
     |
     |  Notes
     |  -----
     |  To import and export using the Arrow C stream interface, use the
     |  ``_import_from_c`` and ``_export_to_c`` methods. However, keep in mind this
     |  interface is intended for expert users.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> schema = pa.schema([('x', pa.int64())])
     |  >>> def iter_record_batches():
     |  ...     for i in range(2):
     |  ...         yield pa.RecordBatch.from_arrays([pa.array([1, 2, 3])], schema=schema)
     |  >>> reader = pa.RecordBatchReader.from_batches(schema, iter_record_batches())
     |  >>> print(reader.schema)
     |  x: int64
     |  >>> for batch in reader:
     |  ...     print(batch)
     |  pyarrow.RecordBatch
     |  x: int64
     |  ----
     |  x: [1,2,3]
     |  pyarrow.RecordBatch
     |  x: int64
     |  ----
     |  x: [1,2,3]
     |
     |  Method resolution order:
     |      RecordBatchReader
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_c_stream__(self, requested_schema=None)
     |      RecordBatchReader.__arrow_c_stream__(self, requested_schema=None)
     |
     |      Export to a C ArrowArrayStream PyCapsule.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule, default None
     |          The schema to which the stream should be casted, passed as a
     |          PyCapsule containing a C ArrowSchema representation of the
     |          requested schema.
     |
     |      Returns
     |      -------
     |      PyCapsule
     |          A capsule containing a C ArrowArrayStream struct.
     |
     |  __enter__(self)
     |      RecordBatchReader.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_val, exc_tb)
     |      RecordBatchReader.__exit__(self, exc_type, exc_val, exc_tb)
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __reduce__ = __reduce_cython__(...)
     |      RecordBatchReader.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      RecordBatchReader.__setstate_cython__(self, __pyx_state)
     |
     |  cast(self, target_schema)
     |      RecordBatchReader.cast(self, target_schema)
     |
     |      Wrap this reader with one that casts each batch lazily as it is pulled.
     |      Currently only a safe cast to target_schema is implemented.
     |
     |      Parameters
     |      ----------
     |      target_schema : Schema
     |          Schema to cast to, the names and order of fields must match.
     |
     |      Returns
     |      -------
     |      RecordBatchReader
     |
     |  close(self)
     |      RecordBatchReader.close(self)
     |
     |      Release any resources associated with the reader.
     |
     |  iter_batches_with_custom_metadata(self)
     |      RecordBatchReader.iter_batches_with_custom_metadata(self)
     |
     |      Iterate over record batches from the stream along with their custom
     |      metadata.
     |
     |      Yields
     |      ------
     |      RecordBatchWithMetadata
     |
     |  read_all(self)
     |      RecordBatchReader.read_all(self)
     |
     |      Read all record batches as a pyarrow.Table.
     |
     |      Returns
     |      -------
     |      Table
     |
     |  read_next_batch(self)
     |      RecordBatchReader.read_next_batch(self)
     |
     |      Read next RecordBatch from the stream.
     |
     |      Raises
     |      ------
     |      StopIteration:
     |          At end of stream.
     |
     |      Returns
     |      -------
     |      RecordBatch
     |
     |  read_next_batch_with_custom_metadata(self)
     |      RecordBatchReader.read_next_batch_with_custom_metadata(self)
     |
     |      Read next RecordBatch from the stream along with its custom metadata.
     |
     |      Raises
     |      ------
     |      StopIteration:
     |          At end of stream.
     |
     |      Returns
     |      -------
     |      batch : RecordBatch
     |      custom_metadata : KeyValueMetadata
     |
     |  read_pandas(self, **options)
     |      _ReadPandasMixin.read_pandas(self, **options)
     |
     |      Read contents of stream to a pandas.DataFrame.
     |
     |      Read all record batches as a pyarrow.Table then convert it to a
     |      pandas.DataFrame using Table.to_pandas.
     |
     |      Parameters
     |      ----------
     |      **options
     |          Arguments to forward to :meth:`Table.to_pandas`.
     |
     |      Returns
     |      -------
     |      df : pandas.DataFrame
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_batches(schema, batches)
     |      RecordBatchReader.from_batches(Schema schema, batches)
     |
     |      Create RecordBatchReader from an iterable of batches.
     |
     |      Parameters
     |      ----------
     |      schema : Schema
     |          The shared schema of the record batches
     |      batches : Iterable[RecordBatch]
     |          The batches that this reader will return.
     |
     |      Returns
     |      -------
     |      reader : RecordBatchReader
     |
     |  from_stream(data, schema=None)
     |      RecordBatchReader.from_stream(data, schema=None)
     |
     |      Create RecordBatchReader from a Arrow-compatible stream object.
     |
     |      This accepts objects implementing the Arrow PyCapsule Protocol for
     |      streams, i.e. objects that have a ``__arrow_c_stream__`` method.
     |
     |      Parameters
     |      ----------
     |      data : Arrow-compatible stream object
     |          Any object that implements the Arrow PyCapsule Protocol for
     |          streams.
     |      schema : Schema, default None
     |          The schema to which the stream should be casted, if supported
     |          by the stream object.
     |
     |      Returns
     |      -------
     |      RecordBatchReader
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  schema
     |      Shared schema of the record batches in the stream.
     |
     |      Returns
     |      -------
     |      Schema

    class RecordBatchWithMetadata(_frozen_importlib.RecordBatchWithMetadata)
     |  RecordBatchWithMetadata(batch, custom_metadata)
     |
     |  RecordBatch with its custom metadata
     |
     |  Parameters
     |  ----------
     |  batch : RecordBatch
     |  custom_metadata : KeyValueMetadata
     |
     |  Method resolution order:
     |      RecordBatchWithMetadata
     |      _frozen_importlib.RecordBatchWithMetadata
     |      builtins.tuple
     |      builtins.object
     |
     |  Methods inherited from _frozen_importlib.RecordBatchWithMetadata:
     |
     |  __getnewargs__(self) from collections.RecordBatchWithMetadata
     |      Return self as a plain tuple.  Used by copy and pickle.
     |
     |  __repr__(self) from collections.RecordBatchWithMetadata
     |      Return a nicely formatted representation string
     |
     |  _asdict(self) from collections.RecordBatchWithMetadata
     |      Return a new dict which maps field names to their values.
     |
     |  _replace(self, /, **kwds) from collections.RecordBatchWithMetadata
     |      Return a new RecordBatchWithMetadata object replacing specified fields with new values
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from _frozen_importlib.RecordBatchWithMetadata:
     |
     |  _make(iterable) from collections.RecordBatchWithMetadata
     |      Make a new RecordBatchWithMetadata object from a sequence or iterable
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from _frozen_importlib.RecordBatchWithMetadata:
     |
     |  __new__(_cls, batch, custom_metadata) from namedtuple_RecordBatchWithMetadata.RecordBatchWithMetadata
     |      Create new instance of RecordBatchWithMetadata(batch, custom_metadata)
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from _frozen_importlib.RecordBatchWithMetadata:
     |
     |  batch
     |      Alias for field number 0
     |
     |  custom_metadata
     |      Alias for field number 1
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from _frozen_importlib.RecordBatchWithMetadata:
     |
     |  __match_args__ = ('batch', 'custom_metadata')
     |
     |  _field_defaults = {}
     |
     |  _fields = ('batch', 'custom_metadata')
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.tuple:
     |
     |  __add__(self, value, /)
     |      Return self+value.
     |
     |  __contains__(self, key, /)
     |      Return bool(key in self).
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __getitem__(self, key, /)
     |      Return self[key].
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(self, value, /)
     |      Return self*value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  count(self, value, /)
     |      Return number of occurrences of value.
     |
     |  index(self, value, start=0, stop=9223372036854775807, /)
     |      Return first index of value.
     |
     |      Raises ValueError if the value is not present.
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from builtins.tuple:
     |
     |  __class_getitem__(...)
     |      See PEP 585

    class ResizableBuffer(Buffer)
     |  A base class for buffers that can be resized.
     |
     |  Method resolution order:
     |      ResizableBuffer
     |      Buffer
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  resize(self, new_size, shrink_to_fit=False)
     |      ResizableBuffer.resize(self, int64_t new_size, shrink_to_fit=False)
     |
     |      Resize buffer to indicated size.
     |
     |      Parameters
     |      ----------
     |      new_size : int
     |          New size of buffer (padding may be added internally).
     |      shrink_to_fit : bool, default False
     |          If this is true, the buffer is shrunk when new_size is less
     |          than the current size.
     |          If this is false, the buffer is never shrunk.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Buffer:
     |
     |  __buffer__(self, flags, /)
     |      Return a buffer object that exposes the underlying memory of the object.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(self, key, /)
     |      Return self[key].
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce_ex__(self, protocol)
     |      Buffer.__reduce_ex__(self, protocol)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  equals(self, other)
     |      Buffer.equals(self, Buffer other)
     |
     |      Determine if two buffers contain exactly the same data.
     |
     |      Parameters
     |      ----------
     |      other : Buffer
     |
     |      Returns
     |      -------
     |      are_equal : bool
     |          True if buffer contents and size are equal
     |
     |  hex(self)
     |      Buffer.hex(self)
     |
     |      Compute hexadecimal representation of the buffer.
     |
     |      Returns
     |      -------
     |      : bytes
     |
     |  slice(self, offset=0, length=None)
     |      Buffer.slice(self, offset=0, length=None)
     |
     |      Slice this buffer.  Memory is not copied.
     |
     |      You can also use the Python slice notation ``buffer[start:stop]``.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of buffer to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Buffer starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Buffer
     |          A logical view over this buffer.
     |
     |  to_pybytes(self)
     |      Buffer.to_pybytes(self)
     |
     |      Return this buffer as a Python bytes object. Memory is copied.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Buffer:
     |
     |  address
     |      The buffer's address, as an integer.
     |
     |      The returned address may point to CPU or device memory.
     |      Use `is_cpu()` to disambiguate.
     |
     |  device
     |      The device where the buffer resides.
     |
     |      Returns
     |      -------
     |      Device
     |
     |  device_type
     |      The device type where the buffer resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the buffer is CPU-accessible.
     |
     |  is_mutable
     |      Whether the buffer is mutable.
     |
     |  memory_manager
     |      The memory manager associated with the buffer.
     |
     |      Returns
     |      -------
     |      MemoryManager
     |
     |  parent
     |
     |  size
     |      The buffer size in bytes.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Buffer:
     |
     |  __hash__ = None

    class RunEndEncodedArray(Array)
     |  Concrete class for Arrow run-end encoded arrays.
     |
     |  Method resolution order:
     |      RunEndEncodedArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  find_physical_length(self)
     |      RunEndEncodedArray.find_physical_length(self)
     |
     |      Find the physical length of this REE array.
     |
     |      The physical length of an REE is the number of physical values (and
     |      run-ends) necessary to represent the logical range of values from offset
     |      to length.
     |
     |      This function uses binary-search, so it has a O(log N) cost.
     |
     |  find_physical_offset(self)
     |      RunEndEncodedArray.find_physical_offset(self)
     |
     |      Find the physical offset of this REE array.
     |
     |      This is the offset of the run that contains the value of the first
     |      logical element of this array considering its offset.
     |
     |      This function uses binary-search, so it has a O(log N) cost.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_arrays(run_ends, values, type=None)
     |      RunEndEncodedArray.from_arrays(run_ends, values, type=None)
     |
     |      Construct RunEndEncodedArray from run_ends and values arrays.
     |
     |      Parameters
     |      ----------
     |      run_ends : Array (int16, int32, or int64 type)
     |          The run_ends array.
     |      values : Array (any type)
     |          The values array.
     |      type : pyarrow.DataType, optional
     |          The run_end_encoded(run_end_type, value_type) array type.
     |
     |      Returns
     |      -------
     |      RunEndEncodedArray
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      RunEndEncodedArray.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct a RunEndEncodedArray from all the parameters that make up an
     |      Array.
     |
     |      RunEndEncodedArrays do not have buffers, only children arrays, but this
     |      implementation is needed to satisfy the Array interface.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The run_end_encoded(run_end_type, value_type) type.
     |      length : int
     |          The logical length of the run-end encoded array. Expected to match
     |          the last value of the run_ends array (children[0]) minus the offset.
     |      buffers : List[Buffer]
     |          Empty List or [None].
     |      null_count : int, default -1
     |          The number of null entries in the array. Run-end encoded arrays
     |          are specified to not have valid bits and null_count always equals 0.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array]
     |          Nested type children containing the run_ends and values arrays.
     |
     |      Returns
     |      -------
     |      RunEndEncodedArray
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  run_ends
     |      An array holding the logical indexes of each run-end.
     |
     |      The physical offset to the array is applied.
     |
     |  values
     |      An array holding the values of each run.
     |
     |      The physical offset to the array is applied.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class RunEndEncodedScalar(Scalar)
     |  Concrete class for RunEndEncoded scalars.
     |
     |  Method resolution order:
     |      RunEndEncodedScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      RunEndEncodedScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return underlying value as a Python object.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value
     |      Return underlying value as a scalar.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class RunEndEncodedType(DataType)
     |  Concrete class for run-end encoded types.
     |
     |  Method resolution order:
     |      RunEndEncodedType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      RunEndEncodedType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  run_end_type
     |
     |  value_type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class Scalar(_Weakrefable)
     |  Scalar()
     |
     |  The base class for scalars.
     |
     |  Notes
     |  -----
     |  This class supports Python's standard operators
     |  for element-wise operations, i.e. arithmetic (`+`, `-`, `/`, `%`, `**`),
     |  bitwise (`&`, `|`, `^`, `>>`, `<<`) and others.
     |  They can be used directly instead of calling underlying
     |  `pyarrow.compute` functions explicitly.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> pa.scalar(42) + pa.scalar(17)
     |  <pyarrow.Int64Scalar: 59>
     |
     |  >>> pa.scalar(6) ** 3
     |  <pyarrow.Int64Scalar: 216>
     |
     |  >>> arr = pa.array([1, 2, 3], type=pa.int8())
     |  >>> val = pa.scalar(42)
     |  >>> val - arr
     |  <pyarrow.lib.Int64Array object at ...>
     |  [
     |    41,
     |    40,
     |    39
     |  ]
     |
     |  Method resolution order:
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python representation.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class Schema(_Weakrefable)
     |  Schema()
     |
     |  A named collection of types a.k.a schema. A schema defines the
     |  column names and types in a record batch or table data structure.
     |  They also contain metadata about the columns. For example, schemas
     |  converted from Pandas contain metadata about their original Pandas
     |  types so they can be converted back to the same types.
     |
     |  Warnings
     |  --------
     |  Do not call this class's constructor directly. Instead use
     |  :func:`pyarrow.schema` factory function which makes a new Arrow
     |  Schema object.
     |
     |  Examples
     |  --------
     |  Create a new Arrow Schema object:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.schema([
     |  ...     ('some_int', pa.int32()),
     |  ...     ('some_string', pa.string())
     |  ... ])
     |  some_int: int32
     |  some_string: string
     |
     |  Create Arrow Schema with metadata:
     |
     |  >>> pa.schema([
     |  ...     pa.field('n_legs', pa.int64()),
     |  ...     pa.field('animals', pa.string())],
     |  ...     metadata={"n_legs": "Number of legs per animal"})
     |  n_legs: int64
     |  animals: string
     |  -- schema metadata --
     |  n_legs: 'Number of legs per animal'
     |
     |  Method resolution order:
     |      Schema
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_c_schema__(self)
     |      Schema.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(self, key, /)
     |      Return self[key].
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      Schema.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __sizeof__(self)
     |      Schema.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  add_metadata(self, metadata)
     |      Schema.add_metadata(self, metadata)
     |
     |      DEPRECATED
     |
     |      Parameters
     |      ----------
     |      metadata : dict
     |          Keys and values must be string-like / coercible to bytes
     |
     |  append(self, field)
     |      Schema.append(self, Field field)
     |
     |      Append a field at the end of the schema.
     |
     |      In contrast to Python's ``list.append()`` it does return a new
     |      object, leaving the original Schema unmodified.
     |
     |      Parameters
     |      ----------
     |      field : Field
     |
     |      Returns
     |      -------
     |      schema: Schema
     |          New object with appended field.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())])
     |
     |      Append a field 'extra' at the end of the schema:
     |
     |      >>> schema_new = schema.append(pa.field('extra', pa.bool_()))
     |      >>> schema_new
     |      n_legs: int64
     |      animals: string
     |      extra: bool
     |
     |      Original schema is unmodified:
     |
     |      >>> schema
     |      n_legs: int64
     |      animals: string
     |
     |  empty_table(self)
     |      Schema.empty_table(self)
     |
     |      Provide an empty table according to the schema.
     |
     |      Returns
     |      -------
     |      table: pyarrow.Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())])
     |
     |      Create an empty table with schema's fields:
     |
     |      >>> schema.empty_table()
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[]]
     |      animals: [[]]
     |
     |  equals(self, other, check_metadata=False)
     |      Schema.equals(self, Schema other, bool check_metadata=False)
     |
     |      Test if this schema is equal to the other
     |
     |      Parameters
     |      ----------
     |      other :  pyarrow.Schema
     |      check_metadata : bool, default False
     |          Key/value metadata must be equal too
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema1 = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |      >>> schema2 = pa.schema([
     |      ...     ('some_int', pa.int32()),
     |      ...     ('some_string', pa.string())
     |      ... ])
     |
     |      Test two equal schemas:
     |
     |      >>> schema1.equals(schema1)
     |      True
     |
     |      Test two unequal schemas:
     |
     |      >>> schema1.equals(schema2)
     |      False
     |
     |  field(self, i)
     |      Schema.field(self, i)
     |
     |      Select a field by its column name or numeric index.
     |
     |      Parameters
     |      ----------
     |      i : int or string
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())])
     |
     |      Select the second field:
     |
     |      >>> schema.field(1)
     |      pyarrow.Field<animals: string>
     |
     |      Select the field of the column named 'n_legs':
     |
     |      >>> schema.field('n_legs')
     |      pyarrow.Field<n_legs: int64>
     |
     |  field_by_name(self, name)
     |      Schema.field_by_name(self, name)
     |
     |      DEPRECATED
     |
     |      Parameters
     |      ----------
     |      name : str
     |
     |      Returns
     |      -------
     |      field: pyarrow.Field
     |
     |  get_all_field_indices(self, name)
     |      Schema.get_all_field_indices(self, name)
     |
     |      Return sorted list of indices for the fields with the given name.
     |
     |      Parameters
     |      ----------
     |      name : str
     |          The name of the field to look up.
     |
     |      Returns
     |      -------
     |      indices : List[int]
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string()),
     |      ...     pa.field('animals', pa.bool_())])
     |
     |      Get the indexes of the fields named 'animals':
     |
     |      >>> schema.get_all_field_indices("animals")
     |      [1, 2]
     |
     |  get_field_index(self, name)
     |      Schema.get_field_index(self, name)
     |
     |      Return index of the unique field with the given name.
     |
     |      Parameters
     |      ----------
     |      name : str
     |          The name of the field to look up.
     |
     |      Returns
     |      -------
     |      index : int
     |          The index of the field with the given name; -1 if the
     |          name isn't found or there are several fields with the given
     |          name.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())])
     |
     |      Get the index of the field named 'animals':
     |
     |      >>> schema.get_field_index("animals")
     |      1
     |
     |      Index in case of several fields with the given name:
     |
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string()),
     |      ...     pa.field('animals', pa.bool_())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |      >>> schema.get_field_index("animals")
     |      -1
     |
     |  insert(self, i, field)
     |      Schema.insert(self, int i, Field field)
     |
     |      Add a field at position i to the schema.
     |
     |      Parameters
     |      ----------
     |      i : int
     |      field : Field
     |
     |      Returns
     |      -------
     |      schema: Schema
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())])
     |
     |      Insert a new field on the second position:
     |
     |      >>> schema.insert(1, pa.field('extra', pa.bool_()))
     |      n_legs: int64
     |      extra: bool
     |      animals: string
     |
     |  remove(self, i)
     |      Schema.remove(self, int i)
     |
     |      Remove the field at index i from the schema.
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      schema: Schema
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())])
     |
     |      Remove the second field of the schema:
     |
     |      >>> schema.remove(1)
     |      n_legs: int64
     |
     |  remove_metadata(self)
     |      Schema.remove_metadata(self)
     |
     |      Create new schema without metadata, if any
     |
     |      Returns
     |      -------
     |      schema : pyarrow.Schema
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |      >>> schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |      Create a new schema with removing the metadata from the original:
     |
     |      >>> schema.remove_metadata()
     |      n_legs: int64
     |      animals: string
     |
     |  serialize(self, memory_pool=None)
     |      Schema.serialize(self, memory_pool=None)
     |
     |      Write Schema to Buffer as encapsulated IPC message
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Uses default memory pool if not specified
     |
     |      Returns
     |      -------
     |      serialized : Buffer
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())])
     |
     |      Write schema to Buffer:
     |
     |      >>> schema.serialize()
     |      <pyarrow.Buffer address=0x... size=... is_cpu=True is_mutable=True>
     |
     |  set(self, i, field)
     |      Schema.set(self, int i, Field field)
     |
     |      Replace a field at position i in the schema.
     |
     |      Parameters
     |      ----------
     |      i : int
     |      field : Field
     |
     |      Returns
     |      -------
     |      schema: Schema
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())])
     |
     |      Replace the second field of the schema with a new field 'extra':
     |
     |      >>> schema.set(1, pa.field('replaced', pa.bool_()))
     |      n_legs: int64
     |      replaced: bool
     |
     |  to_string(self, truncate_metadata=True, show_field_metadata=True, show_schema_metadata=True, element_size_limit=100)
     |      Schema.to_string(self, truncate_metadata=True, show_field_metadata=True, show_schema_metadata=True, element_size_limit=100)
     |
     |      Return human-readable representation of Schema
     |
     |      Parameters
     |      ----------
     |      truncate_metadata : boolean, default True
     |          Limit metadata key/value display to a single line of ~80 characters
     |          or less
     |      show_field_metadata : boolean, default True
     |          Display Field-level KeyValueMetadata
     |      show_schema_metadata : boolean, default True
     |          Display Schema-level KeyValueMetadata
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |      Returns
     |      -------
     |      str : the formatted output
     |
     |  with_metadata(self, metadata)
     |      Schema.with_metadata(self, metadata)
     |
     |      Add metadata as dict of string keys and values to Schema
     |
     |      Parameters
     |      ----------
     |      metadata : dict
     |          Keys and values must be string-like / coercible to bytes
     |
     |      Returns
     |      -------
     |      schema : pyarrow.Schema
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())])
     |
     |      Add metadata to existing schema field:
     |
     |      >>> schema.with_metadata({"n_legs": "Number of legs per animal"})
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |  ----------------------------------------------------------------------
     |  Class methods defined here:
     |
     |  from_pandas(df, preserve_index=None)
     |      Schema.from_pandas(cls, df, preserve_index=None)
     |
     |      Returns implied schema from DataFrame
     |
     |      Parameters
     |      ----------
     |      df : pandas.DataFrame
     |
     |      preserve_index : bool, optional
     |          Whether to store the index as an additional field in the resulting
     |          ``Schema``. The default of None will store the index as a field,
     |          except for RangeIndex which is stored as metadata only. Use
     |          ``preserve_index=True`` to force it to be stored as a field.
     |
     |      Returns
     |      -------
     |      pyarrow.Schema
     |
     |      Examples
     |      --------
     |      >>> import pandas as pd
     |      >>> import pyarrow as pa
     |      >>> df = pd.DataFrame({
     |      ...     'int': [1, 2],
     |      ...     'str': ['a', 'b']
     |      ... })
     |
     |      Create an Arrow Schema from the schema of a pandas DataFrame:
     |
     |      >>> pa.Schema.from_pandas(df)
     |      int: int64
     |      str: large_string
     |      -- schema metadata --
     |      pandas: '{"index_columns": [{"kind": "range", "name": null, ...
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  metadata
     |      The schema's metadata (if any is set).
     |
     |      Returns
     |      -------
     |      metadata: dict or None
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |
     |      Get the metadata of the schema's fields:
     |
     |      >>> schema.metadata
     |      {b'n_legs': b'Number of legs per animal'}
     |
     |  names
     |      The schema's field names.
     |
     |      Returns
     |      -------
     |      list of str
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())])
     |
     |      Get the names of the schema's fields:
     |
     |      >>> schema.names
     |      ['n_legs', 'animals']
     |
     |  pandas_metadata
     |      Return deserialized-from-JSON pandas metadata field (if it exists)
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
     |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> schema = pa.Table.from_pandas(df).schema
     |
     |      Select pandas metadata field from Arrow Schema:
     |
     |      >>> schema.pandas_metadata
     |      {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, 'stop': 4, 'step': 1}], ...
     |
     |  types
     |      The schema's field types.
     |
     |      Returns
     |      -------
     |      list of DataType
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())])
     |
     |      Get the types of the schema's fields:
     |
     |      >>> schema.types
     |      [DataType(int64), DataType(string)]
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class SignalStopHandler(builtins.object)
     |  Methods defined here:
     |
     |  __enter__(self)
     |      SignalStopHandler.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, exc_tb)
     |      SignalStopHandler.__exit__(self, exc_type, exc_value, exc_tb)
     |
     |  __reduce__ = __reduce_cython__(...)
     |      SignalStopHandler.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      SignalStopHandler.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  stop_token

    class SparseCOOTensor(_Weakrefable)
     |  SparseCOOTensor()
     |
     |  A sparse COO (COOrdinate) tensor.
     |
     |  COO format stores a sparse tensor as a collection of (indices, values)
     |  pairs. The indices specify the coordinates of non-zero elements, and
     |  the values contain the actual data at those coordinates.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> import numpy as np
     |  >>> dense_tensor = np.array([[0, 1, 0], [2, 0, 3]], dtype=np.float32)
     |  >>> sparse_coo = pa.SparseCOOTensor.from_dense_numpy(dense_tensor)
     |  >>> sparse_coo
     |  <pyarrow.SparseCOOTensor>
     |  type: float
     |  shape: (2, 3)
     |
     |  Method resolution order:
     |      SparseCOOTensor
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      SparseCOOTensor.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      SparseCOOTensor.__setstate_cython__(self, __pyx_state)
     |
     |  dim_name(self, i)
     |      SparseCOOTensor.dim_name(self, i)
     |
     |      Returns the name of the i-th tensor dimension.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          The physical index of the tensor dimension.
     |
     |      Returns
     |      -------
     |      str
     |
     |  equals(self, other)
     |      SparseCOOTensor.equals(self, SparseCOOTensor other)
     |
     |      Return true if sparse tensors contains exactly equal data.
     |
     |      Parameters
     |      ----------
     |      other : SparseCOOTensor
     |          The other tensor to compare for equality.
     |
     |  to_numpy(self)
     |      SparseCOOTensor.to_numpy(self)
     |
     |      Convert arrow::SparseCOOTensor to numpy.ndarrays with zero copy.
     |
     |  to_pydata_sparse(self)
     |      SparseCOOTensor.to_pydata_sparse(self)
     |
     |      Convert arrow::SparseCOOTensor to pydata/sparse.COO.
     |
     |  to_scipy(self)
     |      SparseCOOTensor.to_scipy(self)
     |
     |      Convert arrow::SparseCOOTensor to scipy.sparse.coo_array.
     |
     |  to_tensor(self)
     |      SparseCOOTensor.to_tensor(self)
     |
     |      Convert arrow::SparseCOOTensor to arrow::Tensor.
     |
     |  ----------------------------------------------------------------------
     |  Class methods defined here:
     |
     |  from_dense_numpy(obj, dim_names=None)
     |      SparseCOOTensor.from_dense_numpy(cls, obj, dim_names=None)
     |
     |      Convert numpy.ndarray to arrow::SparseCOOTensor
     |
     |      Parameters
     |      ----------
     |      obj : numpy.ndarray
     |          Data used to populate the rows.
     |      dim_names : list[str], optional
     |          Names of the dimensions.
     |
     |      Returns
     |      -------
     |      pyarrow.SparseCOOTensor
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_numpy(data, coords, shape, dim_names=None)
     |      SparseCOOTensor.from_numpy(data, coords, shape, dim_names=None)
     |
     |      Create arrow::SparseCOOTensor from numpy.ndarrays
     |
     |      Parameters
     |      ----------
     |      data : numpy.ndarray
     |          Data used to populate the rows.
     |      coords : numpy.ndarray
     |          Coordinates of the data.
     |      shape : tuple
     |          Shape of the tensor.
     |      dim_names : list, optional
     |          Names of the dimensions.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> data = np.array([1.0, 2.0, 3.0], dtype=np.float32)
     |      >>> coords = np.array([[0, 1], [1, 0], [1, 2]], dtype=np.int64)
     |      >>> sparse_coo = pa.SparseCOOTensor.from_numpy(data, coords, shape=(2, 3))
     |      >>> sparse_coo
     |      <pyarrow.SparseCOOTensor>
     |      type: float
     |      shape: (2, 3)
     |
     |  from_pydata_sparse(obj, dim_names=None)
     |      SparseCOOTensor.from_pydata_sparse(obj, dim_names=None)
     |
     |      Convert pydata/sparse.COO to arrow::SparseCOOTensor.
     |
     |      Parameters
     |      ----------
     |      obj : pydata.sparse.COO
     |          The sparse multidimensional array that should be converted.
     |      dim_names : list, optional
     |          Names of the dimensions.
     |
     |  from_scipy(obj, dim_names=None)
     |      SparseCOOTensor.from_scipy(obj, dim_names=None)
     |
     |      Convert scipy.sparse.coo_array or scipy.sparse.coo_matrix to arrow::SparseCOOTensor
     |
     |      Parameters
     |      ----------
     |      obj : scipy.sparse.coo_array or scipy.sparse.coo_matrix
     |          The scipy array or matrix that should be converted.
     |      dim_names : list, optional
     |          Names of the dimensions.
     |
     |  from_tensor(obj)
     |      SparseCOOTensor.from_tensor(obj)
     |
     |      Convert arrow::Tensor to arrow::SparseCOOTensor.
     |
     |      Parameters
     |      ----------
     |      obj : Tensor
     |          The tensor that should be converted.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  dim_names
     |
     |  has_canonical_format
     |
     |  is_mutable
     |
     |  ndim
     |
     |  non_zero_length
     |
     |  shape
     |
     |  size
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class SparseCSCMatrix(_Weakrefable)
     |  SparseCSCMatrix()
     |
     |  A sparse CSC (Compressed Sparse Column) matrix.
     |
     |  CSC format stores a sparse matrix by compressing the column information.
     |  It uses three arrays: data (non-zero values), indices (row indices),
     |  and indptr (column pointers that indicate where each column starts
     |  in the data array). CSC is the transpose of CSR format.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> import numpy as np
     |  >>> dense_matrix = np.array([[1, 0, 2], [0, 0, 3]], dtype=np.float64)
     |  >>> sparse_csc = pa.SparseCSCMatrix.from_dense_numpy(dense_matrix)
     |  >>> sparse_csc
     |  <pyarrow.SparseCSCMatrix>
     |  type: double
     |  shape: (2, 3)
     |
     |  Method resolution order:
     |      SparseCSCMatrix
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      SparseCSCMatrix.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      SparseCSCMatrix.__setstate_cython__(self, __pyx_state)
     |
     |  dim_name(self, i)
     |      SparseCSCMatrix.dim_name(self, i)
     |
     |      Returns the name of the i-th tensor dimension.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          The physical index of the tensor dimension.
     |
     |      Returns
     |      -------
     |      str
     |
     |  equals(self, other)
     |      SparseCSCMatrix.equals(self, SparseCSCMatrix other)
     |
     |      Return true if sparse tensors contains exactly equal data
     |
     |      Parameters
     |      ----------
     |      other : SparseCSCMatrix
     |          The other tensor to compare for equality.
     |
     |  to_numpy(self)
     |      SparseCSCMatrix.to_numpy(self)
     |
     |      Convert arrow::SparseCSCMatrix to numpy.ndarrays with zero copy
     |
     |  to_scipy(self)
     |      SparseCSCMatrix.to_scipy(self)
     |
     |      Convert arrow::SparseCSCMatrix to scipy.sparse.csc_array
     |
     |  to_tensor(self)
     |      SparseCSCMatrix.to_tensor(self)
     |
     |      Convert arrow::SparseCSCMatrix to arrow::Tensor
     |
     |  ----------------------------------------------------------------------
     |  Class methods defined here:
     |
     |  from_dense_numpy(obj, dim_names=None)
     |      SparseCSCMatrix.from_dense_numpy(cls, obj, dim_names=None)
     |
     |      Convert numpy.ndarray to arrow::SparseCSCMatrix
     |
     |      Parameters
     |      ----------
     |      obj : numpy.ndarray
     |          Data used to populate the rows.
     |      dim_names : list[str], optional
     |          Names of the dimensions.
     |
     |      Returns
     |      -------
     |      pyarrow.SparseCSCMatrix
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_numpy(data, indptr, indices, shape, dim_names=None)
     |      SparseCSCMatrix.from_numpy(data, indptr, indices, shape, dim_names=None)
     |
     |      Create arrow::SparseCSCMatrix from numpy.ndarrays
     |
     |      Parameters
     |      ----------
     |      data : numpy.ndarray
     |          Data used to populate the sparse matrix.
     |      indptr : numpy.ndarray
     |          Range of the rows,
     |          The i-th row spans from `indptr[i]` to `indptr[i+1]` in the data.
     |      indices : numpy.ndarray
     |          Column indices of the corresponding non-zero values.
     |      shape : tuple
     |          Shape of the matrix.
     |      dim_names : list, optional
     |          Names of the dimensions.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> data = np.array([1.0, 3.0, 2.0], dtype=np.float64)
     |      >>> indptr = np.array([0, 1, 2, 3], dtype=np.int64)
     |      >>> indices = np.array([0, 1, 0], dtype=np.int64)
     |      >>> sparse_csc = pa.SparseCSCMatrix.from_numpy(
     |      ...     data, indptr, indices, shape=(2, 3))
     |      >>> sparse_csc
     |      <pyarrow.SparseCSCMatrix>
     |      type: double
     |      shape: (2, 3)
     |
     |  from_scipy(obj, dim_names=None)
     |      SparseCSCMatrix.from_scipy(obj, dim_names=None)
     |
     |      Convert scipy.sparse.csc_array or scipy.sparse.csc_matrix to arrow::SparseCSCMatrix
     |
     |      Parameters
     |      ----------
     |      obj : scipy.sparse.csc_array or scipy.sparse.csc_matrix
     |          The scipy matrix that should be converted.
     |      dim_names : list, optional
     |          Names of the dimensions.
     |
     |  from_tensor(obj)
     |      SparseCSCMatrix.from_tensor(obj)
     |
     |      Convert arrow::Tensor to arrow::SparseCSCMatrix
     |
     |      Parameters
     |      ----------
     |      obj : Tensor
     |          The dense tensor that should be converted.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  dim_names
     |
     |  is_mutable
     |
     |  ndim
     |
     |  non_zero_length
     |
     |  shape
     |
     |  size
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class SparseCSFTensor(_Weakrefable)
     |  SparseCSFTensor()
     |
     |  A sparse CSF tensor.
     |
     |  CSF is a generalization of compressed sparse row (CSR) index.
     |
     |  CSF index recursively compresses each dimension of a tensor into a set
     |  of prefix trees. Each path from a root to leaf forms one tensor
     |  non-zero index. CSF is implemented with two arrays of buffers and one
     |  arrays of integers.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> import numpy as np
     |  >>> # Create a 3D sparse tensor
     |  >>> dense_tensor = np.zeros((2, 3, 2), dtype=np.float32)
     |  >>> dense_tensor[0, 1, 0] = 1.0
     |  >>> dense_tensor[1, 2, 1] = 2.0
     |  >>> sparse_csf = pa.SparseCSFTensor.from_dense_numpy(dense_tensor)
     |  >>> sparse_csf
     |  <pyarrow.SparseCSFTensor>
     |  type: float
     |  shape: (2, 3, 2)
     |
     |  Method resolution order:
     |      SparseCSFTensor
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      SparseCSFTensor.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      SparseCSFTensor.__setstate_cython__(self, __pyx_state)
     |
     |  dim_name(self, i)
     |      SparseCSFTensor.dim_name(self, i)
     |
     |      Returns the name of the i-th tensor dimension.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          The physical index of the tensor dimension.
     |
     |      Returns
     |      -------
     |      str
     |
     |  equals(self, other)
     |      SparseCSFTensor.equals(self, SparseCSFTensor other)
     |
     |      Return true if sparse tensors contains exactly equal data
     |
     |      Parameters
     |      ----------
     |      other : SparseCSFTensor
     |          The other tensor to compare for equality.
     |
     |  to_numpy(self)
     |      SparseCSFTensor.to_numpy(self)
     |
     |      Convert arrow::SparseCSFTensor to numpy.ndarrays with zero copy
     |
     |  to_tensor(self)
     |      SparseCSFTensor.to_tensor(self)
     |
     |      Convert arrow::SparseCSFTensor to arrow::Tensor
     |
     |  ----------------------------------------------------------------------
     |  Class methods defined here:
     |
     |  from_dense_numpy(obj, dim_names=None)
     |      SparseCSFTensor.from_dense_numpy(cls, obj, dim_names=None)
     |
     |      Convert numpy.ndarray to arrow::SparseCSFTensor
     |
     |      Parameters
     |      ----------
     |      obj : numpy.ndarray
     |          Data used to populate the rows.
     |      dim_names : list[str], optional
     |          Names of the dimensions.
     |
     |      Returns
     |      -------
     |      pyarrow.SparseCSFTensor
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_numpy(data, indptr, indices, shape, axis_order=None, dim_names=None)
     |      SparseCSFTensor.from_numpy(data, indptr, indices, shape, axis_order=None, dim_names=None)
     |
     |      Create arrow::SparseCSFTensor from numpy.ndarrays
     |
     |      Parameters
     |      ----------
     |      data : numpy.ndarray
     |          Data used to populate the sparse tensor.
     |      indptr : numpy.ndarray
     |          The sparsity structure.
     |          Each two consecutive dimensions in a tensor correspond to
     |          a buffer in indices.
     |          A pair of consecutive values at `indptr[dim][i]`
     |          `indptr[dim][i + 1]` signify a range of nodes in
     |          `indices[dim + 1]` who are children of `indices[dim][i]` node.
     |      indices : numpy.ndarray
     |          Stores values of nodes.
     |          Each tensor dimension corresponds to a buffer in indptr.
     |      shape : tuple
     |          Shape of the matrix.
     |      axis_order : list, optional
     |          the sequence in which dimensions were traversed to
     |          produce the prefix tree.
     |      dim_names : list, optional
     |          Names of the dimensions.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> data = np.array([1.0, 2.0], dtype=np.float32)
     |      >>> indptr = [
     |      ...     np.array([0, 1, 2], dtype=np.int64),
     |      ...     np.array([0, 1, 2], dtype=np.int64),
     |      ... ]
     |      >>> indices = [
     |      ...     np.array([0, 1], dtype=np.int64),
     |      ...     np.array([1, 2], dtype=np.int64),
     |      ...     np.array([0, 1], dtype=np.int64),
     |      ... ]
     |      >>> sparse_csf = pa.SparseCSFTensor.from_numpy(
     |      ...     data, indptr, indices, shape=(2, 3, 2))
     |      >>> sparse_csf
     |      <pyarrow.SparseCSFTensor>
     |      type: float
     |      shape: (2, 3, 2)
     |
     |  from_tensor(obj)
     |      SparseCSFTensor.from_tensor(obj)
     |
     |      Convert arrow::Tensor to arrow::SparseCSFTensor
     |
     |      Parameters
     |      ----------
     |      obj : Tensor
     |          The dense tensor that should be converted.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  dim_names
     |
     |  is_mutable
     |
     |  ndim
     |
     |  non_zero_length
     |
     |  shape
     |
     |  size
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class SparseCSRMatrix(_Weakrefable)
     |  SparseCSRMatrix()
     |
     |  A sparse CSR (Compressed Sparse Row) matrix.
     |
     |  CSR format stores a sparse matrix by compressing the row information.
     |  It uses three arrays: data (non-zero values), indices (column indices),
     |  and indptr (row pointers that indicate where each row starts in the
     |  data array).
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> import numpy as np
     |  >>> dense_matrix = np.array([[1, 0, 2], [0, 0, 3]], dtype=np.float64)
     |  >>> sparse_csr = pa.SparseCSRMatrix.from_dense_numpy(dense_matrix)
     |  >>> sparse_csr
     |  <pyarrow.SparseCSRMatrix>
     |  type: double
     |  shape: (2, 3)
     |
     |  Method resolution order:
     |      SparseCSRMatrix
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      SparseCSRMatrix.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      SparseCSRMatrix.__setstate_cython__(self, __pyx_state)
     |
     |  dim_name(self, i)
     |      SparseCSRMatrix.dim_name(self, i)
     |
     |      Returns the name of the i-th tensor dimension.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          The physical index of the tensor dimension.
     |
     |      Returns
     |      -------
     |      str
     |
     |  equals(self, other)
     |      SparseCSRMatrix.equals(self, SparseCSRMatrix other)
     |
     |      Return true if sparse tensors contains exactly equal data.
     |
     |      Parameters
     |      ----------
     |      other : SparseCSRMatrix
     |          The other tensor to compare for equality.
     |
     |  to_numpy(self)
     |      SparseCSRMatrix.to_numpy(self)
     |
     |      Convert arrow::SparseCSRMatrix to numpy.ndarrays with zero copy.
     |
     |  to_scipy(self)
     |      SparseCSRMatrix.to_scipy(self)
     |
     |      Convert arrow::SparseCSRMatrix to scipy.sparse.csr_array.
     |
     |  to_tensor(self)
     |      SparseCSRMatrix.to_tensor(self)
     |
     |      Convert arrow::SparseCSRMatrix to arrow::Tensor.
     |
     |  ----------------------------------------------------------------------
     |  Class methods defined here:
     |
     |  from_dense_numpy(obj, dim_names=None)
     |      SparseCSRMatrix.from_dense_numpy(cls, obj, dim_names=None)
     |
     |      Convert numpy.ndarray to arrow::SparseCSRMatrix
     |
     |      Parameters
     |      ----------
     |      obj : numpy.ndarray
     |          The dense numpy array that should be converted.
     |      dim_names : list, optional
     |          The names of the dimensions.
     |
     |      Returns
     |      -------
     |      pyarrow.SparseCSRMatrix
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_numpy(data, indptr, indices, shape, dim_names=None)
     |      SparseCSRMatrix.from_numpy(data, indptr, indices, shape, dim_names=None)
     |
     |      Create arrow::SparseCSRMatrix from numpy.ndarrays.
     |
     |      Parameters
     |      ----------
     |      data : numpy.ndarray
     |          Data used to populate the sparse matrix.
     |      indptr : numpy.ndarray
     |          Range of the rows,
     |          The i-th row spans from `indptr[i]` to `indptr[i+1]` in the data.
     |      indices : numpy.ndarray
     |          Column indices of the corresponding non-zero values.
     |      shape : tuple
     |          Shape of the matrix.
     |      dim_names : list, optional
     |          Names of the dimensions.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> data = np.array([1.0, 2.0, 3.0], dtype=np.float64)
     |      >>> indptr = np.array([0, 2, 3], dtype=np.int64)
     |      >>> indices = np.array([0, 2, 1], dtype=np.int64)
     |      >>> sparse_csr = pa.SparseCSRMatrix.from_numpy(
     |      ...     data, indptr, indices, shape=(2, 3))
     |      >>> sparse_csr
     |      <pyarrow.SparseCSRMatrix>
     |      type: double
     |      shape: (2, 3)
     |
     |  from_scipy(obj, dim_names=None)
     |      SparseCSRMatrix.from_scipy(obj, dim_names=None)
     |
     |      Convert scipy.sparse.csr_array or scipy.sparse.csr_matrix to arrow::SparseCSRMatrix.
     |
     |      Parameters
     |      ----------
     |      obj : scipy.sparse.csr_array or scipy.sparse.csr_matrix
     |          The scipy matrix that should be converted.
     |      dim_names : list, optional
     |          Names of the dimensions.
     |
     |  from_tensor(obj)
     |      SparseCSRMatrix.from_tensor(obj)
     |
     |      Convert arrow::Tensor to arrow::SparseCSRMatrix.
     |
     |      Parameters
     |      ----------
     |      obj : Tensor
     |          The dense tensor that should be converted.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  dim_names
     |
     |  is_mutable
     |
     |  ndim
     |
     |  non_zero_length
     |
     |  shape
     |
     |  size
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class SparseUnionType(UnionType)
     |  Concrete class for sparse union types.
     |
     |  Examples
     |  --------
     |  Create an instance of a sparse UnionType using ``pa.union``:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())],
     |  ...          mode=pa.lib.UnionMode_SPARSE),
     |  (SparseUnionType(sparse_union<a: fixed_size_binary[10]=0, b: string=1>),)
     |
     |  Create an instance of a sparse UnionType using ``pa.sparse_union``:
     |
     |  >>> pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |  SparseUnionType(sparse_union<a: fixed_size_binary[10]=0, b: string=1>)
     |
     |  Method resolution order:
     |      SparseUnionType
     |      UnionType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from UnionType:
     |
     |  __getitem__(...)
     |      UnionType.__getitem__(self, i)
     |
     |      Return a child field by its index.
     |
     |      Alias of ``field``.
     |
     |  __iter__(...)
     |      UnionType.__iter__(self)
     |
     |      Iterate over union members, in order.
     |
     |  __len__(...)
     |      UnionType.__len__(self)
     |
     |      Like num_fields().
     |
     |  __reduce__(self)
     |      UnionType.__reduce__(self)
     |
     |  field(self, i)
     |      UnionType.field(self, i) -> Field
     |
     |      Return a child field by its numeric index.
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |      >>> union[0]
     |      pyarrow.Field<a: fixed_size_binary[10]>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from UnionType:
     |
     |  mode
     |      The mode of the union ("dense" or "sparse").
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |      >>> union.mode
     |      'sparse'
     |
     |  type_codes
     |      The type code to indicate each data type in this union.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |      >>> union.type_codes
     |      [0, 1]
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class StopToken(builtins.object)
     |  Methods defined here:
     |
     |  __reduce__ = __reduce_cython__(...)
     |      StopToken.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      StopToken.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class StringArray(Array)
     |  Concrete class for Arrow arrays of string (or utf8) data type.
     |
     |  Method resolution order:
     |      StringArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_buffers(length, value_offsets, data, null_bitmap=None, null_count=-1, offset=0)
     |      StringArray.from_buffers(int length, Buffer value_offsets, Buffer data, Buffer null_bitmap=None, int null_count=-1, int offset=0)
     |
     |      Construct a StringArray from value_offsets and data buffers.
     |      If there are nulls in the data, also a null_bitmap and the matching
     |      null_count must be passed.
     |
     |      Parameters
     |      ----------
     |      length : int
     |      value_offsets : Buffer
     |      data : Buffer
     |      null_bitmap : Buffer, optional
     |      null_count : int, default 0
     |      offset : int, default 0
     |
     |      Returns
     |      -------
     |      string_array : StringArray
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class StringBuilder(_Weakrefable)
     |  Builder class for UTF8 strings.
     |
     |  This class exposes facilities for incrementally adding string values and
     |  building the null bitmap for a pyarrow.Array (type='string').
     |
     |  Method resolution order:
     |      StringBuilder
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __reduce__ = __reduce_cython__(...)
     |      StringBuilder.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      StringBuilder.__setstate_cython__(self, __pyx_state)
     |
     |  append(self, value)
     |      StringBuilder.append(self, value)
     |
     |      Append a single value to the builder.
     |
     |      The value can either be a string/bytes object or a null value
     |      (np.nan or None).
     |
     |      Parameters
     |      ----------
     |      value : string/bytes or np.nan/None
     |          The value to append to the string array builder.
     |
     |  append_values(self, values)
     |      StringBuilder.append_values(self, values)
     |
     |      Append all the values from an iterable.
     |
     |      Parameters
     |      ----------
     |      values : iterable of string/bytes or np.nan/None values
     |          The values to append to the string array builder.
     |
     |  finish(self)
     |      StringBuilder.finish(self)
     |
     |      Return result of builder as an Array object; also resets the builder.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  null_count

    class StringScalar(BinaryScalar)
     |  Concrete class for string-like (utf8) scalars.
     |
     |  Method resolution order:
     |      StringScalar
     |      BinaryScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      StringScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python string.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BinaryScalar:
     |
     |  __buffer__(self, flags, /)
     |      Return a buffer object that exposes the underlying memory of the object.
     |
     |  __bytes__(self)
     |      BinaryScalar.__bytes__(self)
     |
     |  as_buffer(self)
     |      BinaryScalar.as_buffer(self)
     |
     |      Return a view over this value as a Buffer object.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class StringViewArray(Array)
     |  Concrete class for Arrow arrays of string (or utf8) view data type.
     |
     |  Method resolution order:
     |      StringViewArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class StringViewBuilder(_Weakrefable)
     |  Builder class for UTF8 string views.
     |
     |  This class exposes facilities for incrementally adding string values and
     |  building the null bitmap for a pyarrow.Array (type='string_view').
     |
     |  Method resolution order:
     |      StringViewBuilder
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __reduce__ = __reduce_cython__(...)
     |      StringViewBuilder.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      StringViewBuilder.__setstate_cython__(self, __pyx_state)
     |
     |  append(self, value)
     |      StringViewBuilder.append(self, value)
     |
     |      Append a single value to the builder.
     |
     |      The value can either be a string/bytes object or a null value
     |      (np.nan or None).
     |
     |      Parameters
     |      ----------
     |      value : string/bytes or np.nan/None
     |          The value to append to the string array builder.
     |
     |  append_values(self, values)
     |      StringViewBuilder.append_values(self, values)
     |
     |      Append all the values from an iterable.
     |
     |      Parameters
     |      ----------
     |      values : iterable of string/bytes or np.nan/None values
     |          The values to append to the string array builder.
     |
     |  finish(self)
     |      StringViewBuilder.finish(self)
     |
     |      Return result of builder as an Array object; also resets the builder.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  null_count

    class StringViewScalar(StringScalar)
     |  Method resolution order:
     |      StringViewScalar
     |      StringScalar
     |      BinaryScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from StringScalar:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      StringScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python string.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BinaryScalar:
     |
     |  __buffer__(self, flags, /)
     |      Return a buffer object that exposes the underlying memory of the object.
     |
     |  __bytes__(self)
     |      BinaryScalar.__bytes__(self)
     |
     |  as_buffer(self)
     |      BinaryScalar.as_buffer(self)
     |
     |      Return a view over this value as a Buffer object.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class StructArray(Array)
     |  Concrete class for Arrow arrays of a struct data type.
     |
     |  Method resolution order:
     |      StructArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  field(self, index)
     |      StructArray.field(self, index)
     |
     |      Retrieves the child array belonging to field.
     |
     |      Parameters
     |      ----------
     |      index : Union[int, str]
     |          Index / position or name of the field.
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  flatten(self, memory_pool=None)
     |      StructArray.flatten(self, MemoryPool memory_pool=None)
     |
     |      Return one individual array for each field in the struct.
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          For memory allocations, if required, otherwise use default pool.
     |
     |      Returns
     |      -------
     |      result : List[Array]
     |
     |  sort(self, order='ascending', by=None, **kwargs)
     |      StructArray.sort(self, order='ascending', by=None, **kwargs)
     |
     |      Sort the StructArray
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      by : str or None, default None
     |          If to sort the array by one of its fields
     |          or by the whole array.
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : StructArray
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_arrays(arrays, names=None, fields=None, mask=None, memory_pool=None, type=None)
     |      StructArray.from_arrays(arrays, names=None, fields=None, mask=None, memory_pool=None, type=None)
     |
     |      Construct StructArray from collection of arrays representing
     |      each field in the struct.
     |
     |      Either field names, field instances or a struct type must be passed.
     |
     |      Parameters
     |      ----------
     |      arrays : sequence of Array
     |      names : List[str] (optional)
     |          Field names for each struct child.
     |      fields : List[Field] (optional)
     |          Field instances for each struct child.
     |      mask : pyarrow.Array[bool] (optional)
     |          Indicate which values are null (True) or not null (False).
     |      memory_pool : MemoryPool (optional)
     |          For memory allocations, if required, otherwise uses default pool.
     |      type : pyarrow.StructType (optional)
     |          Struct type for name and type of each child.
     |
     |      Returns
     |      -------
     |      result : StructArray
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class StructScalar(Scalar, collections.abc.Mapping)
     |  Concrete class for struct scalars.
     |
     |  Method resolution order:
     |      StructScalar
     |      Scalar
     |      _Weakrefable
     |      collections.abc.Mapping
     |      collections.abc.Collection
     |      collections.abc.Sized
     |      collections.abc.Iterable
     |      collections.abc.Container
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __contains__(self, key, /)
     |      Return bool(key in self).
     |
     |  __getitem__(...)
     |      StructScalar.__getitem__(self, key)
     |
     |      Return the child value for the given field.
     |
     |      Parameters
     |      ----------
     |      index : Union[int, str]
     |          Index / position or name of the field.
     |
     |      Returns
     |      -------
     |      result : Scalar
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      StructScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python dict.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  items(self)
     |      StructScalar.items(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from collections.abc.Mapping:
     |
     |  get(self, key, default=None)
     |      D.get(k[,d]) -> D[k] if k in D, else d.  d defaults to None.
     |
     |  keys(self)
     |      D.keys() -> a set-like object providing a view on D's keys
     |
     |  values(self)
     |      D.values() -> an object providing a view on D's values
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from collections.abc.Mapping:
     |
     |  __abstractmethods__ = frozenset({'__getitem__', '__iter__', '__len__'}...
     |
     |  __reversed__ = None
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Collection:
     |
     |  __subclasshook__(C)
     |      Abstract classes can override this to customize issubclass().
     |
     |      This is invoked early on by abc.ABCMeta.__subclasscheck__().
     |      It should return True, False or NotImplemented.  If it returns
     |      NotImplemented, the normal algorithm is used.  Otherwise, it
     |      overrides the normal algorithm (and the outcome is cached).
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from collections.abc.Iterable:
     |
     |  __class_getitem__ = GenericAlias(...)
     |      Represent a PEP 585 generic type
     |
     |      E.g. for t = list[int], t.__origin__ is list and t.__args__ is (int,).

    class StructType(DataType)
     |  Concrete class for struct data types.
     |
     |  ``StructType`` supports direct indexing using ``[...]`` (implemented via
     |  ``__getitem__``) to access its fields.
     |  It will return the struct field with the given index or name.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |
     |  Accessing fields using direct indexing:
     |
     |  >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |  >>> struct_type[0]
     |  pyarrow.Field<x: int32>
     |  >>> struct_type['y']
     |  pyarrow.Field<y: string>
     |
     |  Accessing fields using ``field()``:
     |
     |  >>> struct_type.field(1)
     |  pyarrow.Field<y: string>
     |  >>> struct_type.field('x')
     |  pyarrow.Field<x: int32>
     |
     |  # Creating a schema from the struct type's fields:
     |  >>> pa.schema(list(struct_type))
     |  x: int32
     |  y: string
     |
     |  Method resolution order:
     |      StructType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __getitem__(...)
     |      StructType.__getitem__(self, i)
     |
     |      Return the struct field with the given index or name.
     |
     |      Alias of ``field``.
     |
     |  __iter__(...)
     |      StructType.__iter__(self)
     |
     |      Iterate over struct fields, in order.
     |
     |  __len__(...)
     |      StructType.__len__(self)
     |
     |      Like num_fields().
     |
     |  __reduce__(self)
     |      StructType.__reduce__(self)
     |
     |  field(self, i)
     |      StructType.field(self, i) -> Field
     |
     |      Select a field by its column name or numeric index.
     |
     |      Parameters
     |      ----------
     |      i : int or str
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |      Examples
     |      --------
     |
     |      >>> import pyarrow as pa
     |      >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |
     |      Select the second field:
     |
     |      >>> struct_type.field(1)
     |      pyarrow.Field<y: string>
     |
     |      Select the field named 'x':
     |
     |      >>> struct_type.field('x')
     |      pyarrow.Field<x: int32>
     |
     |  get_all_field_indices(self, name)
     |      StructType.get_all_field_indices(self, name)
     |
     |      Return sorted list of indices for the fields with the given name.
     |
     |      Parameters
     |      ----------
     |      name : str
     |          The name of the field to look up.
     |
     |      Returns
     |      -------
     |      indices : List[int]
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct_type.get_all_field_indices('x')
     |      [0]
     |
     |  get_field_index(self, name)
     |      StructType.get_field_index(self, name)
     |
     |      Return index of the unique field with the given name.
     |
     |      Parameters
     |      ----------
     |      name : str
     |          The name of the field to look up.
     |
     |      Returns
     |      -------
     |      index : int
     |          The index of the field with the given name; -1 if the
     |          name isn't found or there are several fields with the given
     |          name.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |
     |      Index of the field with a name 'y':
     |
     |      >>> struct_type.get_field_index('y')
     |      1
     |
     |      Index of the field that does not exist:
     |
     |      >>> struct_type.get_field_index('z')
     |      -1
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  fields
     |      Lists all fields within the StructType.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> struct_type = pa.struct([('a', pa.int64()), ('b', pa.float64()), ('c', pa.string())])
     |      >>> struct_type.fields
     |      [pyarrow.Field<a: int64>, pyarrow.Field<b: double>, pyarrow.Field<c: string>]
     |
     |  names
     |      Lists the field names.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> struct_type = pa.struct([('a', pa.int64()), ('b', pa.float64()), ('c', pa.string())])
     |      >>> struct_type.names
     |      ['a', 'b', 'c']
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class Table(_Tabular)
     |  A collection of top-level named, equal length Arrow arrays.
     |
     |  Warnings
     |  --------
     |  Do not call this class's constructor directly, use one of the ``from_*``
     |  methods instead.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> n_legs = pa.array([2, 4, 5, 100])
     |  >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |  >>> names = ["n_legs", "animals"]
     |
     |  Construct a Table from arrays:
     |
     |  >>> pa.Table.from_arrays([n_legs, animals], names=names)
     |  pyarrow.Table
     |  n_legs: int64
     |  animals: string
     |  ----
     |  n_legs: [[2,4,5,100]]
     |  animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |  Construct a Table from a RecordBatch:
     |
     |  >>> batch = pa.record_batch([n_legs, animals], names=names)
     |  >>> pa.Table.from_batches([batch])
     |  pyarrow.Table
     |  n_legs: int64
     |  animals: string
     |  ----
     |  n_legs: [[2,4,5,100]]
     |  animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |  Construct a Table from pandas DataFrame:
     |
     |  >>> import pandas as pd
     |  >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
     |  ...                    'n_legs': [2, 4, 5, 100],
     |  ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |  >>> pa.Table.from_pandas(df)
     |  pyarrow.Table
     |  year: int64
     |  n_legs: int64
     |  animals: large_string
     |  ----
     |  year: [[2020,2022,2019,2021]]
     |  n_legs: [[2,4,5,100]]
     |  animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |  Construct a Table from a dictionary of arrays:
     |
     |  >>> pydict = {'n_legs': n_legs, 'animals': animals}
     |  >>> pa.Table.from_pydict(pydict)
     |  pyarrow.Table
     |  n_legs: int64
     |  animals: string
     |  ----
     |  n_legs: [[2,4,5,100]]
     |  animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |  >>> pa.Table.from_pydict(pydict).schema
     |  n_legs: int64
     |  animals: string
     |
     |  Construct a Table from a dictionary of arrays with metadata:
     |
     |  >>> my_metadata={"n_legs": "Number of legs per animal"}
     |  >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema
     |  n_legs: int64
     |  animals: string
     |  -- schema metadata --
     |  n_legs: 'Number of legs per animal'
     |
     |  Construct a Table from a list of rows:
     |
     |  >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, {'year': 2021, 'animals': 'Centipede'}]
     |  >>> pa.Table.from_pylist(pylist)
     |  pyarrow.Table
     |  n_legs: int64
     |  animals: string
     |  ----
     |  n_legs: [[2,null]]
     |  animals: [["Flamingo","Centipede"]]
     |
     |  Construct a Table from a list of rows with pyarrow schema:
     |
     |  >>> my_schema = pa.schema([
     |  ...     pa.field('year', pa.int64()),
     |  ...     pa.field('n_legs', pa.int64()),
     |  ...     pa.field('animals', pa.string())],
     |  ...     metadata={"year": "Year of entry"})
     |  >>> pa.Table.from_pylist(pylist, schema=my_schema).schema
     |  year: int64
     |  n_legs: int64
     |  animals: string
     |  -- schema metadata --
     |  year: 'Year of entry'
     |
     |  Construct a Table with :func:`pyarrow.table`:
     |
     |  >>> pa.table([n_legs, animals], names=names)
     |  pyarrow.Table
     |  n_legs: int64
     |  animals: string
     |  ----
     |  n_legs: [[2,4,5,100]]
     |  animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |  Method resolution order:
     |      Table
     |      _Tabular
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_c_stream__(self, requested_schema=None)
     |      Table.__arrow_c_stream__(self, requested_schema=None)
     |
     |      Export the table as an Arrow C stream PyCapsule.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule, default None
     |          The schema to which the stream should be casted, passed as a
     |          PyCapsule containing a C ArrowSchema representation of the
     |          requested schema.
     |          Currently, this is not supported and will raise a
     |          NotImplementedError if the schema doesn't match the current schema.
     |
     |      Returns
     |      -------
     |      PyCapsule
     |
     |  __reduce__(self)
     |      Table.__reduce__(self)
     |
     |  __sizeof__(self)
     |      Table.__sizeof__(self)
     |
     |  add_column(self, i, field_, column)
     |      Table.add_column(self, int i, field_, column)
     |
     |      Add column to Table at position.
     |
     |      A new table is returned with the column added, the original table
     |      object is left unchanged.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          Index to place the column at.
     |      field_ : str or Field
     |          If a string is passed then the type is deduced from the column
     |          data.
     |      column : Array, list of Array, or values coercible to arrays
     |          Column data.
     |
     |      Returns
     |      -------
     |      Table
     |          New table with the passed column added.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |
     |      Add column:
     |
     |      >>> year = [2021, 2022, 2019, 2021]
     |      >>> table.add_column(0,"year", [year])
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2021,2022,2019,2021]]
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |      Original table is left unchanged:
     |
     |      >>> table
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |  cast(self, target_schema, safe=None, options=None)
     |      Table.cast(self, Schema target_schema, safe=None, options=None)
     |
     |      Cast table values to another schema.
     |
     |      Parameters
     |      ----------
     |      target_schema : Schema
     |          Schema to cast to, the names and order of fields must match.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table.schema
     |      n_legs: int64
     |      animals: string
     |
     |      Define new schema and cast table values:
     |
     |      >>> my_schema = pa.schema([
     |      ...     pa.field('n_legs', pa.duration('s')),
     |      ...     pa.field('animals', pa.string())]
     |      ...     )
     |      >>> table.cast(target_schema=my_schema)
     |      pyarrow.Table
     |      n_legs: duration[s]
     |      animals: string
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |  combine_chunks(self, memory_pool=None)
     |      Table.combine_chunks(self, MemoryPool memory_pool=None)
     |
     |      Make a new table by combining the chunks this table has.
     |
     |      All the underlying chunks in the ChunkedArray of each column are
     |      concatenated into zero or one chunk.
     |
     |      To avoid buffer overflow, binary columns may be combined into
     |      multiple chunks. Chunks will have the maximum possible length.
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          For memory allocations, if required, otherwise use default pool.
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]])
     |      >>> names = ["n_legs", "animals"]
     |      >>> table = pa.table([n_legs, animals], names=names)
     |      >>> table
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,2,4],[4,5,100]]
     |      animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]]
     |      >>> table.combine_chunks()
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,2,4,4,5,100]]
     |      animals: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]]
     |
     |  drop(self, columns)
     |      Table.drop(self, columns)
     |
     |      Drop one or more columns and return a new table.
     |
     |      Alias of Table.drop_columns, but kept for backwards compatibility.
     |
     |      Parameters
     |      ----------
     |      columns : str or list[str]
     |          Field name(s) referencing existing column(s).
     |
     |      Returns
     |      -------
     |      Table
     |          New table without the column(s).
     |
     |  equals(self, other, check_metadata=False)
     |      Table.equals(self, Table other, bool check_metadata=False)
     |
     |      Check if contents of two tables are equal.
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Table
     |          Table to compare against.
     |      check_metadata : bool, default False
     |          Whether schema metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> names=["n_legs", "animals"]
     |      >>> table = pa.Table.from_arrays([n_legs, animals], names=names)
     |      >>> table_0 = pa.Table.from_arrays([])
     |      >>> table_1 = pa.Table.from_arrays([n_legs, animals],
     |      ...                                 names=names,
     |      ...                                 metadata={"n_legs": "Number of legs per animal"})
     |      >>> table.equals(table)
     |      True
     |      >>> table.equals(table_0)
     |      False
     |      >>> table.equals(table_1)
     |      True
     |      >>> table.equals(table_1, check_metadata=True)
     |      False
     |
     |  flatten(self, memory_pool=None)
     |      Table.flatten(self, MemoryPool memory_pool=None)
     |
     |      Flatten this Table.
     |
     |      Each column with a struct type is flattened
     |      into one column per struct field.  Other columns are left unchanged.
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          For memory allocations, if required, otherwise use default pool
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
     |      ...                    {'year': 2022, 'n_legs': 4}])
     |      >>> month = pa.array([4, 6])
     |      >>> table = pa.Table.from_arrays([struct,month],
     |      ...                              names = ["a", "month"])
     |      >>> table
     |      pyarrow.Table
     |      a: struct<n_legs: int64, animals: string, year: int64>
     |        child 0, n_legs: int64
     |        child 1, animals: string
     |        child 2, year: int64
     |      month: int64
     |      ----
     |      a: [
     |        -- is_valid: all not null
     |        -- child 0 type: int64
     |      [2,4]
     |        -- child 1 type: string
     |      ["Parrot",null]
     |        -- child 2 type: int64
     |      [null,2022]]
     |      month: [[4,6]]
     |
     |      Flatten the columns with struct field:
     |
     |      >>> table.flatten()
     |      pyarrow.Table
     |      a.n_legs: int64
     |      a.animals: string
     |      a.year: int64
     |      month: int64
     |      ----
     |      a.n_legs: [[2,4]]
     |      a.animals: [["Parrot",null]]
     |      a.year: [[null,2022]]
     |      month: [[4,6]]
     |
     |  get_total_buffer_size(self)
     |      Table.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the table.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
     |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> table.get_total_buffer_size()
     |      76
     |
     |  group_by(self, keys, use_threads=True)
     |      Table.group_by(self, keys, use_threads=True)
     |
     |      Declare a grouping over the columns of the table.
     |
     |      Resulting grouping can then be used to perform aggregations
     |      with a subsequent ``aggregate()`` method.
     |
     |      Parameters
     |      ----------
     |      keys : str or list[str]
     |          Name of the columns that should be used as the grouping key.
     |      use_threads : bool, default True
     |          Whether to use multithreading or not. When set to True (the
     |          default), no stable ordering of the output is guaranteed.
     |
     |      Returns
     |      -------
     |      TableGroupBy
     |
     |      See Also
     |      --------
     |      TableGroupBy.aggregate
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],
     |      ...                   'n_legs': [2, 2, 4, 4, 5, 100],
     |      ...                   'animal': ["Flamingo", "Parrot", "Dog", "Horse",
     |      ...                              "Brittle stars", "Centipede"]})
     |      >>> table.group_by('year').aggregate([('n_legs', 'sum')])
     |      pyarrow.Table
     |      year: int64
     |      n_legs_sum: int64
     |      ----
     |      year: [[2020,2022,2021,2019]]
     |      n_legs_sum: [[2,6,104,5]]
     |
     |  join(self, right_table, keys, right_keys=None, join_type='left outer', left_suffix=None, right_suffix=None, coalesce_keys=True, use_threads=True, filter_expression=None)
     |      Table.join(self, right_table, keys, right_keys=None, join_type='left outer', left_suffix=None, right_suffix=None, coalesce_keys=True, use_threads=True, filter_expression=None)
     |
     |      Perform a join between this table and another one.
     |
     |      Result of the join will be a new Table, where further
     |      operations can be applied.
     |
     |      Parameters
     |      ----------
     |      right_table : Table
     |          The table to join to the current one, acting as the right table
     |          in the join operation.
     |      keys : str or list[str]
     |          The columns from current table that should be used as keys
     |          of the join operation left side.
     |      right_keys : str or list[str], default None
     |          The columns from the right_table that should be used as keys
     |          on the join operation right side.
     |          When ``None`` use the same key names as the left table.
     |      join_type : str, default "left outer"
     |          The kind of join that should be performed, one of
     |          ("left semi", "right semi", "left anti", "right anti",
     |          "inner", "left outer", "right outer", "full outer")
     |      left_suffix : str, default None
     |          Which suffix to add to left column names. This prevents confusion
     |          when the columns in left and right tables have colliding names.
     |      right_suffix : str, default None
     |          Which suffix to add to the right column names. This prevents confusion
     |          when the columns in left and right tables have colliding names.
     |      coalesce_keys : bool, default True
     |          If the duplicated keys should be omitted from one of the sides
     |          in the join result.
     |      use_threads : bool, default True
     |          Whether to use multithreading or not.
     |      filter_expression : pyarrow.compute.Expression
     |          Residual filter which is applied to matching row.
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pyarrow.compute as pc
     |      >>> t1 = pa.table({'id': [1, 2, 3],
     |      ...                'year': [2020, 2022, 2019]})
     |      >>> t2 = pa.table({'id': [3, 4],
     |      ...                'n_legs': [5, 100],
     |      ...                'animal': ["Brittle stars", "Centipede"]})
     |
     |      Left outer join:
     |
     |      >>> t1.join(t2, 'id').combine_chunks().sort_by('year')
     |      pyarrow.Table
     |      id: int64
     |      year: int64
     |      n_legs: int64
     |      animal: string
     |      ----
     |      id: [[3,1,2]]
     |      year: [[2019,2020,2022]]
     |      n_legs: [[5,null,null]]
     |      animal: [["Brittle stars",null,null]]
     |
     |      Full outer join:
     |
     |      >>> t1.join(t2, 'id', join_type="full outer").combine_chunks().sort_by('year')
     |      pyarrow.Table
     |      id: int64
     |      year: int64
     |      n_legs: int64
     |      animal: string
     |      ----
     |      id: [[3,1,2,4]]
     |      year: [[2019,2020,2022,null]]
     |      n_legs: [[5,null,null,100]]
     |      animal: [["Brittle stars",null,null,"Centipede"]]
     |
     |      Right outer join:
     |
     |      >>> t1.join(t2, 'id', join_type="right outer").combine_chunks().sort_by('year')
     |      pyarrow.Table
     |      year: int64
     |      id: int64
     |      n_legs: int64
     |      animal: string
     |      ----
     |      year: [[2019,null]]
     |      id: [[3,4]]
     |      n_legs: [[5,100]]
     |      animal: [["Brittle stars","Centipede"]]
     |
     |      Right anti join:
     |
     |      >>> t1.join(t2, 'id', join_type="right anti")
     |      pyarrow.Table
     |      id: int64
     |      n_legs: int64
     |      animal: string
     |      ----
     |      id: [[4]]
     |      n_legs: [[100]]
     |      animal: [["Centipede"]]
     |
     |      Inner join with intended mismatch filter expression:
     |
     |      >>> t1.join(t2, 'id', join_type="inner", filter_expression=pc.equal(pc.field("n_legs"), 100))
     |      pyarrow.Table
     |      id: int64
     |      year: int64
     |      n_legs: int64
     |      animal: string
     |      ----
     |      id: []
     |      year: []
     |      n_legs: []
     |      animal: []
     |
     |  join_asof(self, right_table, on, by, tolerance, right_on=None, right_by=None)
     |      Table.join_asof(self, right_table, on, by, tolerance, right_on=None, right_by=None)
     |
     |      Perform an asof join between this table and another one.
     |
     |      This is similar to a left-join except that we match on nearest key rather
     |      than equal keys. Both tables must be sorted by the key. This type of join
     |      is most useful for time series data that are not perfectly aligned.
     |
     |      Optionally match on equivalent keys with "by" before searching with "on".
     |
     |      Result of the join will be a new Table, where further
     |      operations can be applied.
     |
     |      Parameters
     |      ----------
     |      right_table : Table
     |          The table to join to the current one, acting as the right table
     |          in the join operation.
     |      on : str
     |          The column from current table that should be used as the "on" key
     |          of the join operation left side.
     |
     |          An inexact match is used on the "on" key, i.e. a row is considered a
     |          match if and only if ``right.on - left.on`` is in the range
     |          ``[min(0, tolerance), max(0, tolerance)]``.
     |
     |          The input dataset must be sorted by the "on" key. Must be a single
     |          field of a common type.
     |
     |          Currently, the "on" key must be an integer, date, or timestamp type.
     |      by : str or list[str]
     |          The columns from current table that should be used as the keys
     |          of the join operation left side. The join operation is then done
     |          only for the matches in these columns.
     |      tolerance : int
     |          The tolerance for inexact "on" key matching. A right row is considered
     |          a match with a left row if ``right.on - left.on`` is in the range
     |          ``[min(0, tolerance), max(0, tolerance)]``. ``tolerance`` may be:
     |
     |          - negative, in which case a past-as-of-join occurs
     |            (match iff ``tolerance <= right.on - left.on <= 0``);
     |          - or positive, in which case a future-as-of-join occurs
     |            (match iff ``0 <= right.on - left.on <= tolerance``);
     |          - or zero, in which case an exact-as-of-join occurs
     |            (match iff ``right.on == left.on``).
     |
     |          The tolerance is interpreted in the same units as the "on" key.
     |      right_on : str or list[str], default None
     |          The columns from the right_table that should be used as the on key
     |          on the join operation right side.
     |          When ``None`` use the same key name as the left table.
     |      right_by : str or list[str], default None
     |          The columns from the right_table that should be used as keys
     |          on the join operation right side.
     |          When ``None`` use the same key names as the left table.
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t1 = pa.table({'id': [1, 3, 2, 3, 3],
     |      ...                'year': [2020, 2021, 2022, 2022, 2023]})
     |      >>> t2 = pa.table({'id': [3, 4],
     |      ...                'year': [2020, 2021],
     |      ...                'n_legs': [5, 100],
     |      ...                'animal': ["Brittle stars", "Centipede"]})
     |
     |      >>> t1.join_asof(t2, on='year', by='id', tolerance=-2)
     |      pyarrow.Table
     |      id: int64
     |      year: int64
     |      n_legs: int64
     |      animal: string
     |      ----
     |      id: [[1,3,2,3,3]]
     |      year: [[2020,2021,2022,2022,2023]]
     |      n_legs: [[null,5,null,5,null]]
     |      animal: [[null,"Brittle stars",null,"Brittle stars",null]]
     |
     |  remove_column(self, i)
     |      Table.remove_column(self, int i)
     |
     |      Create new Table with the indicated column removed.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          Index of column to remove.
     |
     |      Returns
     |      -------
     |      Table
     |          New table without the column.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table.remove_column(1)
     |      pyarrow.Table
     |      n_legs: int64
     |      ----
     |      n_legs: [[2,4,5,100]]
     |
     |  rename_columns(self, names)
     |      Table.rename_columns(self, names)
     |
     |      Create new table with columns renamed to provided names.
     |
     |      Parameters
     |      ----------
     |      names : list[str] or dict[str, str]
     |          List of new column names or mapping of old column names to new column names.
     |
     |          If a mapping of old to new column names is passed, then all columns which are
     |          found to match a provided old column name will be renamed to the new column name.
     |          If any column names are not found in the mapping, a KeyError will be raised.
     |
     |      Raises
     |      ------
     |      KeyError
     |          If any of the column names passed in the names mapping do not exist.
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> new_names = ["n", "name"]
     |      >>> table.rename_columns(new_names)
     |      pyarrow.Table
     |      n: int64
     |      name: string
     |      ----
     |      n: [[2,4,5,100]]
     |      name: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |      >>> new_names = {"n_legs": "n", "animals": "name"}
     |      >>> table.rename_columns(new_names)
     |      pyarrow.Table
     |      n: int64
     |      name: string
     |      ----
     |      n: [[2,4,5,100]]
     |      name: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |  replace_schema_metadata(self, metadata=None)
     |      Table.replace_schema_metadata(self, metadata=None)
     |
     |      Create shallow copy of table by replacing schema
     |      key-value metadata with the indicated new metadata (which may be None),
     |      which deletes any existing metadata.
     |
     |      Parameters
     |      ----------
     |      metadata : dict, default None
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
     |      ...                    'n_legs': [2, 4, 5, 100],
     |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table = pa.Table.from_pandas(df)
     |
     |      Constructing a Table with pyarrow schema and metadata:
     |
     |      >>> my_schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |      >>> table= pa.table(df, my_schema)
     |      >>> table.schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |      pandas: ...
     |
     |      Create a shallow copy of a Table with deleted schema metadata:
     |
     |      >>> table.replace_schema_metadata().schema
     |      n_legs: int64
     |      animals: string
     |
     |      Create a shallow copy of a Table with new schema metadata:
     |
     |      >>> metadata={"animals": "Which animal"}
     |      >>> table.replace_schema_metadata(metadata = metadata).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      animals: 'Which animal'
     |
     |  select(self, columns)
     |      Table.select(self, columns)
     |
     |      Select columns of the Table.
     |
     |      Returns a new Table with the specified columns, and metadata
     |      preserved.
     |
     |      Parameters
     |      ----------
     |      columns : list-like
     |          The column names or integer indices to select.
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
     |      ...                   'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table.select([0,1])
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      ----
     |      year: [[2020,2022,2019,2021]]
     |      n_legs: [[2,4,5,100]]
     |      >>> table.select(["year"])
     |      pyarrow.Table
     |      year: int64
     |      ----
     |      year: [[2020,2022,2019,2021]]
     |
     |  set_column(self, i, field_, column)
     |      Table.set_column(self, int i, field_, column)
     |
     |      Replace column in Table at position.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          Index to place the column at.
     |      field_ : str or Field
     |          If a string is passed then the type is deduced from the column
     |          data.
     |      column : Array, list of Array, or values coercible to arrays
     |          Column data.
     |
     |      Returns
     |      -------
     |      Table
     |          New table with the passed column set.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |
     |      Replace a column:
     |
     |      >>> year = [2021, 2022, 2019, 2021]
     |      >>> table.set_column(1,'year', [year])
     |      pyarrow.Table
     |      n_legs: int64
     |      year: int64
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      year: [[2021,2022,2019,2021]]
     |
     |  slice(self, offset=0, length=None)
     |      Table.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this Table.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of table to slice.
     |      length : int, default None
     |          Length of slice (default is until end of table starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
     |      ...                   'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table.slice(length=3)
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2020,2022,2019]]
     |      n_legs: [[2,4,5]]
     |      animals: [["Flamingo","Horse","Brittle stars"]]
     |      >>> table.slice(offset=2)
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2019,2021]]
     |      n_legs: [[5,100]]
     |      animals: [["Brittle stars","Centipede"]]
     |      >>> table.slice(offset=2, length=1)
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2019]]
     |      n_legs: [[5]]
     |      animals: [["Brittle stars"]]
     |
     |  to_batches(self, max_chunksize=None)
     |      Table.to_batches(self, max_chunksize=None)
     |
     |      Convert Table to a list of RecordBatch objects.
     |
     |      Note that this method is zero-copy, it merely exposes the same data
     |      under a different API.
     |
     |      Parameters
     |      ----------
     |      max_chunksize : int, default None
     |          Maximum number of rows for each RecordBatch chunk. Individual chunks
     |          may be smaller depending on the chunk layout of individual columns.
     |
     |      Returns
     |      -------
     |      list[RecordBatch]
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
     |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table = pa.Table.from_pandas(df)
     |
     |      Convert a Table to a RecordBatch:
     |
     |      >>> table.to_batches()[0].to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |
     |      Convert a Table to a list of RecordBatches:
     |
     |      >>> table.to_batches(max_chunksize=2)[0].to_pandas()
     |         n_legs   animals
     |      0       2  Flamingo
     |      1       4     Horse
     |      >>> table.to_batches(max_chunksize=2)[1].to_pandas()
     |         n_legs        animals
     |      0       5  Brittle stars
     |      1     100      Centipede
     |
     |  to_reader(self, max_chunksize=None)
     |      Table.to_reader(self, max_chunksize=None)
     |
     |      Convert the Table to a RecordBatchReader.
     |
     |      Note that this method is zero-copy, it merely exposes the same data
     |      under a different API.
     |
     |      Parameters
     |      ----------
     |      max_chunksize : int, default None
     |          Maximum number of rows for each RecordBatch chunk. Individual chunks
     |          may be smaller depending on the chunk layout of individual columns.
     |
     |      Returns
     |      -------
     |      RecordBatchReader
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |
     |      Convert a Table to a RecordBatchReader:
     |
     |      >>> table.to_reader()
     |      <pyarrow.lib.RecordBatchReader object at ...>
     |
     |      >>> reader = table.to_reader()
     |      >>> reader.schema
     |      n_legs: int64
     |      animals: string
     |      >>> reader.read_all()
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |  to_struct_array(self, max_chunksize=None)
     |      Table.to_struct_array(self, max_chunksize=None)
     |
     |      Convert to a chunked array of struct type.
     |
     |      Parameters
     |      ----------
     |      max_chunksize : int, default None
     |          Maximum number of rows for ChunkedArray chunks. Individual chunks
     |          may be smaller depending on the chunk layout of individual columns.
     |
     |      Returns
     |      -------
     |      ChunkedArray
     |
     |  unify_dictionaries(self, memory_pool=None)
     |      Table.unify_dictionaries(self, MemoryPool memory_pool=None)
     |
     |      Unify dictionaries across all chunks.
     |
     |      This method returns an equivalent table, but where all chunks of
     |      each column share the same dictionary values.  Dictionary indices
     |      are transposed accordingly.
     |
     |      Columns without dictionaries are returned unchanged.
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          For memory allocations, if required, otherwise use default pool
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode()
     |      >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode()
     |      >>> c_arr = pa.chunked_array([arr_1, arr_2])
     |      >>> table = pa.table([c_arr], names=["animals"])
     |      >>> table
     |      pyarrow.Table
     |      animals: dictionary<values=string, indices=int32, ordered=0>
     |      ----
     |      animals: [  -- dictionary:
     |      ["Flamingo","Parrot","Dog"]  -- indices:
     |      [0,1,2],  -- dictionary:
     |      ["Horse","Brittle stars","Centipede"]  -- indices:
     |      [0,1,2]]
     |
     |      Unify dictionaries across both chunks:
     |
     |      >>> table.unify_dictionaries()
     |      pyarrow.Table
     |      animals: dictionary<values=string, indices=int32, ordered=0>
     |      ----
     |      animals: [  -- dictionary:
     |      ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]  -- indices:
     |      [0,1,2],  -- dictionary:
     |      ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]  -- indices:
     |      [3,4,5]]
     |
     |  validate(self, *, full=False)
     |      Table.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Class methods defined here:
     |
     |  from_pandas(df, schema=None, preserve_index=None, nthreads=None, columns=None, safe=True)
     |      Table.from_pandas(cls, df, Schema schema=None, preserve_index=None, nthreads=None, columns=None, bool safe=True)
     |
     |      Convert pandas.DataFrame to an Arrow Table.
     |
     |      The column types in the resulting Arrow Table are inferred from the
     |      dtypes of the pandas.Series in the DataFrame. In the case of non-object
     |      Series, the NumPy dtype is translated to its Arrow equivalent. In the
     |      case of `object`, we need to guess the datatype by looking at the
     |      Python objects in this Series.
     |
     |      Be aware that Series of the `object` dtype don't carry enough
     |      information to always lead to a meaningful Arrow type. In the case that
     |      we cannot infer a type, e.g. because the DataFrame is of length 0 or
     |      the Series only contains None/nan objects, the type is set to
     |      null. This behavior can be avoided by constructing an explicit schema
     |      and passing it to this function.
     |
     |      Parameters
     |      ----------
     |      df : pandas.DataFrame
     |      schema : pyarrow.Schema, optional
     |          The expected schema of the Arrow Table. This can be used to
     |          indicate the type of columns if we cannot infer it automatically.
     |          If passed, the output will have exactly this schema. Columns
     |          specified in the schema that are not found in the DataFrame columns
     |          or its index will raise an error. Additional columns or index
     |          levels in the DataFrame which are not specified in the schema will
     |          be ignored.
     |      preserve_index : bool, optional
     |          Whether to store the index as an additional column in the resulting
     |          ``Table``. The default of None will store the index as a column,
     |          except for RangeIndex which is stored as metadata only. Use
     |          ``preserve_index=True`` to force it to be stored as a column.
     |      nthreads : int, default None
     |          If greater than 1, convert columns to Arrow in parallel using
     |          indicated number of threads. By default, this follows
     |          :func:`pyarrow.cpu_count` (may use up to system CPU count threads).
     |      columns : list, optional
     |         List of column to be converted. If None, use all columns.
     |      safe : bool, default True
     |         Check for overflows or other unsafe conversions.
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
     |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> pa.Table.from_pandas(df)
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: large_string
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_arrays(arrays, names=None, schema=None, metadata=None)
     |      Table.from_arrays(arrays, names=None, schema=None, metadata=None)
     |
     |      Construct a Table from Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      arrays : list of pyarrow.Array or pyarrow.ChunkedArray
     |          Equal-length arrays that should form the table.
     |      names : list of str, optional
     |          Names for the table columns. If not passed, schema must be passed.
     |      schema : Schema, default None
     |          Schema for the created table. If not passed, names must be passed.
     |      metadata : dict or Mapping, default None
     |          Optional metadata for the schema (if inferred).
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> names = ["n_legs", "animals"]
     |
     |      Construct a Table from arrays:
     |
     |      >>> pa.Table.from_arrays([n_legs, animals], names=names)
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |      Construct a Table from arrays with metadata:
     |
     |      >>> my_metadata={"n_legs": "Number of legs per animal"}
     |      >>> pa.Table.from_arrays([n_legs, animals],
     |      ...                       names=names,
     |      ...                       metadata=my_metadata)
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |      >>> pa.Table.from_arrays([n_legs, animals],
     |      ...                       names=names,
     |      ...                       metadata=my_metadata).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |      Construct a Table from arrays with pyarrow schema:
     |
     |      >>> my_schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())],
     |      ...     metadata={"animals": "Name of the animal species"})
     |      >>> pa.Table.from_arrays([n_legs, animals],
     |      ...                       schema=my_schema)
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |      >>> pa.Table.from_arrays([n_legs, animals],
     |      ...                       schema=my_schema).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      animals: 'Name of the animal species'
     |
     |  from_batches(batches, schema=None)
     |      Table.from_batches(batches, Schema schema=None)
     |
     |      Construct a Table from a sequence or iterator of Arrow RecordBatches.
     |
     |      Parameters
     |      ----------
     |      batches : sequence or iterator of RecordBatch
     |          Sequence of RecordBatch to be converted, all schemas must be equal.
     |      schema : Schema, default None
     |          If not passed, will be inferred from the first RecordBatch.
     |
     |      Returns
     |      -------
     |      Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> names = ["n_legs", "animals"]
     |      >>> batch = pa.record_batch([n_legs, animals], names=names)
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |
     |      Construct a Table from a RecordBatch:
     |
     |      >>> pa.Table.from_batches([batch])
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |      Construct a Table from a sequence of RecordBatches:
     |
     |      >>> pa.Table.from_batches([batch, batch])
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,4,5,100],[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Flamingo","Horse","Brittle stars","Centipede"]]
     |
     |  from_struct_array(struct_array)
     |      Table.from_struct_array(struct_array)
     |
     |      Construct a Table from a StructArray.
     |
     |      Each field in the StructArray will become a column in the resulting
     |      ``Table``.
     |
     |      Parameters
     |      ----------
     |      struct_array : StructArray or ChunkedArray
     |          Array to construct the table from.
     |
     |      Returns
     |      -------
     |      pyarrow.Table
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
     |      ...                    {'year': 2022, 'n_legs': 4, 'animals': 'Goat'}])
     |      >>> pa.Table.from_struct_array(struct).to_pandas()
     |         n_legs animals    year
     |      0       2  Parrot     NaN
     |      1       4    Goat  2022.0
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  is_cpu
     |      Whether all ChunkedArrays are CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the table.
     |
     |      In other words, the sum of bytes from all buffer ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will only be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
     |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> table.nbytes
     |      72
     |
     |  num_columns
     |      Number of columns in this table.
     |
     |      Returns
     |      -------
     |      int
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
     |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> table = pa.Table.from_pandas(df)
     |      >>> table.num_columns
     |      2
     |
     |  num_rows
     |      Number of rows in this table.
     |
     |      Due to the definition of a table, all columns have the same number of
     |      rows.
     |
     |      Returns
     |      -------
     |      int
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
     |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> table = pa.Table.from_pandas(df)
     |      >>> table.num_rows
     |      4
     |
     |  schema
     |      Schema of the table and its columns.
     |
     |      Returns
     |      -------
     |      Schema
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table.schema
     |      n_legs: int64
     |      animals: string
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _Tabular:
     |
     |  __array__(self, dtype=None, copy=None)
     |      _Tabular.__array__(self, dtype=None, copy=None)
     |
     |  __dataframe__(self, nan_as_null: 'bool' = False, allow_copy: 'bool' = True)
     |      _Tabular.__dataframe__(self, bool nan_as_null: bool = False, bool allow_copy: bool = True)
     |
     |      Return the dataframe interchange object implementing the interchange protocol.
     |
     |      Parameters
     |      ----------
     |      nan_as_null : bool, default False
     |          Whether to tell the DataFrame to overwrite null values in the data
     |          with ``NaN`` (or ``NaT``).
     |      allow_copy : bool, default True
     |          Whether to allow memory copying when exporting. If set to False
     |          it would cause non-zero-copy exports to fail.
     |
     |      Returns
     |      -------
     |      DataFrame interchange object
     |          The object which consuming library can use to ingress the dataframe.
     |
     |      Notes
     |      -----
     |      Details on the interchange protocol:
     |      https://data-apis.org/dataframe-protocol/latest/index.html
     |      `nan_as_null` currently has no effect; once support for nullable extension
     |      dtypes is added, this value should be propagated to columns.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      _Tabular.__getitem__(self, key)
     |
     |      Slice or return column at given index or column name
     |
     |      Parameters
     |      ----------
     |      key : integer, str, or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      Array (from RecordBatch) or ChunkedArray (from Table) for column input.
     |      RecordBatch or Table for slice input.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce_cython__(self)
     |      _Tabular.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _Tabular.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _Tabular.__setstate_cython__(self, __pyx_state)
     |
     |  append_column(self, field_, column)
     |      _Tabular.append_column(self, field_, column)
     |
     |      Append column at end of columns.
     |
     |      Parameters
     |      ----------
     |      field_ : str or Field
     |          If a string is passed then the type is deduced from the column
     |          data.
     |      column : Array or value coercible to array
     |          Column data.
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |          New table or record batch with the passed column added.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |
     |      Append column at the end:
     |
     |      >>> year = [2021, 2022, 2019, 2021]
     |      >>> table.append_column('year', [year])
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      year: int64
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |      year: [[2021,2022,2019,2021]]
     |
     |  column(self, i)
     |      _Tabular.column(self, i)
     |
     |      Select single column from Table or RecordBatch.
     |
     |      Parameters
     |      ----------
     |      i : int or string
     |          The index or name of the column to retrieve.
     |
     |      Returns
     |      -------
     |      column : Array (for RecordBatch) or ChunkedArray (for Table)
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
     |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table = pa.Table.from_pandas(df)
     |
     |      Select a column by numeric index:
     |
     |      >>> table.column(0)
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          2,
     |          4,
     |          5,
     |          100
     |        ]
     |      ]
     |
     |      Select a column by its name:
     |
     |      >>> table.column("animals")
     |      <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          "Flamingo",
     |          "Horse",
     |          "Brittle stars",
     |          "Centipede"
     |        ]
     |      ]
     |
     |  drop_columns(self, columns)
     |      _Tabular.drop_columns(self, columns)
     |
     |      Drop one or more columns and return a new Table or RecordBatch.
     |
     |      Parameters
     |      ----------
     |      columns : str or list[str]
     |          Field name(s) referencing existing column(s).
     |
     |      Raises
     |      ------
     |      KeyError
     |          If any of the passed column names do not exist.
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |          A tabular object without the column(s).
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
     |      ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table = pa.Table.from_pandas(df)
     |
     |      Drop one column:
     |
     |      >>> table.drop_columns("animals")
     |      pyarrow.Table
     |      n_legs: int64
     |      ----
     |      n_legs: [[2,4,5,100]]
     |
     |      Drop one or more columns:
     |
     |      >>> table.drop_columns(["n_legs", "animals"])
     |      pyarrow.Table
     |      ...
     |      ----
     |
     |  drop_null(self)
     |      _Tabular.drop_null(self)
     |
     |      Remove rows that contain missing values from a Table or RecordBatch.
     |
     |      See :func:`pyarrow.compute.drop_null` for full usage.
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |          A tabular object with the same schema, with rows containing
     |          no missing values.
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'year': [None, 2022, 2019, 2021],
     |      ...                   'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> table.drop_null()
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2022,2021]]
     |      n_legs: [[4,100]]
     |      animals: [["Horse","Centipede"]]
     |
     |  field(self, i)
     |      _Tabular.field(self, i)
     |
     |      Select a schema field by its column name or numeric index.
     |
     |      Parameters
     |      ----------
     |      i : int or string
     |          The index or name of the field to retrieve.
     |
     |      Returns
     |      -------
     |      Field
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table.field(0)
     |      pyarrow.Field<n_legs: int64>
     |      >>> table.field(1)
     |      pyarrow.Field<animals: string>
     |
     |  filter(self, mask, null_selection_behavior='drop')
     |      _Tabular.filter(self, mask, null_selection_behavior='drop')
     |
     |      Select rows from the table or record batch based on a boolean mask.
     |
     |      The Table can be filtered based on a mask, which will be passed to
     |      :func:`pyarrow.compute.filter` to perform the filtering, or it can
     |      be filtered through a boolean :class:`.Expression`
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like or .Expression
     |          The boolean mask or the :class:`.Expression` to filter the table with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled, does nothing if
     |          an :class:`.Expression` is used.
     |
     |      Returns
     |      -------
     |      filtered : Table or RecordBatch
     |          A tabular object of the same schema, with only the rows selected
     |          by applied filtering
     |
     |      Examples
     |      --------
     |      Using a Table (works similarly for RecordBatch):
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
     |      ...                   'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |
     |      Define an expression and select rows:
     |
     |      >>> import pyarrow.compute as pc
     |      >>> expr = pc.field("year") <= 2020
     |      >>> table.filter(expr)
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2020,2019]]
     |      n_legs: [[2,5]]
     |      animals: [["Flamingo","Brittle stars"]]
     |
     |      Define a mask and select rows:
     |
     |      >>> mask=[True, True, False, None]
     |      >>> table.filter(mask)
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2020,2022]]
     |      n_legs: [[2,4]]
     |      animals: [["Flamingo","Horse"]]
     |      >>> table.filter(mask, null_selection_behavior='emit_null')
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2020,2022,null]]
     |      n_legs: [[2,4,null]]
     |      animals: [["Flamingo","Horse",null]]
     |
     |  itercolumns(self)
     |      _Tabular.itercolumns(self)
     |
     |      Iterator over all columns in their numerical order.
     |
     |      Yields
     |      ------
     |      Array (for RecordBatch) or ChunkedArray (for Table)
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
     |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> for i in table.itercolumns():
     |      ...     print(i.null_count)
     |      ...
     |      2
     |      1
     |
     |  sort_by(self, sorting, **kwargs)
     |      _Tabular.sort_by(self, sorting, **kwargs)
     |
     |      Sort the Table or RecordBatch by one or multiple columns.
     |
     |      Parameters
     |      ----------
     |      sorting : str or list[tuple(name, order)]
     |          Name of the column to use to sort (ascending), or
     |          a list of multiple sorting conditions where
     |          each entry is a tuple with column name
     |          and sorting order ("ascending" or "descending")
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |          A new tabular object sorted according to the sort keys.
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],
     |      ...                   'n_legs': [2, 2, 4, 4, 5, 100],
     |      ...                   'animal': ["Flamingo", "Parrot", "Dog", "Horse",
     |      ...                   "Brittle stars", "Centipede"]})
     |      >>> table.sort_by('animal')
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animal: string
     |      ----
     |      year: [[2019,2021,2021,2020,2022,2022]]
     |      n_legs: [[5,100,4,2,4,2]]
     |      animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]]
     |
     |  take(self, indices)
     |      _Tabular.take(self, indices)
     |
     |      Select rows from a Table or RecordBatch.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the tabular object whose rows will be returned.
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |          A tabular object with the same schema, containing the taken rows.
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'year': [2020, 2022, 2019, 2021],
     |      ...                   'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table.take([1,3])
     |      pyarrow.Table
     |      year: int64
     |      n_legs: int64
     |      animals: string
     |      ----
     |      year: [[2022,2021]]
     |      n_legs: [[4,100]]
     |      animals: [["Horse","Centipede"]]
     |
     |  to_pydict(self, *, maps_as_pydicts=None)
     |      _Tabular.to_pydict(self, *, maps_as_pydicts=None)
     |
     |      Convert the Table or RecordBatch to a dict or OrderedDict.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      dict
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
     |      >>> table = pa.Table.from_arrays([n_legs, animals], names=["n_legs", "animals"])
     |      >>> table.to_pydict()
     |      {'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']}
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      _Tabular.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert the Table or RecordBatch to a list of rows / dictionaries.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      list
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> data = [[2, 4, 5, 100],
     |      ...         ["Flamingo", "Horse", "Brittle stars", "Centipede"]]
     |      >>> table = pa.table(data, names=["n_legs", "animals"])
     |      >>> table.to_pylist()
     |      [{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ...
     |
     |  to_string(self, *, show_metadata=False, preview_cols=0)
     |      _Tabular.to_string(self, *, show_metadata=False, preview_cols=0)
     |
     |      Return human-readable string representation of Table or RecordBatch.
     |
     |      Parameters
     |      ----------
     |      show_metadata : bool, default False
     |          Display Field-level and Schema-level KeyValueMetadata.
     |      preview_cols : int, default 0
     |          Display values of the columns for the first N columns.
     |
     |      Returns
     |      -------
     |      str
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from _Tabular:
     |
     |  from_pydict(mapping, schema=None, metadata=None)
     |      _Tabular.from_pydict(cls, mapping, schema=None, metadata=None)
     |
     |      Construct a Table or RecordBatch from Arrow arrays or columns.
     |
     |      Parameters
     |      ----------
     |      mapping : dict or Mapping
     |          A mapping of strings to Arrays or Python lists.
     |      schema : Schema, default None
     |          If not passed, will be inferred from the Mapping values.
     |      metadata : dict or Mapping, default None
     |          Optional metadata for the schema (if inferred).
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> pydict = {'n_legs': n_legs, 'animals': animals}
     |
     |      Construct a Table from a dictionary of arrays:
     |
     |      >>> pa.Table.from_pydict(pydict)
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,4,5,100]]
     |      animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
     |      >>> pa.Table.from_pydict(pydict).schema
     |      n_legs: int64
     |      animals: string
     |
     |      Construct a Table from a dictionary of arrays with metadata:
     |
     |      >>> my_metadata={"n_legs": "Number of legs per animal"}
     |      >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |      Construct a Table from a dictionary of arrays with pyarrow schema:
     |
     |      >>> my_schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |      >>> pa.Table.from_pydict(pydict, schema=my_schema).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |  from_pylist(mapping, schema=None, metadata=None)
     |      _Tabular.from_pylist(cls, mapping, schema=None, metadata=None)
     |
     |      Construct a Table or RecordBatch from list of rows / dictionaries.
     |
     |      Parameters
     |      ----------
     |      mapping : list of dicts of rows
     |          A mapping of strings to row values.
     |      schema : Schema, default None
     |          If not passed, will be inferred from the first row of the
     |          mapping values.
     |      metadata : dict or Mapping, default None
     |          Optional metadata for the schema (if inferred).
     |
     |      Returns
     |      -------
     |      Table or RecordBatch
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'},
     |      ...           {'n_legs': 4, 'animals': 'Dog'}]
     |
     |      Construct a Table from a list of rows:
     |
     |      >>> pa.Table.from_pylist(pylist)
     |      pyarrow.Table
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [[2,4]]
     |      animals: [["Flamingo","Dog"]]
     |
     |      Construct a Table from a list of rows with metadata:
     |
     |      >>> my_metadata={"n_legs": "Number of legs per animal"}
     |      >>> pa.Table.from_pylist(pylist, metadata=my_metadata).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |      Construct a Table from a list of rows with pyarrow schema:
     |
     |      >>> my_schema = pa.schema([
     |      ...     pa.field('n_legs', pa.int64()),
     |      ...     pa.field('animals', pa.string())],
     |      ...     metadata={"n_legs": "Number of legs per animal"})
     |      >>> pa.Table.from_pylist(pylist, schema=my_schema).schema
     |      n_legs: int64
     |      animals: string
     |      -- schema metadata --
     |      n_legs: 'Number of legs per animal'
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from _Tabular:
     |
     |  column_names
     |      Names of the Table or RecordBatch columns.
     |
     |      Returns
     |      -------
     |      list of str
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [2, 4, 5, 100],
     |      ...                   'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
     |      >>> table.column_names
     |      ['n_legs', 'animals']
     |
     |  columns
     |      List of all columns in numerical order.
     |
     |      Returns
     |      -------
     |      columns : list of Array (for RecordBatch) or list of ChunkedArray (for Table)
     |
     |      Examples
     |      --------
     |      Table (works similarly for RecordBatch)
     |
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |      >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
     |      ...                    'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> table = pa.Table.from_pandas(df)
     |      >>> table.columns
     |      [<pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          null,
     |          4,
     |          5,
     |          null
     |        ]
     |      ], <pyarrow.lib.ChunkedArray object at ...>
     |      [
     |        [
     |          "Flamingo",
     |          "Horse",
     |          null,
     |          "Centipede"
     |        ]
     |      ]]
     |
     |  shape
     |      Dimensions of the table or record batch: (#rows, #columns).
     |
     |      Returns
     |      -------
     |      (int, int)
     |          Number of rows and number of columns.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> table = pa.table({'n_legs': [None, 4, 5, None],
     |      ...                   'animals': ["Flamingo", "Horse", None, "Centipede"]})
     |      >>> table.shape
     |      (4, 2)
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from _Tabular:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class TableGroupBy(builtins.object)
     |  TableGroupBy(table, keys, use_threads=True)
     |
     |  A grouping of columns in a table on which to perform aggregations.
     |
     |  Parameters
     |  ----------
     |  table : pyarrow.Table
     |      Input table to execute the aggregation on.
     |  keys : str or list[str]
     |      Name of the grouped columns.
     |  use_threads : bool, default True
     |      Whether to use multithreading or not. When set to True (the default),
     |      no stable ordering of the output is guaranteed.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> t = pa.table([
     |  ...       pa.array(["a", "a", "b", "b", "c"]),
     |  ...       pa.array([1, 2, 3, 4, 5]),
     |  ... ], names=["keys", "values"])
     |
     |  Grouping of columns:
     |
     |  >>> pa.TableGroupBy(t,"keys")
     |  <pyarrow.lib.TableGroupBy object at ...>
     |
     |  Perform aggregations:
     |
     |  >>> pa.TableGroupBy(t,"keys").aggregate([("values", "sum")])
     |  pyarrow.Table
     |  keys: string
     |  values_sum: int64
     |  ----
     |  keys: [["a","b","c"]]
     |  values_sum: [[3,7,5]]
     |
     |  Methods defined here:
     |
     |  __init__(self, table, keys, use_threads=True)
     |      TableGroupBy.__init__(self, table, keys, use_threads=True)
     |
     |  aggregate(self, aggregations)
     |      TableGroupBy.aggregate(self, aggregations)
     |
     |      Perform an aggregation over the grouped columns of the table.
     |
     |      Parameters
     |      ----------
     |      aggregations : list[tuple(str, str)] or list[tuple(str, str, FunctionOptions)]
     |          List of tuples, where each tuple is one aggregation specification
     |          and consists of: aggregation column name followed
     |          by function name and optionally aggregation function option.
     |          Pass empty list to get a single row for each group.
     |          The column name can be a string, an empty list or a list of
     |          column names, for unary, nullary and n-ary aggregation functions
     |          respectively.
     |
     |          For the list of function names and respective aggregation
     |          function options see :ref:`py-grouped-aggrs`.
     |
     |      Returns
     |      -------
     |      Table
     |          Results of the aggregation functions.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.table([
     |      ...       pa.array(["a", "a", "b", "b", "c"]),
     |      ...       pa.array([1, 2, 3, 4, 5]),
     |      ... ], names=["keys", "values"])
     |
     |      Sum the column "values" over the grouped column "keys":
     |
     |      >>> t.group_by("keys").aggregate([("values", "sum")])
     |      pyarrow.Table
     |      keys: string
     |      values_sum: int64
     |      ----
     |      keys: [["a","b","c"]]
     |      values_sum: [[3,7,5]]
     |
     |      Count the rows over the grouped column "keys":
     |
     |      >>> t.group_by("keys").aggregate([([], "count_all")])
     |      pyarrow.Table
     |      keys: string
     |      count_all: int64
     |      ----
     |      keys: [["a","b","c"]]
     |      count_all: [[2,2,1]]
     |
     |      Do multiple aggregations:
     |
     |      >>> t.group_by("keys").aggregate([
     |      ...    ("values", "sum"),
     |      ...    ("keys", "count")
     |      ... ])
     |      pyarrow.Table
     |      keys: string
     |      values_sum: int64
     |      keys_count: int64
     |      ----
     |      keys: [["a","b","c"]]
     |      values_sum: [[3,7,5]]
     |      keys_count: [[2,2,1]]
     |
     |      Count the number of non-null values for column "values"
     |      over the grouped column "keys":
     |
     |      >>> import pyarrow.compute as pc
     |      >>> t.group_by(["keys"]).aggregate([
     |      ...    ("values", "count", pc.CountOptions(mode="only_valid"))
     |      ... ])
     |      pyarrow.Table
     |      keys: string
     |      values_count: int64
     |      ----
     |      keys: [["a","b","c"]]
     |      values_count: [[2,2,1]]
     |
     |      Get a single row for each group in column "keys":
     |
     |      >>> t.group_by("keys").aggregate([])
     |      pyarrow.Table
     |      keys: string
     |      ----
     |      keys: [["a","b","c"]]
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  __dict__
     |      dictionary for instance variables
     |
     |  __weakref__
     |      list of weak references to the object

    class Tensor(_Weakrefable)
     |  Tensor()
     |
     |  A n-dimensional array a.k.a Tensor.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> import numpy as np
     |  >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |  >>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |  <pyarrow.Tensor>
     |  type: int32
     |  shape: (2, 3)
     |  strides: (12, 4)
     |
     |  Method resolution order:
     |      Tensor
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __buffer__(self, flags, /)
     |      Return a buffer object that exposes the underlying memory of the object.
     |
     |  __dlpack__(self, stream=None)
     |      Tensor.__dlpack__(self, stream=None)
     |
     |      Export a Tensor as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the tensor, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Tensor.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this tensor resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      Tensor.__reduce_cython__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __setstate__ = __setstate_cython__(...)
     |      Tensor.__setstate_cython__(self, __pyx_state)
     |
     |  dim_name(self, i)
     |      Tensor.dim_name(self, i)
     |
     |      Returns the name of the i-th tensor dimension.
     |
     |      Parameters
     |      ----------
     |      i : int
     |          The physical index of the tensor dimension.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |      >>> tensor.dim_name(0)
     |      'dim1'
     |      >>> tensor.dim_name(1)
     |      'dim2'
     |
     |  equals(self, other)
     |      Tensor.equals(self, Tensor other)
     |
     |      Return true if the tensors contains exactly equal data.
     |
     |      Parameters
     |      ----------
     |      other : Tensor
     |          The other tensor to compare for equality.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |      >>> y = np.array([[2, 2, 4], [4, 5, 10]], np.int32)
     |      >>> tensor2 = pa.Tensor.from_numpy(y, dim_names=["a","b"])
     |      >>> tensor.equals(tensor)
     |      True
     |      >>> tensor.equals(tensor2)
     |      False
     |
     |  to_numpy(self)
     |      Tensor.to_numpy(self)
     |
     |      Convert arrow::Tensor to numpy.ndarray with zero copy
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |      >>> tensor.to_numpy()
     |      array([[  2,   2,   4],
     |             [  4,   5, 100]], dtype=int32)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_numpy(obj, dim_names=None)
     |      Tensor.from_numpy(obj, dim_names=None)
     |
     |      Create a Tensor from a numpy array.
     |
     |      Parameters
     |      ----------
     |      obj : numpy.ndarray
     |          The source numpy array
     |      dim_names : list, optional
     |          Names of each dimension of the Tensor.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |      >>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |      <pyarrow.Tensor>
     |      type: int32
     |      shape: (2, 3)
     |      strides: (12, 4)
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  dim_names
     |      Names of this tensor dimensions.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |      >>> tensor.dim_names
     |      ['dim1', 'dim2']
     |
     |  is_contiguous
     |      Is this tensor contiguous in memory.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |      >>> tensor.is_contiguous
     |      True
     |
     |  is_mutable
     |      Is this tensor mutable or immutable.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |      >>> tensor.is_mutable
     |      True
     |
     |  ndim
     |      The dimension (n) of this tensor.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |      >>> tensor.ndim
     |      2
     |
     |  shape
     |      The shape of this tensor.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |      >>> tensor.shape
     |      (2, 3)
     |
     |  size
     |      The size of this tensor.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |      >>> tensor.size
     |      6
     |
     |  strides
     |      Strides of this tensor.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import numpy as np
     |      >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32)
     |      >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"])
     |      >>> tensor.strides
     |      (12, 4)
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __hash__ = None
     |
     |  __pyx_vtable__ = <capsule object NULL>

    class Time32Array(NumericArray)
     |  Concrete class for Arrow arrays of time32 data type.
     |
     |  Method resolution order:
     |      Time32Array
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Time32Scalar(Scalar)
     |  Concrete class for time32 scalars.
     |
     |  Method resolution order:
     |      Time32Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Time32Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python datetime.timedelta instance.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Time32Type(DataType)
     |  Concrete class for time32 data types.
     |
     |  Supported time unit resolutions are 's' [second]
     |  and 'ms' [millisecond].
     |
     |  Examples
     |  --------
     |  Create an instance of time32 type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.time32('ms')
     |  Time32Type(time32[ms])
     |
     |  Method resolution order:
     |      Time32Type
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  unit
     |      The time unit ('s' or 'ms').
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.time32('ms')
     |      >>> t.unit
     |      'ms'
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      DataType.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class Time64Array(NumericArray)
     |  Concrete class for Arrow arrays of time64 data type.
     |
     |  Method resolution order:
     |      Time64Array
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class Time64Scalar(Scalar)
     |  Concrete class for time64 scalars.
     |
     |  Method resolution order:
     |      Time64Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      Time64Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python datetime.timedelta instance.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class Time64Type(DataType)
     |  Concrete class for time64 data types.
     |
     |  Supported time unit resolutions are 'us' [microsecond]
     |  and 'ns' [nanosecond].
     |
     |  Examples
     |  --------
     |  Create an instance of time64 type:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.time64('us')
     |  Time64Type(time64[us])
     |
     |  Method resolution order:
     |      Time64Type
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  unit
     |      The time unit ('us' or 'ns').
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.time64('us')
     |      >>> t.unit
     |      'us'
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      DataType.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class TimestampArray(NumericArray)
     |  Concrete class for Arrow arrays of timestamp data type.
     |
     |  Method resolution order:
     |      TimestampArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class TimestampScalar(Scalar)
     |  Concrete class for timestamp scalars.
     |
     |  Method resolution order:
     |      TimestampScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __repr__(...)
     |      TimestampScalar.__repr__(self)
     |
     |      Return the representation of TimestampScalar using `strftime` to avoid
     |      original repr datetime values being out of range.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      TimestampScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Pandas Timestamp instance (if units are
     |      nanoseconds and pandas is available), otherwise as a Python
     |      datetime.datetime instance.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  value
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class TimestampType(DataType)
     |  Concrete class for timestamp data types.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |
     |  Create an instance of timestamp type:
     |
     |  >>> pa.timestamp('us')
     |  TimestampType(timestamp[us])
     |
     |  Create an instance of timestamp type with timezone:
     |
     |  >>> pa.timestamp('s', tz='UTC')
     |  TimestampType(timestamp[s, tz=UTC])
     |
     |  Method resolution order:
     |      TimestampType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __reduce__(self)
     |      TimestampType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  tz
     |      The timestamp time zone, if any, or None.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.timestamp('s', tz='UTC')
     |      >>> t.tz
     |      'UTC'
     |
     |  unit
     |      The timestamp unit ('s', 'ms', 'us' or 'ns').
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> t = pa.timestamp('us')
     |      >>> t.unit
     |      'us'
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class Transcoder(builtins.object)
     |  Transcoder(decoder, encoder)
     |
     |  Methods defined here:
     |
     |  __call__(self, buf)
     |      Transcoder.__call__(self, buf)
     |
     |  __init__(self, decoder, encoder)
     |      Transcoder.__init__(self, decoder, encoder)
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  __dict__
     |      dictionary for instance variables
     |
     |  __weakref__
     |      list of weak references to the object

    class TransformInputStream(NativeFile)
     |  TransformInputStream(NativeFile stream, transform_func)
     |
     |  Transform an input stream.
     |
     |  Parameters
     |  ----------
     |  stream : NativeFile
     |      The stream to transform.
     |  transform_func : callable
     |      The transformation to apply.
     |
     |  Method resolution order:
     |      TransformInputStream
     |      NativeFile
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce__ = __reduce_cython__(...)
     |      TransformInputStream.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      TransformInputStream.__setstate_cython__(self, __pyx_state)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from NativeFile:
     |
     |  __enter__(self)
     |      NativeFile.__enter__(self)
     |
     |  __exit__(self, exc_type, exc_value, tb)
     |      NativeFile.__exit__(self, exc_type, exc_value, tb)
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __next__(...)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  close(self)
     |      NativeFile.close(self)
     |
     |  download(self, stream_or_path, buffer_size=None)
     |      NativeFile.download(self, stream_or_path, buffer_size=None)
     |
     |      Read this file completely to a local path or destination stream.
     |
     |      This method first seeks to the beginning of the file.
     |
     |      Parameters
     |      ----------
     |      stream_or_path : str or file-like object
     |          If a string, a local file path to write to; otherwise,
     |          should be a writable stream.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  fileno(self)
     |      NativeFile.fileno(self)
     |
     |      NOT IMPLEMENTED
     |
     |  flush(self)
     |      NativeFile.flush(self)
     |
     |      Flush the stream, if applicable.
     |
     |      An error is raised if stream is not writable.
     |
     |  get_stream(self, file_offset, nbytes)
     |      NativeFile.get_stream(self, file_offset, nbytes)
     |
     |      Return an input stream that reads a file segment independent of the
     |      state of the file.
     |
     |      Allows reading portions of a random access file as an input stream
     |      without interfering with each other.
     |
     |      Parameters
     |      ----------
     |      file_offset : int
     |      nbytes : int
     |
     |      Returns
     |      -------
     |      stream : NativeFile
     |
     |  isatty(self)
     |      NativeFile.isatty(self)
     |
     |  metadata(self)
     |      NativeFile.metadata(self)
     |
     |      Return file metadata
     |
     |  read(self, nbytes=None)
     |      NativeFile.read(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      If *nbytes* is None, then the entire remaining file contents are read.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read1(self, nbytes=None)
     |      NativeFile.read1(self, nbytes=None)
     |
     |      Read and return up to n bytes.
     |
     |      Unlike read(), if *nbytes* is None then a chunk is read, not the
     |      entire file.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, default None
     |          The maximum number of bytes to read.
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_at(self, nbytes, offset)
     |      NativeFile.read_at(self, nbytes, offset)
     |
     |      Read indicated number of bytes at offset from the file
     |
     |      Parameters
     |      ----------
     |      nbytes : int
     |      offset : int
     |
     |      Returns
     |      -------
     |      data : bytes
     |
     |  read_buffer(self, nbytes=None)
     |      NativeFile.read_buffer(self, nbytes=None)
     |
     |      Read from buffer.
     |
     |      Parameters
     |      ----------
     |      nbytes : int, optional
     |          maximum number of bytes read
     |
     |  readable(self)
     |      NativeFile.readable(self)
     |
     |  readall(self)
     |      NativeFile.readall(self)
     |
     |  readinto(self, b)
     |      NativeFile.readinto(self, b)
     |
     |      Read into the supplied buffer
     |
     |      Parameters
     |      ----------
     |      b : buffer-like object
     |          A writable buffer object (such as a bytearray).
     |
     |      Returns
     |      -------
     |      written : int
     |          number of bytes written
     |
     |  readline(self, size=None)
     |      NativeFile.readline(self, size=None)
     |
     |      NOT IMPLEMENTED. Read and return a line of bytes from the file.
     |
     |      If size is specified, read at most size bytes.
     |
     |      Line terminator is always b"\n".
     |
     |      Parameters
     |      ----------
     |      size : int
     |          maximum number of bytes read
     |
     |  readlines(self, hint=None)
     |      NativeFile.readlines(self, hint=None)
     |
     |      NOT IMPLEMENTED. Read lines of the file
     |
     |      Parameters
     |      ----------
     |      hint : int
     |          maximum number of bytes read until we stop
     |
     |  seek(self, position, whence=0)
     |      NativeFile.seek(self, int64_t position, int whence=0)
     |
     |      Change current file stream position
     |
     |      Parameters
     |      ----------
     |      position : int
     |          Byte offset, interpreted relative to value of whence argument
     |      whence : int, default 0
     |          Point of reference for seek offset
     |
     |      Notes
     |      -----
     |      Values of whence:
     |      * 0 -- start of stream (the default); offset should be zero or positive
     |      * 1 -- current stream position; offset may be negative
     |      * 2 -- end of stream; offset is usually negative
     |
     |      Returns
     |      -------
     |      int
     |          The new absolute stream position.
     |
     |  seekable(self)
     |      NativeFile.seekable(self)
     |
     |  size(self)
     |      NativeFile.size(self)
     |
     |      Return file size
     |
     |  tell(self)
     |      NativeFile.tell(self)
     |
     |      Return current stream position
     |
     |  truncate(self)
     |      NativeFile.truncate(self)
     |
     |      NOT IMPLEMENTED
     |
     |  upload(self, stream, buffer_size=None)
     |      NativeFile.upload(self, stream, buffer_size=None)
     |
     |      Write from a source stream to this file.
     |
     |      Parameters
     |      ----------
     |      stream : file-like object
     |          Source stream to pipe to this file.
     |      buffer_size : int, optional
     |          The buffer size to use for data transfers.
     |
     |  writable(self)
     |      NativeFile.writable(self)
     |
     |  write(self, data)
     |      NativeFile.write(self, data)
     |
     |      Write data to the file.
     |
     |      Parameters
     |      ----------
     |      data : bytes-like object or exporter of buffer protocol
     |
     |      Returns
     |      -------
     |      int
     |          nbytes: number of bytes written
     |
     |  writelines(self, lines)
     |      NativeFile.writelines(self, lines)
     |
     |      Write lines to the file.
     |
     |      Parameters
     |      ----------
     |      lines : iterable
     |          Iterable of bytes-like objects or exporters of buffer protocol
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from NativeFile:
     |
     |  closed
     |
     |  mode
     |      The file mode. Currently instances of NativeFile may support:
     |
     |      * rb: binary read
     |      * wb: binary write
     |      * rb+: binary read and write
     |      * ab: binary append

    class UInt16Array(IntegerArray)
     |  Concrete class for Arrow arrays of uint16 data type.
     |
     |  Method resolution order:
     |      UInt16Array
     |      IntegerArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class UInt16Scalar(Scalar)
     |  Concrete class for uint16 scalars.
     |
     |  Method resolution order:
     |      UInt16Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      UInt16Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python int.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class UInt32Array(IntegerArray)
     |  Concrete class for Arrow arrays of uint32 data type.
     |
     |  Method resolution order:
     |      UInt32Array
     |      IntegerArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class UInt32Scalar(Scalar)
     |  Concrete class for uint32 scalars.
     |
     |  Method resolution order:
     |      UInt32Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      UInt32Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python int.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class UInt64Array(IntegerArray)
     |  Concrete class for Arrow arrays of uint64 data type.
     |
     |  Method resolution order:
     |      UInt64Array
     |      IntegerArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class UInt64Scalar(Scalar)
     |  Concrete class for uint64 scalars.
     |
     |  Method resolution order:
     |      UInt64Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      UInt64Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python int.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class UInt8Array(IntegerArray)
     |  Concrete class for Arrow arrays of uint8 data type.
     |
     |  Method resolution order:
     |      UInt8Array
     |      IntegerArray
     |      NumericArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class UInt8Scalar(Scalar)
     |  Concrete class for uint8 scalars.
     |
     |  Method resolution order:
     |      UInt8Scalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      UInt8Scalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this value as a Python int.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class UnionArray(Array)
     |  Concrete class for Arrow arrays of a Union data type.
     |
     |  Method resolution order:
     |      UnionArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  child(self, pos)
     |      UnionArray.child(self, int pos)
     |
     |      DEPRECATED, use field() instead.
     |
     |      Parameters
     |      ----------
     |      pos : int
     |          The physical index of the union child field (not its type code).
     |
     |      Returns
     |      -------
     |      field : pyarrow.Field
     |          The given child field.
     |
     |  field(self, pos)
     |      UnionArray.field(self, int pos)
     |
     |      Return the given child field as an individual array.
     |
     |      For sparse unions, the returned array has its offset, length,
     |      and null count adjusted.
     |
     |      For dense unions, the returned array is unchanged.
     |
     |      Parameters
     |      ----------
     |      pos : int
     |          The physical index of the union child field (not its type code).
     |
     |      Returns
     |      -------
     |      field : Array
     |          The given child field.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_dense(types, value_offsets, children, field_names=None, type_codes=None)
     |      UnionArray.from_dense(Array types, Array value_offsets, list children, list field_names=None, list type_codes=None)
     |
     |      Construct dense UnionArray from arrays of int8 types, int32 offsets and
     |      children arrays
     |
     |      Parameters
     |      ----------
     |      types : Array (int8 type)
     |      value_offsets : Array (int32 type)
     |      children : list
     |      field_names : list
     |      type_codes : list
     |
     |      Returns
     |      -------
     |      union_array : UnionArray
     |
     |  from_sparse(types, children, field_names=None, type_codes=None)
     |      UnionArray.from_sparse(Array types, list children, list field_names=None, list type_codes=None)
     |
     |      Construct sparse UnionArray from arrays of int8 types and children
     |      arrays
     |
     |      Parameters
     |      ----------
     |      types : Array (int8 type)
     |      children : list
     |      field_names : list
     |      type_codes : list
     |
     |      Returns
     |      -------
     |      union_array : UnionArray
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  offsets
     |      Get the value offsets array (dense arrays only).
     |
     |      Does not account for any slice offset.
     |
     |  type_codes
     |      Get the type codes array.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class UnionScalar(Scalar)
     |  Concrete class for Union scalars.
     |
     |  Method resolution order:
     |      UnionScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      UnionScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return underlying value as a Python object.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  type_code
     |      Return the union type code for this scalar.
     |
     |  value
     |      Return underlying value as a scalar.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class UnionType(DataType)
     |  Base class for union data types.
     |
     |  Examples
     |  --------
     |  Create an instance of a dense UnionType using ``pa.union``:
     |
     |  >>> import pyarrow as pa
     |  >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())],
     |  ...          mode=pa.lib.UnionMode_DENSE),
     |  (DenseUnionType(dense_union<a: fixed_size_binary[10]=0, b: string=1>),)
     |
     |  Create an instance of a dense UnionType using ``pa.dense_union``:
     |
     |  >>> pa.dense_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |  DenseUnionType(dense_union<a: fixed_size_binary[10]=0, b: string=1>)
     |
     |  Create an instance of a sparse UnionType using ``pa.union``:
     |
     |  >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())],
     |  ...          mode=pa.lib.UnionMode_SPARSE),
     |  (SparseUnionType(sparse_union<a: fixed_size_binary[10]=0, b: string=1>),)
     |
     |  Create an instance of a sparse UnionType using ``pa.sparse_union``:
     |
     |  >>> pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |  SparseUnionType(sparse_union<a: fixed_size_binary[10]=0, b: string=1>)
     |
     |  Method resolution order:
     |      UnionType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __getitem__(...)
     |      UnionType.__getitem__(self, i)
     |
     |      Return a child field by its index.
     |
     |      Alias of ``field``.
     |
     |  __iter__(...)
     |      UnionType.__iter__(self)
     |
     |      Iterate over union members, in order.
     |
     |  __len__(...)
     |      UnionType.__len__(self)
     |
     |      Like num_fields().
     |
     |  __reduce__(self)
     |      UnionType.__reduce__(self)
     |
     |  field(self, i)
     |      UnionType.field(self, i) -> Field
     |
     |      Return a child field by its numeric index.
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |      >>> union[0]
     |      pyarrow.Field<a: fixed_size_binary[10]>
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  mode
     |      The mode of the union ("dense" or "sparse").
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |      >>> union.mode
     |      'sparse'
     |
     |  type_codes
     |      The type code to indicate each data type in this union.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())])
     |      >>> union.type_codes
     |      [0, 1]
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  bit_width
     |      Bit width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().bit_width
     |      64
     |
     |  byte_width
     |      Byte width for fixed width type.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().byte_width
     |      8
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class UnknownExtensionType(ExtensionType)
     |  UnknownExtensionType(DataType storage_type, serialized)
     |
     |  A concrete class for Python-defined extension types that refer to
     |  an unknown Python implementation.
     |
     |  Parameters
     |  ----------
     |  storage_type : DataType
     |      The storage type for which the extension is built.
     |  serialized : bytes
     |      The serialised output.
     |
     |  Method resolution order:
     |      UnknownExtensionType
     |      ExtensionType
     |      BaseExtensionType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_ext_serialize__(self)
     |      UnknownExtensionType.__arrow_ext_serialize__(self)
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Class methods defined here:
     |
     |  __arrow_ext_deserialize__(storage_type, serialized)
     |      UnknownExtensionType.__arrow_ext_deserialize__(cls, storage_type, serialized)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from ExtensionType:
     |
     |  __arrow_ext_class__(self)
     |      ExtensionType.__arrow_ext_class__(self)
     |
     |      Return an extension array class to be used for building or
     |      deserializing arrays with this extension type.
     |
     |      This method should return a subclass of the ExtensionArray class. By
     |      default, if not specialized in the extension implementation, an
     |      extension type array will be a built-in ExtensionArray instance.
     |
     |  __arrow_ext_scalar_class__(self)
     |      ExtensionType.__arrow_ext_scalar_class__(self)
     |
     |      Return an extension scalar class for building scalars with this
     |      extension type.
     |
     |      This method should return subclass of the ExtensionScalar class. By
     |      default, if not specialized in the extension implementation, an
     |      extension type scalar will be a built-in ExtensionScalar instance.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __reduce__(self)
     |      ExtensionType.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from ExtensionType:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseExtensionType:
     |
     |  wrap_array(self, storage)
     |      BaseExtensionType.wrap_array(self, storage)
     |
     |      Wrap the given storage array as an extension array.
     |
     |      Parameters
     |      ----------
     |      storage : Array or ChunkedArray
     |
     |      Returns
     |      -------
     |      array : Array or ChunkedArray
     |          Extension array wrapping the storage array
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from BaseExtensionType:
     |
     |  bit_width
     |      The bit width of the extension type.
     |
     |  byte_width
     |      The byte width of the extension type.
     |
     |  extension_name
     |      The extension type name.
     |
     |  storage_type
     |      The underlying storage type.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class UuidArray(ExtensionArray)
     |  Concrete class for Arrow arrays of UUID data type.
     |
     |  Method resolution order:
     |      UuidArray
     |      ExtensionArray
     |      Array
     |      _PandasConvertible
     |      _Weakrefable
     |      builtins.object
     |
     |  Data descriptors defined here:
     |
     |  __dict__
     |      dictionary for instance variables
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from ExtensionArray:
     |
     |  __new__(*args, **kwargs) class method of ExtensionArray
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_storage(typ, storage)
     |      ExtensionArray.from_storage(BaseExtensionType typ, Array storage)
     |
     |      Construct ExtensionArray from type and storage array.
     |
     |      Parameters
     |      ----------
     |      typ : DataType
     |          The extension type for the result array.
     |      storage : Array
     |          The underlying storage for the result array.
     |
     |      Returns
     |      -------
     |      ext_array : ExtensionArray
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ExtensionArray:
     |
     |  storage
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from ExtensionArray:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Array:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __array__(self, dtype=None, copy=None)
     |      Array.__array__(self, dtype=None, copy=None)
     |
     |  __arrow_c_array__(self, requested_schema=None)
     |      Array.__arrow_c_array__(self, requested_schema=None)
     |
     |      Get a pair of PyCapsules containing a C ArrowArray representation of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
     |          respectively.
     |
     |  __arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |      Array.__arrow_c_device_array__(self, requested_schema=None, **kwargs)
     |
     |      Get a pair of PyCapsules containing a C ArrowDeviceArray representation
     |      of the object.
     |
     |      Parameters
     |      ----------
     |      requested_schema : PyCapsule | None
     |          A PyCapsule containing a C ArrowSchema representation of a requested
     |          schema. PyArrow will attempt to cast the array to this data type.
     |          If None, the array will be returned as-is, with a type matching the
     |          one returned by :meth:`__arrow_c_schema__()`.
     |      kwargs
     |          Currently no additional keyword arguments are supported, but
     |          this method will accept any keyword with a value of ``None``
     |          for compatibility with future keywords.
     |
     |      Returns
     |      -------
     |      Tuple[PyCapsule, PyCapsule]
     |          A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
     |          respectively.
     |
     |  __dlpack__(self, stream=None)
     |      Array.__dlpack__(self, stream=None)
     |
     |      Export a primitive array as a DLPack capsule.
     |
     |      Parameters
     |      ----------
     |      stream : int, optional
     |          A Python integer representing a pointer to a stream. Currently not supported.
     |          Stream is provided by the consumer to the producer to instruct the producer
     |          to ensure that operations can safely be performed on the array.
     |
     |      Returns
     |      -------
     |      capsule : PyCapsule
     |          A DLPack capsule for the array, pointing to a DLManagedTensor.
     |
     |  __dlpack_device__(self)
     |      Array.__dlpack_device__(self)
     |
     |      Return the DLPack device tuple this arrays resides on.
     |
     |      Returns
     |      -------
     |      tuple : Tuple[int, int]
     |          Tuple with index specifying the type of the device (where
     |          CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the
     |          device which is 0 by default for CPU.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getitem__(...)
     |      Array.__getitem__(self, key)
     |
     |      Slice or return value at given index
     |
     |      Parameters
     |      ----------
     |      key : integer or slice
     |          Slices with step not equal to 1 (or None) will produce a copy
     |          rather than a zero-copy view
     |
     |      Returns
     |      -------
     |      value : Scalar (index) or Array (slice)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Array.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self)
     |      Array.__sizeof__(self)
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  buffers(self)
     |      Array.buffers(self)
     |
     |      Return a list of Buffer objects pointing to this array's physical
     |      storage.
     |
     |      To correctly interpret these buffers, you need to also apply the offset
     |      multiplied with the size of the stored data type.
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast array values to another data type
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast array to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      cast : Array
     |
     |  copy_to(self, destination)
     |      Array.copy_to(self, destination)
     |
     |      Construct a copy of the array with all buffers on destination
     |      device.
     |
     |      This method recursively copies the array's buffers and those of its
     |      children onto the destination MemoryManager device and returns the
     |      new Array.
     |
     |      Parameters
     |      ----------
     |      destination : pyarrow.MemoryManager or pyarrow.Device
     |          The destination device to copy the array to.
     |
     |      Returns
     |      -------
     |      Array
     |
     |  dictionary_encode(self, null_encoding='mask')
     |      Array.dictionary_encode(self, null_encoding='mask')
     |
     |      Compute dictionary-encoded representation of array.
     |
     |      See :func:`pyarrow.compute.dictionary_encode` for full usage.
     |
     |      Parameters
     |      ----------
     |      null_encoding : str, default "mask"
     |          How to handle null entries.
     |
     |      Returns
     |      -------
     |      encoded : DictionaryArray
     |          A dictionary-encoded version of this array.
     |
     |  diff(self, other)
     |      Array.diff(self, Array other)
     |
     |      Compare contents of this array against another one.
     |
     |      Return a string containing the result of diffing this array
     |      (on the left side) against the other array (on the right side).
     |
     |      Parameters
     |      ----------
     |      other : Array
     |          The other array to compare this array with.
     |
     |      Returns
     |      -------
     |      diff : str
     |          A human-readable printout of the differences.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> left = pa.array(["one", "two", "three"])
     |      >>> right = pa.array(["two", None, "two-and-a-half", "three"])
     |      >>> print(left.diff(right)) # doctest: +SKIP
     |
     |      @@ -0, +0 @@
     |      -"one"
     |      @@ -2, +1 @@
     |      +null
     |      +"two-and-a-half"
     |
     |  drop_null(self)
     |      Array.drop_null(self)
     |
     |      Remove missing values from an array.
     |
     |  equals(self, other)
     |      Array.equals(self, Array other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Array
     |
     |      Returns
     |      -------
     |      bool
     |
     |  fill_null(self, fill_value)
     |      Array.fill_null(self, fill_value)
     |
     |      See :func:`pyarrow.compute.fill_null` for usage.
     |
     |      Parameters
     |      ----------
     |      fill_value : any
     |          The replacement value for null entries.
     |
     |      Returns
     |      -------
     |      result : Array
     |          A new array with nulls replaced by the given value.
     |
     |  filter(self, mask, *, null_selection_behavior='drop')
     |      Array.filter(self, mask, *, null_selection_behavior='drop')
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.filter` for full usage.
     |
     |      Parameters
     |      ----------
     |      mask : Array or array-like
     |          The boolean mask to filter the array with.
     |      null_selection_behavior : str, default "drop"
     |          How nulls in the mask should be handled.
     |
     |      Returns
     |      -------
     |      filtered : Array
     |          An array of the same type, with only the elements selected by
     |          the boolean mask.
     |
     |  get_total_buffer_size(self)
     |      Array.get_total_buffer_size(self)
     |
     |      The sum of bytes in each buffer referenced by the array.
     |
     |      An array may only reference a portion of a buffer.
     |      This method will overestimate in this case and return the
     |      byte size of the entire buffer.
     |
     |      If a buffer is referenced multiple times then it will
     |      only be counted once.
     |
     |  index(self, value, start=None, end=None, *, memory_pool=None)
     |      Array.index(self, value, start=None, end=None, *, memory_pool=None)
     |
     |      Find the first index of a value.
     |
     |      See :func:`pyarrow.compute.index` for full usage.
     |
     |      Parameters
     |      ----------
     |      value : Scalar or object
     |          The value to look for in the array.
     |      start : int, optional
     |          The start index where to look for `value`.
     |      end : int, optional
     |          The end index where to look for `value`.
     |      memory_pool : MemoryPool, optional
     |          A memory pool for potential memory allocations.
     |
     |      Returns
     |      -------
     |      index : Int64Scalar
     |          The index of the value in the array (-1 if not found).
     |
     |  is_nan(self)
     |      Array.is_nan(self)
     |
     |      Return BooleanArray indicating the NaN values.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_null(self, *, nan_is_null=False)
     |      Array.is_null(self, *, nan_is_null=False)
     |
     |      Return BooleanArray indicating the null values.
     |
     |      Parameters
     |      ----------
     |      nan_is_null : bool (optional, default False)
     |          Whether floating-point NaN values should also be considered null.
     |
     |      Returns
     |      -------
     |      array : boolean Array
     |
     |  is_valid(self)
     |      Array.is_valid(self)
     |
     |      Return BooleanArray indicating the non-null values.
     |
     |  slice(self, offset=0, length=None)
     |      Array.slice(self, offset=0, length=None)
     |
     |      Compute zero-copy slice of this array.
     |
     |      Parameters
     |      ----------
     |      offset : int, default 0
     |          Offset from start of array to slice.
     |      length : int, default None
     |          Length of slice (default is until end of Array starting from
     |          offset).
     |
     |      Returns
     |      -------
     |      sliced : Array
     |          An array with the same datatype, containing the sliced values.
     |
     |  sort(self, order='ascending', **kwargs)
     |      Array.sort(self, order='ascending', **kwargs)
     |
     |      Sort the Array
     |
     |      Parameters
     |      ----------
     |      order : str, default "ascending"
     |          Which order to sort values in.
     |          Accepted values are "ascending", "descending".
     |      **kwargs : dict, optional
     |          Additional sorting options.
     |          As allowed by :class:`SortOptions`
     |
     |      Returns
     |      -------
     |      result : Array
     |
     |  sum(self, **kwargs)
     |      Array.sum(self, **kwargs)
     |
     |      Sum the values in a numerical array.
     |
     |      See :func:`pyarrow.compute.sum` for full usage.
     |
     |      Parameters
     |      ----------
     |      **kwargs : dict, optional
     |          Options to pass to :func:`pyarrow.compute.sum`.
     |
     |      Returns
     |      -------
     |      sum : Scalar
     |          A scalar containing the sum value.
     |
     |  take(self, indices)
     |      Array.take(self, indices)
     |
     |      Select values from an array.
     |
     |      See :func:`pyarrow.compute.take` for full usage.
     |
     |      Parameters
     |      ----------
     |      indices : Array or array-like
     |          The indices in the array whose values will be returned.
     |
     |      Returns
     |      -------
     |      taken : Array
     |          An array with the same datatype, containing the taken values.
     |
     |  to_numpy(self, zero_copy_only=True, writable=False)
     |      Array.to_numpy(self, zero_copy_only=True, writable=False)
     |
     |      Return a NumPy view or copy of this array.
     |
     |      By default, tries to return a view of this array. This is only
     |      supported for primitive arrays with the same memory layout as NumPy
     |      (i.e. integers, floating point, ..) and without any nulls.
     |
     |      For the extension arrays, this method simply delegates to the
     |      underlying storage array.
     |
     |      Parameters
     |      ----------
     |      zero_copy_only : bool, default True
     |          If True, an exception will be raised if the conversion to a numpy
     |          array would require copying the underlying data (e.g. in presence
     |          of nulls, or for non-primitive types).
     |      writable : bool, default False
     |          For numpy arrays created with zero copy (view on the Arrow data),
     |          the resulting array is not writable (Arrow data is immutable).
     |          By setting this to True, a copy of the array is made to ensure
     |          it is writable.
     |
     |      Returns
     |      -------
     |      array : numpy.ndarray
     |
     |  to_pylist(self, *, maps_as_pydicts=None)
     |      Array.to_pylist(self, *, maps_as_pydicts=None)
     |
     |      Convert to a list of native Python objects.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |
     |          If 'lossy', whenever duplicate keys are detected, a warning will be printed.
     |          The last seen value of a duplicate key will be in the Python dictionary.
     |          If 'strict', this instead results in an exception being raised when detected.
     |
     |      Returns
     |      -------
     |      lst : list
     |
     |  to_string(self, *, indent=2, top_level_indent=0, window=10, container_window=2, skip_new_lines=False, element_size_limit=100)
     |      Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False, int element_size_limit=100)
     |
     |      Render a "pretty-printed" string representation of the Array.
     |
     |      Note: for data on a non-CPU device, the full array is copied to CPU
     |      memory.
     |
     |      Parameters
     |      ----------
     |      indent : int, default 2
     |          How much to indent the internal items in the string to
     |          the right, by default ``2``.
     |      top_level_indent : int, default 0
     |          How much to indent right the entire content of the array,
     |          by default ``0``.
     |      window : int
     |          How many primitive items to preview at the begin and end
     |          of the array when the array is bigger than the window.
     |          The other items will be ellipsed.
     |      container_window : int
     |          How many container items (such as a list in a list array)
     |          to preview at the begin and end of the array when the array
     |          is bigger than the window.
     |      skip_new_lines : bool
     |          If the array should be rendered as a single line of text
     |          or if each element should be on its own line.
     |      element_size_limit : int, default 100
     |          Maximum number of characters of a single element before it is truncated.
     |
     |  tolist(self)
     |      Array.tolist(self)
     |
     |      Alias of to_pylist for compatibility with NumPy.
     |
     |  unique(self)
     |      Array.unique(self)
     |
     |      Compute distinct elements in array.
     |
     |      Returns
     |      -------
     |      unique : Array
     |          An array of the same data type, with deduplicated elements.
     |
     |  validate(self, *, full=False)
     |      Array.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  value_counts(self)
     |      Array.value_counts(self)
     |
     |      Compute counts of unique elements in array.
     |
     |      Returns
     |      -------
     |      StructArray
     |          An array of  <input type "Values", int64 "Counts"> structs
     |
     |  view(self, target_type)
     |      Array.view(self, target_type)
     |
     |      Return zero-copy "view" of array as another data type.
     |
     |      The data types must have compatible columnar buffer layouts
     |
     |      Parameters
     |      ----------
     |      target_type : DataType
     |          Type to construct view as.
     |
     |      Returns
     |      -------
     |      view : Array
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from Array:
     |
     |  from_buffers(type, length, buffers, null_count=-1, offset=0, children=None)
     |      Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)
     |
     |      Construct an Array from a sequence of buffers.
     |
     |      The concrete type returned depends on the datatype.
     |
     |      Parameters
     |      ----------
     |      type : DataType
     |          The value type of the array.
     |      length : int
     |          The number of values in the array.
     |      buffers : List[Buffer | None]
     |          The buffers backing this array.
     |      null_count : int, default -1
     |          The number of null entries in the array. Negative value means that
     |          the null count is not known.
     |      offset : int, default 0
     |          The array's logical offset (in values, not in bytes) from the
     |          start of each buffer.
     |      children : List[Array], default None
     |          Nested type children with length matching type.num_fields.
     |
     |      Returns
     |      -------
     |      array : Array
     |
     |  from_pandas(obj, mask=None, type=None, safe=True, memory_pool=None)
     |      Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)
     |
     |      Convert pandas.Series to an Arrow Array.
     |
     |      This method uses Pandas semantics about what values indicate
     |      nulls. See pyarrow.array for more general conversion from arrays or
     |      sequences to Arrow arrays.
     |
     |      Parameters
     |      ----------
     |      obj : ndarray, pandas.Series, array-like
     |      mask : array (boolean), optional
     |          Indicate which values are null (True) or not null (False).
     |      type : pyarrow.DataType
     |          Explicit type to attempt to coerce to, otherwise will be inferred
     |          from the data.
     |      safe : bool, default True
     |          Check for overflows or other unsafe conversions.
     |      memory_pool : pyarrow.MemoryPool, optional
     |          If not passed, will allocate memory from the currently-set default
     |          memory pool.
     |
     |      Notes
     |      -----
     |      Localized timestamps will currently be returned as UTC (pandas's native
     |      representation). Timezone-naive data will be implicitly interpreted as
     |      UTC.
     |
     |      Returns
     |      -------
     |      array : pyarrow.Array or pyarrow.ChunkedArray
     |          ChunkedArray is returned if object data overflows binary buffer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Array:
     |
     |  device_type
     |      The device type where the array resides.
     |
     |      Returns
     |      -------
     |      DeviceAllocationType
     |
     |  is_cpu
     |      Whether the array is CPU-accessible.
     |
     |  nbytes
     |      Total number of bytes consumed by the elements of the array.
     |
     |      In other words, the sum of bytes from all buffer
     |      ranges referenced.
     |
     |      Unlike `get_total_buffer_size` this method will account for array
     |      offsets.
     |
     |      If buffers are shared between arrays then the shared
     |      portion will be counted multiple times.
     |
     |      The dictionary of dictionary arrays will always be counted in their
     |      entirety even if the array only references a portion of the dictionary.
     |
     |  null_count
     |
     |  offset
     |      A relative position into another array's data.
     |
     |      The purpose is to enable zero-copy slicing. This value defaults to zero
     |      but must be applied on all operations with the physical storage
     |      buffers.
     |
     |  statistics
     |      Statistics of the array.
     |
     |  type
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from Array:
     |
     |  __hash__ = None
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from _PandasConvertible:
     |
     |  __reduce_cython__(self)
     |      _PandasConvertible.__reduce_cython__(self)
     |
     |  __setstate__ = __setstate_cython__(...)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  __setstate_cython__(self, __pyx_state)
     |      _PandasConvertible.__setstate_cython__(self, __pyx_state)
     |
     |  to_pandas(self, memory_pool=None, categories=None, strings_to_categorical=False, zero_copy_only=False, integer_object_nulls=False, date_as_object=True, timestamp_as_object=False, use_threads=True, deduplicate_objects=True, ignore_metadata=False, safe=True, split_blocks=False, self_destruct=False, maps_as_pydicts=None, types_mapper=None, coerce_temporal_nanoseconds=False)
     |      _PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, str maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)
     |
     |      Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
     |
     |      Parameters
     |      ----------
     |      memory_pool : MemoryPool, default None
     |          Arrow MemoryPool to use for allocations. Uses the default memory
     |          pool if not passed.
     |      categories : list, default empty
     |          List of fields that should be returned as pandas.Categorical. Only
     |          applies to table-like data structures.
     |      strings_to_categorical : bool, default False
     |          Encode string (UTF8) and binary types to pandas.Categorical.
     |      zero_copy_only : bool, default False
     |          Raise an ArrowException if this function call would require copying
     |          the underlying data.
     |      integer_object_nulls : bool, default False
     |          Cast integers with nulls to objects
     |      date_as_object : bool, default True
     |          Cast dates to objects. If False, convert to datetime64 dtype with
     |          the equivalent time unit (if supported). Note: in pandas version
     |          < 2.0, only datetime64[ns] conversion is supported.
     |      timestamp_as_object : bool, default False
     |          Cast non-nanosecond timestamps (np.datetime64) to objects. This is
     |          useful in pandas version 1.x if you have timestamps that don't fit
     |          in the normal date range of nanosecond timestamps (1678 CE-2262 CE).
     |          Non-nanosecond timestamps are supported in pandas version 2.0.
     |          If False, all timestamps are converted to datetime64 dtype.
     |      use_threads : bool, default True
     |          Whether to parallelize the conversion using multiple threads.
     |      deduplicate_objects : bool, default True
     |          Do not create multiple copies Python objects when created, to save
     |          on memory use. Conversion will be slower.
     |      ignore_metadata : bool, default False
     |          If True, do not use the 'pandas' metadata to reconstruct the
     |          DataFrame index, if present
     |      safe : bool, default True
     |          For certain data types, a cast is needed in order to store the
     |          data in a pandas DataFrame or Series (e.g. timestamps are always
     |          stored as nanoseconds in pandas). This option controls whether it
     |          is a safe cast or not.
     |      split_blocks : bool, default False
     |          If True, generate one internal "block" for each column when
     |          creating a pandas.DataFrame from a RecordBatch or Table. While this
     |          can temporarily reduce memory note that various pandas operations
     |          can trigger "consolidation" which may balloon memory use.
     |      self_destruct : bool, default False
     |          EXPERIMENTAL: If True, attempt to deallocate the originating Arrow
     |          memory while converting the Arrow object to pandas. If you use the
     |          object after calling to_pandas with this option it will crash your
     |          program.
     |
     |          Note that you may not see always memory usage improvements. For
     |          example, if multiple columns share an underlying allocation,
     |          memory can't be freed until all columns are converted.
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          The default behavior (`None`), is to convert Arrow Map arrays to
     |          Python association lists (list-of-tuples) in the same order as the
     |          Arrow Map, as in [(key1, value1), (key2, value2), ...].
     |
     |          If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
     |          This can change the ordering of (key, value) pairs, and will
     |          deduplicate multiple keys, resulting in a possible loss of data.
     |
     |          If 'lossy', this key deduplication results in a warning printed
     |          when detected. If 'strict', this instead results in an exception
     |          being raised when detected.
     |      types_mapper : function, default None
     |          A function mapping a pyarrow DataType to a pandas ExtensionDtype.
     |          This can be used to override the default pandas type for conversion
     |          of built-in pyarrow types or in absence of pandas_metadata in the
     |          Table schema. The function receives a pyarrow DataType and is
     |          expected to return a pandas ExtensionDtype or ``None`` if the
     |          default conversion should be used for that type. If you have
     |          a dictionary mapping, you can pass ``dict.get`` as function.
     |      coerce_temporal_nanoseconds : bool, default False
     |          Only applicable to pandas version >= 2.0.
     |          A legacy option to coerce date32, date64, duration, and timestamp
     |          time units to nanoseconds when converting to pandas. This is the
     |          default behavior in pandas version 1.x. Set this option to True if
     |          you'd like to use this coercion when using pandas version >= 2.0
     |          for backwards compatibility (not recommended otherwise).
     |
     |      Returns
     |      -------
     |      pandas.Series or pandas.DataFrame depending on type of object
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> import pandas as pd
     |
     |      Convert a Table to pandas DataFrame:
     |
     |      >>> table = pa.table([
     |      ...    pa.array([2, 4, 5, 100]),
     |      ...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      ...    ], names=['n_legs', 'animals'])
     |      >>> table.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(table.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a RecordBatch to pandas DataFrame:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.array([2, 4, 5, 100])
     |      >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
     |      >>> batch = pa.record_batch([n_legs, animals],
     |      ...                         names=["n_legs", "animals"])
     |      >>> batch
     |      pyarrow.RecordBatch
     |      n_legs: int64
     |      animals: string
     |      ----
     |      n_legs: [2,4,5,100]
     |      animals: ["Flamingo","Horse","Brittle stars","Centipede"]
     |      >>> batch.to_pandas()
     |         n_legs        animals
     |      0       2       Flamingo
     |      1       4          Horse
     |      2       5  Brittle stars
     |      3     100      Centipede
     |      >>> isinstance(batch.to_pandas(), pd.DataFrame)
     |      True
     |
     |      Convert a Chunked Array to pandas Series:
     |
     |      >>> import pyarrow as pa
     |      >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
     |      >>> n_legs.to_pandas()
     |      0      2
     |      1      2
     |      2      4
     |      3      4
     |      4      5
     |      5    100
     |      dtype: int64
     |      >>> isinstance(n_legs.to_pandas(), pd.Series)
     |      True

    class UuidScalar(ExtensionScalar)
     |  Concrete class for Uuid extension scalar.
     |
     |  Method resolution order:
     |      UuidScalar
     |      ExtensionScalar
     |      Scalar
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  as_py(self, *, maps_as_pydicts=None)
     |      UuidScalar.as_py(self, *, maps_as_pydicts=None)
     |
     |      Return this scalar as a Python UUID.
     |
     |      Parameters
     |      ----------
     |      maps_as_pydicts : str, optional, default `None`
     |          Valid values are `None`, 'lossy', or 'strict'.
     |          This parameter is ignored for non-nested Scalars.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |
     |  __dict__
     |      dictionary for instance variables
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from ExtensionScalar:
     |
     |  __new__(*args, **kwargs) class method of ExtensionScalar
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  from_storage(typ, value)
     |      ExtensionScalar.from_storage(BaseExtensionType typ, value)
     |
     |      Construct ExtensionScalar from type and storage value.
     |
     |      Parameters
     |      ----------
     |      typ : DataType
     |          The extension type for the result scalar.
     |      value : object
     |          The storage value for the result scalar.
     |
     |      Returns
     |      -------
     |      ext_scalar : ExtensionScalar
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from ExtensionScalar:
     |
     |  value
     |      Return storage value as a scalar.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from ExtensionScalar:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from Scalar:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(...)
     |
     |  __and__(...)
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(...)
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(...)
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(...)
     |      Return self|value.
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __reduce__(self)
     |      Scalar.__reduce__(self)
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(...)
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  __sub__(...)
     |
     |  __truediv__(...)
     |
     |  __xor__(...)
     |
     |  cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |      Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None)
     |
     |      Cast scalar value to another data type.
     |
     |      See :func:`pyarrow.compute.cast` for usage.
     |
     |      Parameters
     |      ----------
     |      target_type : DataType, default None
     |          Type to cast scalar to.
     |      safe : boolean, default True
     |          Whether to check for conversion errors such as overflow.
     |      options : CastOptions, default None
     |          Additional checks pass by CastOptions
     |      memory_pool : MemoryPool, optional
     |          memory pool to use for allocations during function execution.
     |
     |      Returns
     |      -------
     |      scalar : A Scalar of the given target data type.
     |
     |  equals(self, other)
     |      Scalar.equals(self, Scalar other)
     |
     |      Parameters
     |      ----------
     |      other : pyarrow.Scalar
     |
     |      Returns
     |      -------
     |      bool
     |
     |  validate(self, *, full=False)
     |      Scalar.validate(self, *, full=False)
     |
     |      Perform validation checks.  An exception is raised if validation fails.
     |
     |      By default only cheap validation checks are run.  Pass `full=True`
     |      for thorough validation checks (potentially O(n)).
     |
     |      Parameters
     |      ----------
     |      full : bool, default False
     |          If True, run expensive checks, otherwise cheap checks only.
     |
     |      Raises
     |      ------
     |      ArrowInvalid
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from Scalar:
     |
     |  is_valid
     |      Holds a valid (non-null) value.
     |
     |  type
     |      Data type of the Scalar object.

    class UuidType(BaseExtensionType)
     |  Concrete class for UUID extension type.
     |
     |  Method resolution order:
     |      UuidType
     |      BaseExtensionType
     |      DataType
     |      _Weakrefable
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __arrow_ext_class__(self)
     |      UuidType.__arrow_ext_class__(self)
     |
     |  __arrow_ext_scalar_class__(self)
     |      UuidType.__arrow_ext_scalar_class__(self)
     |
     |  __reduce__(self)
     |      UuidType.__reduce__(self)
     |
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |
     |  __new__(*args, **kwargs)
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  __pyx_vtable__ = <capsule object NULL>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from BaseExtensionType:
     |
     |  wrap_array(self, storage)
     |      BaseExtensionType.wrap_array(self, storage)
     |
     |      Wrap the given storage array as an extension array.
     |
     |      Parameters
     |      ----------
     |      storage : Array or ChunkedArray
     |
     |      Returns
     |      -------
     |      array : Array or ChunkedArray
     |          Extension array wrapping the storage array
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from BaseExtensionType:
     |
     |  bit_width
     |      The bit width of the extension type.
     |
     |  byte_width
     |      The byte width of the extension type.
     |
     |  extension_name
     |      The extension type name.
     |
     |  storage_type
     |      The underlying storage type.
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from DataType:
     |
     |  __arrow_c_schema__(self)
     |      DataType.__arrow_c_schema__(self)
     |
     |      Export to a ArrowSchema PyCapsule
     |
     |      Unlike _export_to_c, this will not leak memory if the capsule is not used.
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __init__(self, /, *args, **kwargs)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __repr__(self, /)
     |      Return repr(self).
     |
     |  __str__(self, /)
     |      Return str(self).
     |
     |  equals(self, other, *, check_metadata=False)
     |      DataType.equals(self, other, *, check_metadata=False)
     |
     |      Return true if type is equivalent to passed value.
     |
     |      Parameters
     |      ----------
     |      other : DataType or string convertible to DataType
     |      check_metadata : bool
     |          Whether nested Field metadata equality should be checked as well.
     |
     |      Returns
     |      -------
     |      is_equal : bool
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().equals(pa.string())
     |      False
     |      >>> pa.int64().equals(pa.int64())
     |      True
     |
     |  field(self, i)
     |      DataType.field(self, i) -> Field
     |
     |      Parameters
     |      ----------
     |      i : int
     |
     |      Returns
     |      -------
     |      pyarrow.Field
     |
     |  to_pandas_dtype(self)
     |      DataType.to_pandas_dtype(self)
     |
     |      Return the equivalent NumPy / Pandas dtype.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().to_pandas_dtype()
     |      <class 'numpy.int64'>
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from DataType:
     |
     |  has_variadic_buffers
     |      If True, the number of expected buffers is only
     |      lower-bounded by num_buffers.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().has_variadic_buffers
     |      False
     |      >>> pa.string_view().has_variadic_buffers
     |      True
     |
     |  id
     |
     |  num_buffers
     |      Number of data buffers required to construct Array type
     |      excluding children.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64().num_buffers
     |      2
     |      >>> pa.string().num_buffers
     |      3
     |
     |  num_fields
     |      The number of child fields.
     |
     |      Examples
     |      --------
     |      >>> import pyarrow as pa
     |      >>> pa.int64()
     |      DataType(int64)
     |      >>> pa.int64().num_fields
     |      0
     |      >>> pa.list_(pa.string())
     |      ListType(list<item: string>)
     |      >>> pa.list_(pa.string()).num_fields
     |      1
     |      >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()})
     |      >>> struct.num_fields
     |      2

    class WriteStats(_frozen_importlib.WriteStats)
     |  WriteStats(num_messages, num_record_batches, num_dictionary_batches, num_dictionary_deltas, num_replaced_dictionaries)
     |
     |  IPC write statistics
     |
     |  Parameters
     |  ----------
     |  num_messages : int
     |      Number of messages.
     |  num_record_batches : int
     |      Number of record batches.
     |  num_dictionary_batches : int
     |      Number of dictionary batches.
     |  num_dictionary_deltas : int
     |      Delta of dictionaries.
     |  num_replaced_dictionaries : int
     |      Number of replaced dictionaries.
     |
     |  Method resolution order:
     |      WriteStats
     |      _frozen_importlib.WriteStats
     |      builtins.tuple
     |      builtins.object
     |
     |  Methods inherited from _frozen_importlib.WriteStats:
     |
     |  __getnewargs__(self) from collections.WriteStats
     |      Return self as a plain tuple.  Used by copy and pickle.
     |
     |  __repr__(self) from collections.WriteStats
     |      Return a nicely formatted representation string
     |
     |  _asdict(self) from collections.WriteStats
     |      Return a new dict which maps field names to their values.
     |
     |  _replace(self, /, **kwds) from collections.WriteStats
     |      Return a new WriteStats object replacing specified fields with new values
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from _frozen_importlib.WriteStats:
     |
     |  _make(iterable) from collections.WriteStats
     |      Make a new WriteStats object from a sequence or iterable
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from _frozen_importlib.WriteStats:
     |
     |  __new__(_cls, num_messages, num_record_batches, num_dictionary_batches, num_dictionary_deltas, num_replaced_dictionaries) from namedtuple_WriteStats.WriteStats
     |      Create new instance of WriteStats(num_messages, num_record_batches, num_dictionary_batches, num_dictionary_deltas, num_replaced_dictionaries)
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from _frozen_importlib.WriteStats:
     |
     |  num_messages
     |      Alias for field number 0
     |
     |  num_record_batches
     |      Alias for field number 1
     |
     |  num_dictionary_batches
     |      Alias for field number 2
     |
     |  num_dictionary_deltas
     |      Alias for field number 3
     |
     |  num_replaced_dictionaries
     |      Alias for field number 4
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from _frozen_importlib.WriteStats:
     |
     |  __match_args__ = ('num_messages', 'num_record_batches', 'num_dictionar...
     |
     |  _field_defaults = {}
     |
     |  _fields = ('num_messages', 'num_record_batches', 'num_dictionary_batch...
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.tuple:
     |
     |  __add__(self, value, /)
     |      Return self+value.
     |
     |  __contains__(self, key, /)
     |      Return bool(key in self).
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __getitem__(self, key, /)
     |      Return self[key].
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __iter__(self, /)
     |      Implement iter(self).
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __len__(self, /)
     |      Return len(self).
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mul__(self, value, /)
     |      Return self*value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  count(self, value, /)
     |      Return number of occurrences of value.
     |
     |  index(self, value, start=0, stop=9223372036854775807, /)
     |      Return first index of value.
     |
     |      Raises ValueError if the value is not present.
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from builtins.tuple:
     |
     |  __class_getitem__(...)
     |      See PEP 585

FUNCTIONS
    Lock = allocate_lock(...)
        allocate_lock() -> lock object
        (allocate() is an obsolete synonym)

        Create a new lock object. See help(type(threading.Lock())) for
        information about locks.

    __reduce_cython__(self)
        _RecordBatchFileReader.__reduce_cython__(self)

    __setstate_cython__(self, __pyx_state)
        _RecordBatchFileReader.__setstate_cython__(self, __pyx_state)

    allocate_buffer(size, memory_pool=None, resizable=False)
        allocate_buffer(int64_t size, MemoryPool memory_pool=None, resizable=False)

        Allocate a mutable buffer.

        Parameters
        ----------
        size : int
            Number of bytes to allocate (plus internal padding)
        memory_pool : MemoryPool, optional
            The pool to allocate memory from.
            If not given, the default memory pool is used.
        resizable : bool, default False
            If true, the returned buffer is resizable.

        Returns
        -------
        buffer : Buffer or ResizableBuffer

    arange(start, stop, step=1, *, memory_pool=None)
        arange(int64_t start, int64_t stop, int64_t step=1, *, memory_pool=None)

        Create an array of evenly spaced values within a given interval.

        This function is similar to Python's `range` function.
        The resulting array will contain values starting from `start` up to but not
        including `stop`, with a step size of `step`.

        Parameters
        ----------
        start : int
            The starting value for the sequence. The returned array will include this value.
        stop : int
            The stopping value for the sequence. The returned array will not include this value.
        step : int, default 1
            The spacing between values.
        memory_pool : MemoryPool, optional
            A memory pool to use for memory allocations.

        Raises
        ------
        ArrowInvalid
            If `step` is zero.

        Returns
        -------
        arange : Array

    array(obj, type=None, mask=None, size=None, from_pandas=None, safe=True, memory_pool=None)
        array(obj, type=None, mask=None, size=None, from_pandas=None, bool safe=True, MemoryPool memory_pool=None)

        Create pyarrow.Array instance from a Python object.

        Parameters
        ----------
        obj : sequence, iterable, ndarray, pandas.Series, Arrow-compatible array
            If both type and size are specified may be a single use iterable. If
            not strongly-typed, Arrow type will be inferred for resulting array.
            Any Arrow-compatible array that implements the Arrow PyCapsule Protocol
            (has an ``__arrow_c_array__`` or ``__arrow_c_device_array__`` method)
            can be passed as well.
        type : pyarrow.DataType
            Explicit type to attempt to coerce to, otherwise will be inferred from
            the data.
        mask : array[bool], optional
            Indicate which values are null (True) or not null (False).
        size : int64, optional
            Size of the elements. If the input is larger than size bail at this
            length. For iterators, if size is larger than the input iterator this
            will be treated as a "max size", but will involve an initial allocation
            of size followed by a resize to the actual size (so if you know the
            exact size specifying it correctly will give you better performance).
        from_pandas : bool, default None
            Use pandas's semantics for inferring nulls from values in
            ndarray-like data. If passed, the mask tasks precedence, but
            if a value is unmasked (not-null), but still null according to
            pandas semantics, then it is null. Defaults to False if not
            passed explicitly by user, or True if a pandas object is
            passed in.
        safe : bool, default True
            Check for overflows or other unsafe conversions.
        memory_pool : pyarrow.MemoryPool, optional
            If not passed, will allocate memory from the currently-set default
            memory pool.

        Returns
        -------
        array : pyarrow.Array or pyarrow.ChunkedArray
            A ChunkedArray instead of an Array is returned if:

            - the object data overflowed binary storage.
            - the object's ``__arrow_array__`` protocol method returned a chunked
              array.

        Notes
        -----
        Timezone will be preserved in the returned array for timezone-aware data,
        else no timezone will be returned for naive timestamps.
        Internally, UTC values are stored for timezone-aware data with the
        timezone set in the data type.

        Pandas's DateOffsets and dateutil.relativedelta.relativedelta are by
        default converted as MonthDayNanoIntervalArray. relativedelta leapdays
        are ignored as are all absolute fields on both objects. datetime.timedelta
        can also be converted to MonthDayNanoIntervalArray but this requires
        passing MonthDayNanoIntervalType explicitly.

        Converting to dictionary array will promote to a wider integer type for
        indices if the number of distinct values cannot be represented, even if
        the index type was explicitly set. This means that if there are more than
        127 values the returned dictionary array's index type will be at least
        pa.int16() even if pa.int8() was passed to the function. Note that an
        explicit index type will not be demoted even if it is wider than required.

        This class supports Python's standard operators
        for element-wise operations, i.e. arithmetic (`+`, `-`, `/`, `%`, `**`),
        bitwise (`&`, `|`, `^`, `>>`, `<<`) and others.
        They can be used directly instead of calling underlying
        `pyarrow.compute` functions explicitly.

        Examples
        --------
        >>> import pandas as pd
        >>> import pyarrow as pa
        >>> pa.array(pd.Series([1, 2]))
        <pyarrow.lib.Int64Array object at ...>
        [
          1,
          2
        ]

        >>> pa.array(["a", "b", "a"], type=pa.dictionary(pa.int8(), pa.string()))
        <pyarrow.lib.DictionaryArray object at ...>
        ...
        -- dictionary:
          [
            "a",
            "b"
          ]
        -- indices:
          [
            0,
            1,
            0
          ]

        >>> import numpy as np
        >>> pa.array(pd.Series([1, 2]), mask=np.array([0, 1], dtype=bool))
        <pyarrow.lib.Int64Array object at ...>
        [
          1,
          null
        ]

        >>> arr = pa.array(range(1024), type=pa.dictionary(pa.int8(), pa.int64()))
        >>> arr.type.index_type
        DataType(int16)

        >>> arr1 = pa.array([1, 2, 3], type=pa.int8())
        >>> arr2 = pa.array([4, 5, 6], type=pa.int8())
        >>> arr1 + arr2
        <pyarrow.lib.Int8Array object at ...>
        [
          5,
          7,
          9
        ]

        >>> val = pa.scalar(42)
        >>> val - arr1
        <pyarrow.lib.Int64Array object at ...>
        [
          41,
          40,
          39
        ]

    as_buffer(o)
        as_buffer(o)

    asarray(values, type=None)
        asarray(values, type=None)

        Convert to pyarrow.Array, inferring type if not provided.

        Parameters
        ----------
        values : array-like
            This can be a sequence, numpy.ndarray, pyarrow.Array or
            pyarrow.ChunkedArray. If a ChunkedArray is passed, the output will be
            a ChunkedArray, otherwise the output will be a Array.
        type : string or DataType
            Explicitly construct the array with this type. Attempt to cast if
            indicated type is different.

        Returns
        -------
        arr : Array or ChunkedArray

    benchmark_PandasObjectIsNull(obj)
        benchmark_PandasObjectIsNull(list obj)

    binary(length=-1)
        binary(int length=-1)

        Create variable-length or fixed size binary type.

        Parameters
        ----------
        length : int, optional, default -1
            If length == -1 then return a variable length binary type. If length is
            greater than or equal to 0 then return a fixed size binary type of
            width `length`.

        Examples
        --------
        Create an instance of a variable-length binary type:

        >>> import pyarrow as pa
        >>> pa.binary()
        DataType(binary)

        and use the variable-length binary type to create an array:

        >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary())
        <pyarrow.lib.BinaryArray object at ...>
        [
          666F6F,
          626172,
          62617A
        ]

        Create an instance of a fixed-size binary type:

        >>> pa.binary(3)
        FixedSizeBinaryType(fixed_size_binary[3])

        and use the fixed-length binary type to create an array:

        >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary(3))
        <pyarrow.lib.FixedSizeBinaryArray object at ...>
        [
          666F6F,
          626172,
          62617A
        ]

    binary_view()
        binary_view()

        Create a variable-length binary view type.

        Examples
        --------
        Create an instance of a string type:

        >>> import pyarrow as pa
        >>> pa.binary_view()
        DataType(binary_view)

    bool8()
        bool8()

        Create instance of bool8 extension type.

        Examples
        --------
        Create an instance of bool8 extension type:

        >>> import pyarrow as pa
        >>> type = pa.bool8()
        >>> type
        Bool8Type(extension<arrow.bool8>)

        Inspect the data type:

        >>> type.storage_type
        DataType(int8)

        Create a table with a bool8 array:

        >>> arr = [-1, 0, 1, 2, None]
        >>> storage = pa.array(arr, pa.int8())
        >>> other = pa.ExtensionArray.from_storage(type, storage)
        >>> pa.table([other], names=["unknown_col"])
        pyarrow.Table
        unknown_col: extension<arrow.bool8>
        ----
        unknown_col: [[-1,0,1,2,null]]

        Returns
        -------
        type : Bool8Type

    bool_()
        bool_()

        Create instance of boolean type.

        Examples
        --------
        Create an instance of a boolean type:

        >>> import pyarrow as pa
        >>> pa.bool_()
        DataType(bool)
        >>> print(pa.bool_())
        bool

        Create a ``Field`` type with a boolean type
        and a name:

        >>> pa.field('bool_field', pa.bool_())
        pyarrow.Field<bool_field: bool>

    chunked_array(arrays, type=None)
        chunked_array(arrays, type=None)

        Construct chunked array from list of array-like objects

        Parameters
        ----------
        arrays : Array, list of Array, or array-like
            Must all be the same data type. Can be empty only if type also passed.
            Any Arrow-compatible array that implements the Arrow PyCapsule Protocol
            (has an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method) can be
            passed as well.
        type : DataType or string coercible to DataType

        Returns
        -------
        ChunkedArray

        Examples
        --------
        >>> import pyarrow as pa
        >>> pa.chunked_array([], type=pa.int8())
        <pyarrow.lib.ChunkedArray object at ...>
        [
        ...
        ]

        >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]])
        <pyarrow.lib.ChunkedArray object at ...>
        [
          [
            2,
            2,
            4
          ],
          [
            4,
            5,
            100
          ]
        ]

    compress(buf, codec='lz4', asbytes=False, memory_pool=None)
        compress(buf, codec='lz4', asbytes=False, memory_pool=None)

        Compress data from buffer-like object.

        Parameters
        ----------
        buf : pyarrow.Buffer, bytes, or other object supporting buffer protocol
        codec : str, default 'lz4'
            Compression codec.
            Supported types: {'brotli, 'gzip', 'lz4', 'lz4_raw', 'snappy', 'zstd'}
        asbytes : bool, default False
            Return result as Python bytes object, otherwise Buffer.
        memory_pool : MemoryPool, default None
            Memory pool to use for buffer allocations, if any.

        Returns
        -------
        compressed : pyarrow.Buffer or bytes (if asbytes=True)

    concat_arrays(arrays, memory_pool=None)
        concat_arrays(arrays, MemoryPool memory_pool=None)

        Concatenate the given arrays.

        The contents of the input arrays are copied into the returned array.

        Raises
        ------
        ArrowInvalid
            If not all of the arrays have the same type.

        Parameters
        ----------
        arrays : iterable of pyarrow.Array
            Arrays to concatenate, must be identically typed.
        memory_pool : MemoryPool, default None
            For memory allocations. If None, the default pool is used.

        Examples
        --------
        >>> import pyarrow as pa
        >>> arr1 = pa.array([2, 4, 5, 100])
        >>> arr2 = pa.array([2, 4])
        >>> pa.concat_arrays([arr1, arr2])
        <pyarrow.lib.Int64Array object at ...>
        [
          2,
          4,
          5,
          100,
          2,
          4
        ]

    concat_batches(recordbatches, memory_pool=None)
        concat_batches(recordbatches, MemoryPool memory_pool=None)

        Concatenate pyarrow.RecordBatch objects.

        All recordbatches must share the same Schema,
        the operation implies a copy of the data to merge
        the arrays of the different RecordBatches.

        Parameters
        ----------
        recordbatches : iterable of pyarrow.RecordBatch objects
            Pyarrow record batches to concatenate into a single RecordBatch.
        memory_pool : MemoryPool, default None
            For memory allocations, if required, otherwise use default pool.

        Examples
        --------
        >>> import pyarrow as pa
        >>> t1 = pa.record_batch([
        ...     pa.array([2, 4, 5, 100]),
        ...     pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
        ...     ], names=['n_legs', 'animals'])
        >>> t2 = pa.record_batch([
        ...     pa.array([2, 4]),
        ...     pa.array(["Parrot", "Dog"])
        ...     ], names=['n_legs', 'animals'])
        >>> pa.concat_batches([t1,t2])
        pyarrow.RecordBatch
        n_legs: int64
        animals: string
        ----
        n_legs: [2,4,5,100,2,4]
        animals: ["Flamingo","Horse","Brittle stars","Centipede","Parrot","Dog"]

    concat_tables(tables, memory_pool=None, promote_options='none', **kwargs)
        concat_tables(tables, MemoryPool memory_pool=None, str promote_options='none', **kwargs)

        Concatenate pyarrow.Table objects.

        If promote_options="none", a zero-copy concatenation will be performed. The schemas
        of all the Tables must be the same (except the metadata), otherwise an
        exception will be raised. The result Table will share the metadata with the
        first table.

        If promote_options="default", any null type arrays will be casted to the type of other
        arrays in the column of the same name. If a table is missing a particular
        field, null values of the appropriate type will be generated to take the
        place of the missing field. The new schema will share the metadata with the
        first table. Each field in the new schema will share the metadata with the
        first table which has the field defined. Note that type promotions may
        involve additional allocations on the given ``memory_pool``.

        If promote_options="permissive", the behavior of default plus types will be promoted
        to the common denominator that fits all the fields.

        Parameters
        ----------
        tables : iterable of pyarrow.Table objects
            Pyarrow tables to concatenate into a single Table.
        memory_pool : MemoryPool, default None
            For memory allocations, if required, otherwise use default pool.
        promote_options : str, default none
            Accepts strings "none", "default" and "permissive".
        **kwargs : dict, optional

        Examples
        --------
        >>> import pyarrow as pa
        >>> t1 = pa.table([
        ...     pa.array([2, 4, 5, 100]),
        ...     pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
        ...     ], names=['n_legs', 'animals'])
        >>> t2 = pa.table([
        ...     pa.array([2, 4]),
        ...     pa.array(["Parrot", "Dog"])
        ...     ], names=['n_legs', 'animals'])
        >>> pa.concat_tables([t1,t2])
        pyarrow.Table
        n_legs: int64
        animals: string
        ----
        n_legs: [[2,4,5,100],[2,4]]
        animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Parrot","Dog"]]

    cpu_count()
        cpu_count()

        Return the number of threads to use in parallel operations.

        The number of threads is determined at startup by inspecting the
        ``OMP_NUM_THREADS`` and ``OMP_THREAD_LIMIT`` environment variables.
        If neither is present, it will default to the number of hardware threads
        on the system. It can be modified at runtime by calling
        :func:`set_cpu_count()`.

        See Also
        --------
        set_cpu_count : Modify the size of this pool.
        io_thread_count : The analogous function for the I/O thread pool.

    create_memory_map(path, size)
        create_memory_map(path, size)

        Create a file of the given size and memory-map it.

        Parameters
        ----------
        path : str
            The file path to create, on the local filesystem.
        size : int
            The file size to create.

        Returns
        -------
        mmap : MemoryMappedFile

        Examples
        --------
        Create a file with a memory map:

        >>> import pyarrow as pa
        >>> with pa.create_memory_map('example_mmap_create.dat', 27) as mmap:
        ...     mmap.write(b'Create a memory-mapped file')
        ...     mmap.read_at(10, 9)
        ...
        27
        b'memory-map'

    date32()
        date32()

        Create instance of 32-bit date (days since UNIX epoch 1970-01-01).

        Examples
        --------
        Create an instance of 32-bit date type:

        >>> import pyarrow as pa
        >>> pa.date32()
        DataType(date32[day])

        Create a scalar with 32-bit date type:

        >>> from datetime import date
        >>> pa.scalar(date(2012, 1, 1), type=pa.date32())
        <pyarrow.Date32Scalar: datetime.date(2012, 1, 1)>

    date64()
        date64()

        Create instance of 64-bit date (milliseconds since UNIX epoch 1970-01-01).

        Examples
        --------
        Create an instance of 64-bit date type:

        >>> import pyarrow as pa
        >>> pa.date64()
        DataType(date64[ms])

        Create a scalar with 64-bit date type:

        >>> from datetime import datetime
        >>> pa.scalar(datetime(2012, 1, 1), type=pa.date64())
        <pyarrow.Date64Scalar: datetime.date(2012, 1, 1)>

    decimal128(precision, scale=0)
        decimal128(int precision, int scale=0) -> DataType

        Create decimal type with precision and scale and 128-bit width.

        Arrow decimals are fixed-point decimal numbers encoded as a scaled
        integer.  The precision is the number of significant digits that the
        decimal type can represent; the scale is the number of digits after
        the decimal point (note the scale can be negative).

        As an example, ``decimal128(7, 3)`` can exactly represent the numbers
        1234.567 and -1234.567 (encoded internally as the 128-bit integers
        1234567 and -1234567, respectively), but neither 12345.67 nor 123.4567.

        ``decimal128(5, -3)`` can exactly represent the number 12345000
        (encoded internally as the 128-bit integer 12345), but neither
        123450000 nor 1234500.

        If you need a precision higher than 38 significant digits, consider
        using ``decimal256``.

        Parameters
        ----------
        precision : int
            Must be between 1 and 38
        scale : int

        Returns
        -------
        decimal_type : Decimal128Type

        Examples
        --------
        Create an instance of decimal type:

        >>> import pyarrow as pa
        >>> pa.decimal128(5, 2)
        Decimal128Type(decimal128(5, 2))

        Create an array with decimal type:

        >>> import decimal
        >>> a = decimal.Decimal('123.45')
        >>> pa.array([a], pa.decimal128(5, 2))
        <pyarrow.lib.Decimal128Array object at ...>
        [
          123.45
        ]

    decimal256(precision, scale=0)
        decimal256(int precision, int scale=0) -> DataType

        Create decimal type with precision and scale and 256-bit width.

        Arrow decimals are fixed-point decimal numbers encoded as a scaled
        integer.  The precision is the number of significant digits that the
        decimal type can represent; the scale is the number of digits after
        the decimal point (note the scale can be negative).

        For most use cases, the maximum precision offered by ``decimal128``
        is sufficient, and it will result in a more compact and more efficient
        encoding.  ``decimal256`` is useful if you need a precision higher
        than 38 significant digits.

        Parameters
        ----------
        precision : int
            Must be between 1 and 76
        scale : int

        Returns
        -------
        decimal_type : Decimal256Type

    decimal32(precision, scale=0)
        decimal32(int precision, int scale=0) -> DataType

        Create decimal type with precision and scale and 32-bit width.

        Arrow decimals are fixed-point decimal numbers encoded as a scaled
        integer.  The precision is the number of significant digits that the
        decimal type can represent; the scale is the number of digits after
        the decimal point (note the scale can be negative).

        As an example, ``decimal32(7, 3)`` can exactly represent the numbers
        1234.567 and -1234.567 (encoded internally as the 32-bit integers
        1234567 and -1234567, respectively), but neither 12345.67 nor 123.4567.

        ``decimal32(5, -3)`` can exactly represent the number 12345000
        (encoded internally as the 32-bit integer 12345), but neither
        123450000 nor 1234500.

        If you need a precision higher than 9 significant digits, consider
        using ``decimal64``, ``decimal128``, or ``decimal256``.

        Parameters
        ----------
        precision : int
            Must be between 1 and 9
        scale : int

        Returns
        -------
        decimal_type : Decimal32Type

        Examples
        --------
        Create an instance of decimal type:

        >>> import pyarrow as pa
        >>> pa.decimal32(5, 2)
        Decimal32Type(decimal32(5, 2))

        Create an array with decimal type:

        >>> import decimal
        >>> a = decimal.Decimal('123.45')
        >>> pa.array([a], pa.decimal32(5, 2))
        <pyarrow.lib.Decimal32Array object at ...>
        [
          123.45
        ]

    decimal64(precision, scale=0)
        decimal64(int precision, int scale=0) -> DataType

        Create decimal type with precision and scale and 64-bit width.

        Arrow decimals are fixed-point decimal numbers encoded as a scaled
        integer.  The precision is the number of significant digits that the
        decimal type can represent; the scale is the number of digits after
        the decimal point (note the scale can be negative).

        As an example, ``decimal64(7, 3)`` can exactly represent the numbers
        1234.567 and -1234.567 (encoded internally as the 64-bit integers
        1234567 and -1234567, respectively), but neither 12345.67 nor 123.4567.

        ``decimal64(5, -3)`` can exactly represent the number 12345000
        (encoded internally as the 64-bit integer 12345), but neither
        123450000 nor 1234500.

        If you need a precision higher than 18 significant digits, consider
        using ``decimal128``, or ``decimal256``.

        Parameters
        ----------
        precision : int
            Must be between 1 and 18
        scale : int

        Returns
        -------
        decimal_type : Decimal64Type

        Examples
        --------
        Create an instance of decimal type:

        >>> import pyarrow as pa
        >>> pa.decimal64(5, 2)
        Decimal64Type(decimal64(5, 2))

        Create an array with decimal type:

        >>> import decimal
        >>> a = decimal.Decimal('123.45')
        >>> pa.array([a], pa.decimal64(5, 2))
        <pyarrow.lib.Decimal64Array object at ...>
        [
          123.45
        ]

    decompress(buf, decompressed_size=None, codec='lz4', asbytes=False, memory_pool=None)
        decompress(buf, decompressed_size=None, codec='lz4', asbytes=False, memory_pool=None)

        Decompress data from buffer-like object.

        Parameters
        ----------
        buf : pyarrow.Buffer, bytes, or memoryview-compatible object
            Input object to decompress data from.
        decompressed_size : int, default None
            Size of the decompressed result
        codec : str, default 'lz4'
            Compression codec.
            Supported types: {'brotli, 'gzip', 'lz4', 'lz4_raw', 'snappy', 'zstd'}
        asbytes : bool, default False
            Return result as Python bytes object, otherwise Buffer.
        memory_pool : MemoryPool, default None
            Memory pool to use for buffer allocations, if any.

        Returns
        -------
        uncompressed : pyarrow.Buffer or bytes (if asbytes=True)

    default_cpu_memory_manager()
        default_cpu_memory_manager()

        Return the default CPU MemoryManager instance.

        The returned singleton instance uses the default MemoryPool.

    default_memory_pool()
        default_memory_pool()

        Return the process-global memory pool.

        Examples
        --------
        >>> default_memory_pool()
        <pyarrow.MemoryPool backend_name=... bytes_allocated=0 max_memory=...>

    dense_union(child_fields, type_codes=None)
        dense_union(child_fields, type_codes=None)

        Create DenseUnionType from child fields.

        A dense union is a nested type where each logical value is taken from
        a single child, at a specific offset.  A buffer of 8-bit type ids
        indicates which child a given logical value is to be taken from,
        and a buffer of 32-bit offsets indicates at which physical position
        in the given child array the logical value is to be taken from.

        Unlike a sparse union, a dense union allows encoding only the child array
        values which are actually referred to by the union array.  This is
        counterbalanced by the additional footprint of the offsets buffer, and
        the additional indirection cost when looking up values.

        Parameters
        ----------
        child_fields : sequence of Field values
            Each field must have a UTF8-encoded name, and these field names are
            part of the type metadata.
        type_codes : list of integers, default None

        Returns
        -------
        type : DenseUnionType

    dictionary(index_type, value_type, ordered=False)
        dictionary(index_type, value_type, bool ordered=False) -> DictionaryType

        Dictionary (categorical, or simply encoded) type.

        Parameters
        ----------
        index_type : DataType
        value_type : DataType
        ordered : bool

        Returns
        -------
        type : DictionaryType

        Examples
        --------
        Create an instance of dictionary type:

        >>> import pyarrow as pa
        >>> pa.dictionary(pa.int64(), pa.utf8())
        DictionaryType(dictionary<values=string, indices=int64, ordered=0>)

        Use dictionary type to create an array:

        >>> pa.array(["a", "b", None, "d"], pa.dictionary(pa.int64(), pa.utf8()))
        <pyarrow.lib.DictionaryArray object at ...>
        ...
        -- dictionary:
          [
            "a",
            "b",
            "d"
          ]
        -- indices:
          [
            0,
            1,
            null,
            2
          ]

    duration(unit)
        duration(unit)

        Create instance of a duration type with unit resolution.

        Parameters
        ----------
        unit : str
            One of 's' [second], 'ms' [millisecond], 'us' [microsecond], or
            'ns' [nanosecond].

        Returns
        -------
        type : pyarrow.DurationType

        Examples
        --------
        Create an instance of duration type:

        >>> import pyarrow as pa
        >>> pa.duration('us')
        DurationType(duration[us])
        >>> pa.duration('s')
        DurationType(duration[s])

        Create an array with duration type:

        >>> pa.array([0, 1, 2], type=pa.duration('s'))
        <pyarrow.lib.DurationArray object at ...>
        [
          0,
          1,
          2
        ]

    enable_signal_handlers(enable)
        enable_signal_handlers(bool enable)

        Enable or disable interruption of long-running operations.

        By default, certain long running operations will detect user
        interruptions, such as by pressing Ctrl-C.  This detection relies
        on setting a signal handler for the duration of the long-running
        operation, and may therefore interfere with other frameworks or
        libraries (such as an event loop).

        Parameters
        ----------
        enable : bool
            Whether to enable user interruption by setting a temporary
            signal handler.

    encode_file_path(path)
        encode_file_path(path)

    ensure_metadata(meta, allow_none=False)
        ensure_metadata(meta, bool allow_none=False) -> KeyValueMetadata

    ensure_type(ty, allow_none=False)
        ensure_type(ty, bool allow_none=False) -> DataType

    field(name, type=None, nullable=None, metadata=None)
        field(name, type=None, nullable=None, metadata=None)

        Create a pyarrow.Field instance.

        Parameters
        ----------
        name : str or bytes
            Name of the field.
            Alternatively, you can also pass an object that implements the Arrow
            PyCapsule Protocol for schemas (has an ``__arrow_c_schema__`` method).
        type : pyarrow.DataType or str
            Arrow datatype of the field or a string matching one.
        nullable : bool, default True
            Whether the field's values are nullable.
        metadata : dict, default None
            Optional field metadata, the keys and values must be coercible to
            bytes.

        Returns
        -------
        field : pyarrow.Field

        Examples
        --------
        Create an instance of pyarrow.Field:

        >>> import pyarrow as pa
        >>> pa.field('key', pa.int32())
        pyarrow.Field<key: int32>
        >>> pa.field('key', pa.int32(), nullable=False)
        pyarrow.Field<key: int32 not null>

        >>> field = pa.field('key', pa.int32(),
        ...                  metadata={"key": "Something important"})
        >>> field
        pyarrow.Field<key: int32>
        >>> field.metadata
        {b'key': b'Something important'}

        Use the field to create a struct type:

        >>> pa.struct([field])
        StructType(struct<key: int32>)

        A str can also be passed for the type parameter:

        >>> pa.field('key', 'int32')
        pyarrow.Field<key: int32>

    fixed_shape_tensor(value_type, shape, dim_names=None, permutation=None)
        fixed_shape_tensor(DataType value_type, shape, dim_names=None, permutation=None)

        Create instance of fixed shape tensor extension type with shape and optional
        names of tensor dimensions and indices of the desired logical
        ordering of dimensions.

        Parameters
        ----------
        value_type : DataType
            Data type of individual tensor elements.
        shape : tuple or list of integers
            The physical shape of the contained tensors.
        dim_names : tuple or list of strings, default None
            Explicit names to tensor dimensions.
        permutation : tuple or list integers, default None
            Indices of the desired ordering of the original dimensions.
            The indices contain a permutation of the values ``[0, 1, .., N-1]`` where
            N is the number of dimensions. The permutation indicates which dimension
            of the logical layout corresponds to which dimension of the physical tensor.
            For more information on this parameter see
            :ref:`fixed_shape_tensor_extension`.

        Examples
        --------
        Create an instance of fixed shape tensor extension type:

        >>> import pyarrow as pa
        >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), [2, 2])
        >>> tensor_type
        FixedShapeTensorType(extension<arrow.fixed_shape_tensor[value_type=int32, shape=[2,2]]>)

        Inspect the data type:

        >>> tensor_type.value_type
        DataType(int32)
        >>> tensor_type.shape
        [2, 2]

        Create a table with fixed shape tensor extension array:

        >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]]
        >>> storage = pa.array(arr, pa.list_(pa.int32(), 4))
        >>> tensor = pa.ExtensionArray.from_storage(tensor_type, storage)
        >>> pa.table([tensor], names=["tensor_array"])
        pyarrow.Table
        tensor_array: extension<arrow.fixed_shape_tensor[value_type=int32, shape=[2,2]]>
        ----
        tensor_array: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]

        Create an instance of fixed shape tensor extension type with names
        of tensor dimensions:

        >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3),
        ...                                     dim_names=['C', 'H', 'W'])
        >>> tensor_type.dim_names
        ['C', 'H', 'W']

        Create an instance of fixed shape tensor extension type with
        permutation:

        >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3),
        ...                                     permutation=[0, 2, 1])
        >>> tensor_type.permutation
        [0, 2, 1]

        Returns
        -------
        type : FixedShapeTensorType

    float16()
        float16()

        Create half-precision floating point type.

        Examples
        --------
        Create an instance of float16 type:

        >>> import pyarrow as pa
        >>> pa.float16()
        DataType(halffloat)
        >>> print(pa.float16())
        halffloat

        Create an array with float16 type:

        >>> arr = np.array([1.5, np.nan], dtype=np.float16)
        >>> a = pa.array(arr, type=pa.float16())
        >>> a
        <pyarrow.lib.HalfFloatArray object at ...>
        [
          1.5,
          nan
        ]

        Note that unlike other float types, if you convert this array
        to a python list, the types of its elements will be ``np.float16``

        >>> [type(val) for val in a.to_pylist()]
        [<class 'float'>, <class 'float'>]

    float32()
        float32()

        Create single-precision floating point type.

        Examples
        --------
        Create an instance of float32 type:

        >>> import pyarrow as pa
        >>> pa.float32()
        DataType(float)
        >>> print(pa.float32())
        float

        Create an array with float32 type:

        >>> pa.array([0.0, 1.0, 2.0], type=pa.float32())
        <pyarrow.lib.FloatArray object at ...>
        [
          0,
          1,
          2
        ]

    float64()
        float64()

        Create double-precision floating point type.

        Examples
        --------
        Create an instance of float64 type:

        >>> import pyarrow as pa
        >>> pa.float64()
        DataType(double)
        >>> print(pa.float64())
        double

        Create an array with float64 type:

        >>> pa.array([0.0, 1.0, 2.0], type=pa.float64())
        <pyarrow.lib.DoubleArray object at ...>
        [
          0,
          1,
          2
        ]

    foreign_buffer(address, size, base=None)
        foreign_buffer(address, size, base=None)

        Construct an Arrow buffer with the given *address* and *size*.

        The buffer will be optionally backed by the Python *base* object, if given.
        The *base* object will be kept alive as long as this buffer is alive,
        including across language boundaries (for example if the buffer is
        referenced by C++ code).

        Parameters
        ----------
        address : int
            The starting address of the buffer. The address can
            refer to both device or host memory but it must be
            accessible from device after mapping it with
            `get_device_address` method.
        size : int
            The size of device buffer in bytes.
        base : {None, object}
            Object that owns the referenced memory.

    from_numpy_dtype(dtype)
        from_numpy_dtype(dtype)

        Convert NumPy dtype to pyarrow.DataType.

        Parameters
        ----------
        dtype : the numpy dtype to convert


        Examples
        --------
        Create a pyarrow DataType from NumPy dtype:

        >>> import pyarrow as pa
        >>> import numpy as np
        >>> pa.from_numpy_dtype(np.dtype('float16'))
        DataType(halffloat)
        >>> pa.from_numpy_dtype('U')
        DataType(string)
        >>> pa.from_numpy_dtype(bool)
        DataType(bool)
        >>> pa.from_numpy_dtype(np.str_)
        DataType(string)

    frombytes(o, *, safe=False)
        frombytes(o, *, safe=False)

        Decode the given bytestring to unicode.

        Parameters
        ----------
        o : bytes-like
            Input object.
        safe : bool, default False
            If true, raise on encoding errors.

    get_record_batch_size(batch)
        get_record_batch_size(RecordBatch batch)

        Return total size of serialized RecordBatch including metadata and padding.

        Parameters
        ----------
        batch : RecordBatch
            The recordbatch for which we want to know the size.

    get_tensor_size(tensor)
        get_tensor_size(Tensor tensor)

        Return total size of serialized Tensor including metadata and padding.

        Parameters
        ----------
        tensor : Tensor
            The tensor for which we want to known the size.

    have_libhdfs()
        have_libhdfs()

        Return true if HDFS (HadoopFileSystem) library is set up correctly.

    infer_type(values, mask=None, from_pandas=False)
        infer_type(values, mask=None, from_pandas=False)

        Attempt to infer Arrow data type that can hold the passed Python
        sequence type in an Array object

        Parameters
        ----------
        values : array-like
            Sequence to infer type from.
        mask : ndarray (bool type), optional
            Optional exclusion mask where True marks null, False non-null.
        from_pandas : bool, default False
            Use pandas's NA/null sentinel values for type inference.

        Returns
        -------
        type : DataType

    input_stream(source, compression='detect', buffer_size=None)
        input_stream(source, compression='detect', buffer_size=None)

        Create an Arrow input stream.

        Parameters
        ----------
        source : str, Path, buffer, or file-like object
            The source to open for reading.
        compression : str optional, default 'detect'
            The compression algorithm to use for on-the-fly decompression.
            If "detect" and source is a file path, then compression will be
            chosen based on the file extension.
            If None, no compression will be applied.
            Otherwise, a well-known algorithm name must be supplied (e.g. "gzip").
        buffer_size : int, default None
            If None or 0, no buffering will happen. Otherwise the size of the
            temporary read buffer.

        Examples
        --------
        Create a readable BufferReader (NativeFile) from a Buffer or a memoryview object:

        >>> import pyarrow as pa
        >>> buf = memoryview(b"some data")
        >>> with pa.input_stream(buf) as stream:
        ...     stream.read(4)
        ...
        b'some'

        Create a readable OSFile (NativeFile) from a string or file path:

        >>> import gzip
        >>> with gzip.open('example.gz', 'wb') as f:
        ...     f.write(b'some data')
        ...
        9
        >>> with pa.input_stream('example.gz') as stream:
        ...     stream.read()
        ...
        b'some data'

        Create a readable PythonFile (NativeFile) from a a Python file object:

        >>> with open('example.txt', mode='w') as f:
        ...     f.write('some text')
        ...
        9
        >>> with pa.input_stream('example.txt') as stream:
        ...     stream.read(6)
        ...
        b'some t'

    int16()
        int16()

        Create instance of signed int16 type.

        Examples
        --------
        Create an instance of int16 type:

        >>> import pyarrow as pa
        >>> pa.int16()
        DataType(int16)
        >>> print(pa.int16())
        int16

        Create an array with int16 type:

        >>> pa.array([0, 1, 2], type=pa.int16())
        <pyarrow.lib.Int16Array object at ...>
        [
          0,
          1,
          2
        ]

    int32()
        int32()

        Create instance of signed int32 type.

        Examples
        --------
        Create an instance of int32 type:

        >>> import pyarrow as pa
        >>> pa.int32()
        DataType(int32)
        >>> print(pa.int32())
        int32

        Create an array with int32 type:

        >>> pa.array([0, 1, 2], type=pa.int32())
        <pyarrow.lib.Int32Array object at ...>
        [
          0,
          1,
          2
        ]

    int64()
        int64()

        Create instance of signed int64 type.

        Examples
        --------
        Create an instance of int64 type:

        >>> import pyarrow as pa
        >>> pa.int64()
        DataType(int64)
        >>> print(pa.int64())
        int64

        Create an array with int64 type:

        >>> pa.array([0, 1, 2], type=pa.int64())
        <pyarrow.lib.Int64Array object at ...>
        [
          0,
          1,
          2
        ]

    int8()
        int8()

        Create instance of signed int8 type.

        Examples
        --------
        Create an instance of int8 type:

        >>> import pyarrow as pa
        >>> pa.int8()
        DataType(int8)
        >>> print(pa.int8())
        int8

        Create an array with int8 type:

        >>> pa.array([0, 1, 2], type=pa.int8())
        <pyarrow.lib.Int8Array object at ...>
        [
          0,
          1,
          2
        ]

    io_thread_count()
        io_thread_count()

        Return the number of threads to use for I/O operations.

        Many operations, such as scanning a dataset, will implicitly make
        use of this pool. The number of threads is set to a fixed value at
        startup. It can be modified at runtime by calling
        :func:`set_io_thread_count()`.

        See Also
        --------
        set_io_thread_count : Modify the size of this pool.
        cpu_count : The analogous function for the CPU thread pool.

    is_boolean_value(obj)
        is_boolean_value(obj)

        Check if the object is a boolean.

        Parameters
        ----------
        obj : object
            The object to check

    is_float_value(obj)
        is_float_value(obj)

        Check if the object is a float.

        Parameters
        ----------
        obj : object
            The object to check

    is_integer_value(obj)
        is_integer_value(obj)

        Check if the object is an integer.

        Parameters
        ----------
        obj : object
            The object to check

    is_opentelemetry_enabled() -> 'bool'
        is_opentelemetry_enabled() -> bool

        Returns True if OpenTelemetry is enabled in libarrow.

    is_threading_enabled() -> 'bool'
        is_threading_enabled() -> bool

        Returns True if threading is enabled in libarrow.

        If it isn't enabled, then python shouldn't create any
        threads either, because we're probably on a system where
        threading doesn't work (e.g. Emscripten).

    jemalloc_memory_pool()
        jemalloc_memory_pool()

        Return a memory pool based on the jemalloc heap.

        NotImplementedError is raised if jemalloc support is not enabled.

    jemalloc_set_decay_ms(decay_ms)
        jemalloc_set_decay_ms(decay_ms)

        Set arenas.dirty_decay_ms and arenas.muzzy_decay_ms to indicated number of
        milliseconds. A value of 0 (the default) results in dirty / muzzy memory
        pages being released right away to the OS, while a higher value will result
        in a time-based decay. See the jemalloc docs for more information

        It's best to set this at the start of your application.

        Parameters
        ----------
        decay_ms : int
            Number of milliseconds to set for jemalloc decay conf parameters. Note
            that this change will only affect future memory arenas

    json_(storage_type=DataType(string))
        json_(DataType storage_type=utf8())

        Create instance of JSON extension type.

        Parameters
        ----------
        storage_type : DataType, default pyarrow.string()
            The underlying data type. Can be on of the following types:
            string, large_string, string_view.

        Returns
        -------
        type : JsonType

        Examples
        --------
        Create an instance of JSON extension type:

        >>> import pyarrow as pa
        >>> pa.json_(pa.utf8())
        JsonType(extension<arrow.json>)

        Use the JSON type to create an array:

        >>> pa.array(['{"a": 1}', '{"b": 2}'], type=pa.json_(pa.utf8()))
        <pyarrow.lib.JsonArray object at ...>
        [
          "{"a": 1}",
          "{"b": 2}"
        ]

    large_binary()
        large_binary()

        Create large variable-length binary type.

        This data type may not be supported by all Arrow implementations.  Unless
        you need to represent data larger than 2GB, you should prefer binary().

        Examples
        --------
        Create an instance of large variable-length binary type:

        >>> import pyarrow as pa
        >>> pa.large_binary()
        DataType(large_binary)

        and use the type to create an array:

        >>> pa.array(['foo', 'bar', 'baz'], type=pa.large_binary())
        <pyarrow.lib.LargeBinaryArray object at ...>
        [
          666F6F,
          626172,
          62617A
        ]

    large_list(value_type)
        large_list(value_type) -> LargeListType

        Create LargeListType instance from child data type or field.

        This data type may not be supported by all Arrow implementations.
        Unless you need to represent data larger than 2**31 elements, you should
        prefer list_().

        Parameters
        ----------
        value_type : DataType or Field

        Returns
        -------
        list_type : DataType

        Examples
        --------
        Create an instance of LargeListType:

        >>> import pyarrow as pa
        >>> pa.large_list(pa.int8())
        LargeListType(large_list<item: int8>)

        Use the LargeListType to create an array:

        >>> pa.array([[-1, 3]] * 5, type=pa.large_list(pa.int8()))
        <pyarrow.lib.LargeListArray object at ...>
        [
          [
            -1,
            3
          ],
          [
            -1,
            3
          ],
        ...

    large_list_view(value_type)
        large_list_view(value_type) -> LargeListViewType

        Create LargeListViewType instance from child data type or field.

        This data type may not be supported by all Arrow implementations
        because it is an alternative to the ListType.

        Parameters
        ----------
        value_type : DataType or Field

        Returns
        -------
        list_view_type : DataType

        Examples
        --------
        Create an instance of LargeListViewType:

        >>> import pyarrow as pa
        >>> pa.large_list_view(pa.int8())
        LargeListViewType(large_list_view<item: int8>)

    large_string()
        large_string()

        Create large UTF8 variable-length string type.

        This data type may not be supported by all Arrow implementations.  Unless
        you need to represent data larger than 2GB, you should prefer string().

        Examples
        --------
        Create an instance of large UTF8 variable-length binary type:

        >>> import pyarrow as pa
        >>> pa.large_string()
        DataType(large_string)

        and use the type to create an array:

        >>> pa.array(['foo', 'bar'] * 50, type=pa.large_string())
        <pyarrow.lib.LargeStringArray object at ...>
        [
          "foo",
          "bar",
          ...
          "foo",
          "bar"
        ]

    large_utf8()
        large_utf8()

        Alias for large_string().

        Examples
        --------
        Create an instance of large UTF8 variable-length binary type:

        >>> import pyarrow as pa
        >>> pa.large_utf8()
        DataType(large_string)

        and use the type to create an array:

        >>> pa.array(['foo', 'bar'] * 50, type=pa.large_utf8())
        <pyarrow.lib.LargeStringArray object at ...>
        [
          "foo",
          "bar",
          ...
          "foo",
          "bar"
        ]

    list_(value_type, list_size=-1)
        list_(value_type, int list_size=-1)

        Create ListType instance from child data type or field.

        Parameters
        ----------
        value_type : DataType or Field
        list_size : int, optional, default -1
            If length == -1 then return a variable length list type. If length is
            greater than or equal to 0 then return a fixed size list type.

        Returns
        -------
        list_type : DataType

        Examples
        --------
        Create an instance of ListType:

        >>> import pyarrow as pa
        >>> pa.list_(pa.string())
        ListType(list<item: string>)
        >>> pa.list_(pa.int32(), 2)
        FixedSizeListType(fixed_size_list<item: int32>[2])

        Use the ListType to create a scalar:

        >>> pa.scalar(['foo', None], type=pa.list_(pa.string(), 2))
        <pyarrow.FixedSizeListScalar: ['foo', None]>

        or an array:

        >>> pa.array([[1, 2], [3, 4]], pa.list_(pa.int32(), 2))
        <pyarrow.lib.FixedSizeListArray object at ...>
        [
          [
            1,
            2
          ],
          [
            3,
            4
          ]
        ]

    list_view(value_type)
        list_view(value_type) -> ListViewType

        Create ListViewType instance from child data type or field.

        This data type may not be supported by all Arrow implementations
        because it is an alternative to the ListType.

        Parameters
        ----------
        value_type : DataType or Field

        Returns
        -------
        list_view_type : DataType

        Examples
        --------
        Create an instance of ListViewType:

        >>> import pyarrow as pa
        >>> pa.list_view(pa.string())
        ListViewType(list_view<item: string>)

    log_memory_allocations(enable=True)
        log_memory_allocations(enable=True)

        Enable or disable memory allocator logging for debugging purposes

        Parameters
        ----------
        enable : bool, default True
            Pass False to disable logging

    logging_memory_pool(parent)
        logging_memory_pool(MemoryPool parent)

        Create and return a MemoryPool instance that redirects to the
        *parent*, but also dumps allocation logs on stderr.

        Parameters
        ----------
        parent : MemoryPool
            The real memory pool that should be used for allocations.

    map_(key_type, item_type, keys_sorted=False)
        map_(key_type, item_type, keys_sorted=False) -> MapType

        Create MapType instance from key and item data types or fields.

        Parameters
        ----------
        key_type : DataType or Field
        item_type : DataType or Field
        keys_sorted : bool

        Returns
        -------
        map_type : DataType

        Examples
        --------
        Create an instance of MapType:

        >>> import pyarrow as pa
        >>> pa.map_(pa.string(), pa.int32())
        MapType(map<string, int32>)
        >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True)
        MapType(map<string, int32, keys_sorted>)

        Use MapType to create an array:

        >>> data = [[{'key': 'a', 'value': 1}, {'key': 'b', 'value': 2}], [{'key': 'c', 'value': 3}]]
        >>> pa.array(data, type=pa.map_(pa.string(), pa.int32(), keys_sorted=True))
        <pyarrow.lib.MapArray object at ...>
        [
          keys:
          [
            "a",
            "b"
          ]
          values:
          [
            1,
            2
          ],
          keys:
          [
            "c"
          ]
          values:
          [
            3
          ]
        ]

    memory_map(path, mode='r')
        memory_map(path, mode='r')

        Open memory map at file path. Size of the memory map cannot change.

        Parameters
        ----------
        path : str
        mode : {'r', 'r+', 'w'}, default 'r'
            Whether the file is opened for reading ('r'), writing ('w')
            or both ('r+').

        Returns
        -------
        mmap : MemoryMappedFile

        Examples
        --------
        Reading from a memory map without any memory allocation or copying:

        >>> import pyarrow as pa
        >>> with pa.output_stream('example_mmap.txt') as stream:
        ...     stream.write(b'Constructing a buffer referencing the mapped memory')
        ...
        51
        >>> with pa.memory_map('example_mmap.txt') as mmap:
        ...     mmap.read_at(6,45)
        ...
        b'memory'

    mimalloc_memory_pool()
        mimalloc_memory_pool()

        Return a memory pool based on the mimalloc heap.

        NotImplementedError is raised if mimalloc support is not enabled.

    month_day_nano_interval()
        month_day_nano_interval()

        Create instance of an interval type representing months, days and
        nanoseconds between two dates.

        Examples
        --------
        Create an instance of an month_day_nano_interval type:

        >>> import pyarrow as pa
        >>> pa.month_day_nano_interval()
        DataType(month_day_nano_interval)

        Create a scalar with month_day_nano_interval type:

        >>> pa.scalar((1, 15, -30), type=pa.month_day_nano_interval())
        <pyarrow.MonthDayNanoIntervalScalar: MonthDayNano(months=1, days=15, nanoseconds=-30)>

    null()
        null()

        Create instance of null type.

        Examples
        --------
        Create an instance of a null type:

        >>> import pyarrow as pa
        >>> pa.null()
        DataType(null)
        >>> print(pa.null())
        null

        Create a ``Field`` type with a null type and a name:

        >>> pa.field('null_field', pa.null())
        pyarrow.Field<null_field: null>

    nulls(size, type=None, memory_pool=None)
        nulls(size, type=None, MemoryPool memory_pool=None)

        Create a strongly-typed Array instance with all elements null.

        Parameters
        ----------
        size : int
            Array length.
        type : pyarrow.DataType, default None
            Explicit type for the array. By default use NullType.
        memory_pool : MemoryPool, default None
            Arrow MemoryPool to use for allocations. Uses the default memory
            pool if not passed.

        Returns
        -------
        arr : Array

        Examples
        --------
        >>> import pyarrow as pa
        >>> pa.nulls(10)
        <pyarrow.lib.NullArray object at ...>
        10 nulls

        >>> pa.nulls(3, pa.uint32())
        <pyarrow.lib.UInt32Array object at ...>
        [
          null,
          null,
          null
        ]

    opaque(storage_type, type_name, vendor_name)
        opaque(DataType storage_type, str type_name, str vendor_name)

        Create instance of opaque extension type.

        Parameters
        ----------
        storage_type : DataType
            The underlying data type.
        type_name : str
            The name of the type in the external system.
        vendor_name : str
            The name of the external system.

        Examples
        --------
        Create an instance of an opaque extension type:

        >>> import pyarrow as pa
        >>> type = pa.opaque(pa.binary(), "other", "jdbc")
        >>> type
        OpaqueType(extension<arrow.opaque[storage_type=binary, type_name=other, vendor_name=jdbc]>)

        Inspect the data type:

        >>> type.storage_type
        DataType(binary)
        >>> type.type_name
        'other'
        >>> type.vendor_name
        'jdbc'

        Create a table with an opaque array:

        >>> arr = [None, b"foobar"]
        >>> storage = pa.array(arr, pa.binary())
        >>> other = pa.ExtensionArray.from_storage(type, storage)
        >>> pa.table([other], names=["unknown_col"])
        pyarrow.Table
        unknown_col: extension<arrow.opaque[storage_type=binary, type_name=other, vendor_name=jdbc]>
        ----
        unknown_col: [[null,666F6F626172]]

        Returns
        -------
        type : OpaqueType

    output_stream(source, compression='detect', buffer_size=None)
        output_stream(source, compression='detect', buffer_size=None)

        Create an Arrow output stream.

        Parameters
        ----------
        source : str, Path, buffer, file-like object
            The source to open for writing.
        compression : str optional, default 'detect'
            The compression algorithm to use for on-the-fly compression.
            If "detect" and source is a file path, then compression will be
            chosen based on the file extension.
            If None, no compression will be applied.
            Otherwise, a well-known algorithm name must be supplied (e.g. "gzip").
        buffer_size : int, default None
            If None or 0, no buffering will happen. Otherwise the size of the
            temporary write buffer.

        Examples
        --------
        Create a writable NativeFile from a pyarrow Buffer:

        >>> import pyarrow as pa
        >>> data = b"buffer data"
        >>> empty_obj = bytearray(11)
        >>> buf = pa.py_buffer(empty_obj)
        >>> with pa.output_stream(buf) as stream:
        ...     stream.write(data)
        ...
        11
        >>> with pa.input_stream(buf) as stream:
        ...     stream.read(6)
        ...
        b'buffer'

        or from a memoryview object:

        >>> buf = memoryview(empty_obj)
        >>> with pa.output_stream(buf) as stream:
        ...     stream.write(data)
        ...
        11
        >>> with pa.input_stream(buf) as stream:
        ...     stream.read()
        ...
        b'buffer data'

        Create a writable NativeFile from a string or file path:

        >>> with pa.output_stream('example_second.txt') as stream:
        ...     stream.write(b'Write some data')
        ...
        15
        >>> with pa.input_stream('example_second.txt') as stream:
        ...     stream.read()
        ...
        b'Write some data'

    proxy_memory_pool(parent)
        proxy_memory_pool(MemoryPool parent)

        Create and return a MemoryPool instance that redirects to the
        *parent*, but with separate allocation statistics.

        Parameters
        ----------
        parent : MemoryPool
            The real memory pool that should be used for allocations.

    py_buffer(obj)
        py_buffer(obj)

        Construct an Arrow buffer from a Python bytes-like or buffer-like object

        Parameters
        ----------
        obj : object
            the object from which the buffer should be constructed.

    read_message(source)
        read_message(source)

        Read length-prefixed message from file or buffer-like object

        Parameters
        ----------
        source : pyarrow.NativeFile, file-like object, or buffer-like object

        Returns
        -------
        message : Message

    read_record_batch(obj, schema, dictionary_memo=None)
        read_record_batch(obj, Schema schema, DictionaryMemo dictionary_memo=None)

        Read RecordBatch from message, given a known schema. If reading data from a
        complete IPC stream, use ipc.open_stream instead

        Parameters
        ----------
        obj : Message or Buffer-like
        schema : Schema
        dictionary_memo : DictionaryMemo, optional
            If message contains dictionaries, must pass a populated
            DictionaryMemo

        Returns
        -------
        batch : RecordBatch

    read_schema(obj, dictionary_memo=None)
        read_schema(obj, DictionaryMemo dictionary_memo=None)

        Read Schema from message or buffer

        Parameters
        ----------
        obj : buffer or Message
        dictionary_memo : DictionaryMemo, optional
            Needed to be able to reconstruct dictionary-encoded fields
            with read_record_batch

        Returns
        -------
        schema : Schema

    read_tensor(source)
        read_tensor(source)

        Read pyarrow.Tensor from pyarrow.NativeFile object from current
        position. If the file source supports zero copy (e.g. a memory map), then
        this operation does not allocate any memory. This function not assume that
        the stream is aligned

        Parameters
        ----------
        source : pyarrow.NativeFile

        Returns
        -------
        tensor : Tensor

    record_batch(data, names=None, schema=None, metadata=None)
        record_batch(data, names=None, schema=None, metadata=None)

        Create a pyarrow.RecordBatch from another Python data structure or sequence
        of arrays.

        Parameters
        ----------
        data : dict, list, pandas.DataFrame, Arrow-compatible table
            A mapping of strings to Arrays or Python lists, a list of Arrays,
            a pandas DataFame, or any tabular object implementing the
            Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or
            ``__arrow_c_device_array__`` method).
        names : list, default None
            Column names if list of arrays passed as data. Mutually exclusive with
            'schema' argument.
        schema : Schema, default None
            The expected schema of the RecordBatch. If not passed, will be inferred
            from the data. Mutually exclusive with 'names' argument.
        metadata : dict or Mapping, default None
            Optional metadata for the schema (if schema not passed).

        Returns
        -------
        RecordBatch

        See Also
        --------
        RecordBatch.from_arrays, RecordBatch.from_pandas, table

        Examples
        --------
        >>> import pyarrow as pa
        >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
        >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
        >>> names = ["n_legs", "animals"]

        Construct a RecordBatch from a python dictionary:

        >>> pa.record_batch({"n_legs": n_legs, "animals": animals})
        pyarrow.RecordBatch
        n_legs: int64
        animals: string
        ----
        n_legs: [2,2,4,4,5,100]
        animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
        >>> pa.record_batch({"n_legs": n_legs, "animals": animals}).to_pandas()
           n_legs        animals
        0       2       Flamingo
        1       2         Parrot
        2       4            Dog
        3       4          Horse
        4       5  Brittle stars
        5     100      Centipede

        Creating a RecordBatch from a list of arrays with names:

        >>> pa.record_batch([n_legs, animals], names=names)
        pyarrow.RecordBatch
        n_legs: int64
        animals: string
        ----
        n_legs: [2,2,4,4,5,100]
        animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]

        Creating a RecordBatch from a list of arrays with names and metadata:

        >>> my_metadata={"n_legs": "How many legs does an animal have?"}
        >>> pa.record_batch([n_legs, animals],
        ...                  names=names,
        ...                  metadata = my_metadata)
        pyarrow.RecordBatch
        n_legs: int64
        animals: string
        ----
        n_legs: [2,2,4,4,5,100]
        animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
        >>> pa.record_batch([n_legs, animals],
        ...                  names=names,
        ...                  metadata = my_metadata).schema
        n_legs: int64
        animals: string
        -- schema metadata --
        n_legs: 'How many legs does an animal have?'

        Creating a RecordBatch from a pandas DataFrame:

        >>> import pandas as pd
        >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022],
        ...                    'month': [3, 5, 7, 9],
        ...                    'day': [1, 5, 9, 13],
        ...                    'n_legs': [2, 4, 5, 100],
        ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
        >>> pa.record_batch(df)
        pyarrow.RecordBatch
        year: int64
        month: int64
        day: int64
        n_legs: int64
        animals: large_string
        ----
        year: [2020,2022,2021,2022]
        month: [3,5,7,9]
        day: [1,5,9,13]
        n_legs: [2,4,5,100]
        animals: ["Flamingo","Horse","Brittle stars","Centipede"]

        >>> pa.record_batch(df).to_pandas()
           year  month  day  n_legs        animals
        0  2020      3    1       2       Flamingo
        1  2022      5    5       4          Horse
        2  2021      7    9       5  Brittle stars
        3  2022      9   13     100      Centipede

        Creating a RecordBatch from a pandas DataFrame with schema:

        >>> my_schema = pa.schema([
        ...     pa.field('n_legs', pa.int64()),
        ...     pa.field('animals', pa.string())],
        ...     metadata={"n_legs": "Number of legs per animal"})
        >>> pa.record_batch(df, my_schema).schema
        n_legs: int64
        animals: string
        -- schema metadata --
        n_legs: 'Number of legs per animal'
        pandas: ...
        >>> pa.record_batch(df, my_schema).to_pandas()
           n_legs        animals
        0       2       Flamingo
        1       4          Horse
        2       5  Brittle stars
        3     100      Centipede

    register_extension_type(ext_type)
        register_extension_type(ext_type)

        Register a Python extension type.

        Registration is based on the extension name (so different registered types
        need unique extension names). Registration needs an extension type
        instance, but then works for any instance of the same subclass regardless
        of parametrization of the type.

        Parameters
        ----------
        ext_type : BaseExtensionType instance
            The ExtensionType subclass to register.

        Examples
        --------
        Define a RationalType extension type subclassing ExtensionType:

        >>> import pyarrow as pa
        >>> class RationalType(pa.ExtensionType):
        ...     def __init__(self, data_type: pa.DataType):
        ...         if not pa.types.is_integer(data_type):
        ...             raise TypeError(f"data_type must be an integer type not {data_type}")
        ...         super().__init__(
        ...             pa.struct(
        ...                 [
        ...                     ("numer", data_type),
        ...                     ("denom", data_type),
        ...                 ],
        ...             ),
        ...             # N.B. This name does _not_ reference `data_type` so deserialization
        ...             # will work for _any_ integer `data_type` after registration
        ...             "my_package.rational",
        ...         )
        ...     def __arrow_ext_serialize__(self) -> bytes:
        ...         # No parameters are necessary
        ...         return b""
        ...     @classmethod
        ...     def __arrow_ext_deserialize__(cls, storage_type, serialized):
        ...         # return an instance of this subclass
        ...         return RationalType(storage_type[0].type)

        Register the extension type:

        >>> pa.register_extension_type(RationalType(pa.int64()))

        Unregister the extension type:

        >>> pa.unregister_extension_type("my_package.rational")

    repeat(value, size, memory_pool=None)
        repeat(value, size, MemoryPool memory_pool=None)

        Create an Array instance whose slots are the given scalar.

        Parameters
        ----------
        value : Scalar-like object
            Either a pyarrow.Scalar or any python object coercible to a Scalar.
        size : int
            Number of times to repeat the scalar in the output Array.
        memory_pool : MemoryPool, default None
            Arrow MemoryPool to use for allocations. Uses the default memory
            pool if not passed.

        Returns
        -------
        arr : Array

        Examples
        --------
        >>> import pyarrow as pa
        >>> pa.repeat(10, 3)
        <pyarrow.lib.Int64Array object at ...>
        [
          10,
          10,
          10
        ]

        >>> pa.repeat([1, 2], 2)
        <pyarrow.lib.ListArray object at ...>
        [
          [
            1,
            2
          ],
          [
            1,
            2
          ]
        ]

        >>> pa.repeat("string", 3)
        <pyarrow.lib.StringArray object at ...>
        [
          "string",
          "string",
          "string"
        ]

        >>> pa.repeat(pa.scalar({'a': 1, 'b': [1, 2]}), 2)
        <pyarrow.lib.StructArray object at ...>
        -- is_valid: all not null
        -- child 0 type: int64
          [
            1,
            1
          ]
        -- child 1 type: list<item: int64>
          [
            [
              1,
              2
            ],
            [
              1,
              2
            ]
          ]

    run_end_encoded(run_end_type, value_type)
        run_end_encoded(run_end_type, value_type)

        Create RunEndEncodedType from run-end and value types.

        Parameters
        ----------
        run_end_type : pyarrow.DataType
            The integer type of the run_ends array. Must be 'int16', 'int32', or 'int64'.
        value_type : pyarrow.DataType
            The type of the values array.

        Returns
        -------
        type : RunEndEncodedType

    runtime_info()
        runtime_info()

        Get runtime information.

        Returns
        -------
        info : pyarrow.RuntimeInfo

    scalar(value, type=None, *, from_pandas=None, memory_pool=None)
        scalar(value, type=None, *, from_pandas=None, MemoryPool memory_pool=None)

        Create a pyarrow.Scalar instance from a Python object.

        Parameters
        ----------
        value : Any
            Python object coercible to arrow's type system.
        type : pyarrow.DataType
            Explicit type to attempt to coerce to, otherwise will be inferred from
            the value.
        from_pandas : bool, default None
            Use pandas's semantics for inferring nulls from values in
            ndarray-like data. Defaults to False if not passed explicitly by user,
            or True if a pandas object is passed in.
        memory_pool : pyarrow.MemoryPool, optional
            If not passed, will allocate memory from the currently-set default
            memory pool.

        Returns
        -------
        scalar : pyarrow.Scalar

        Examples
        --------
        >>> import pyarrow as pa

        >>> pa.scalar(42)
        <pyarrow.Int64Scalar: 42>

        >>> pa.scalar("string")
        <pyarrow.StringScalar: 'string'>

        >>> pa.scalar([1, 2])
        <pyarrow.ListScalar: [1, 2]>

        >>> pa.scalar([1, 2], type=pa.list_(pa.int16()))
        <pyarrow.ListScalar: [1, 2]>

    schema(fields, metadata=None)
        schema(fields, metadata=None)

        Construct pyarrow.Schema from collection of fields.

        Parameters
        ----------
        fields : iterable of Fields or tuples, or mapping of strings to DataTypes
            Can also pass an object that implements the Arrow PyCapsule Protocol
            for schemas (has an ``__arrow_c_schema__`` method).
        metadata : dict, default None
            Keys and values must be coercible to bytes.

        Examples
        --------
        Create a Schema from iterable of tuples:

        >>> import pyarrow as pa
        >>> pa.schema([
        ...     ('some_int', pa.int32()),
        ...     ('some_string', pa.string()),
        ...     pa.field('some_required_string', pa.string(), nullable=False)
        ... ])
        some_int: int32
        some_string: string
        some_required_string: string not null

        Create a Schema from iterable of Fields:

        >>> pa.schema([
        ...     pa.field('some_int', pa.int32()),
        ...     pa.field('some_string', pa.string())
        ... ])
        some_int: int32
        some_string: string

        DataTypes can also be passed as strings. The following is equivalent to the
        above example:

        >>> pa.schema([
        ...     pa.field('some_int', "int32"),
        ...     pa.field('some_string', "string")
        ... ])
        some_int: int32
        some_string: string

        Or more concisely:

        >>> pa.schema([
        ...     ('some_int', "int32"),
        ...     ('some_string', "string")
        ... ])
        some_int: int32
        some_string: string

        Returns
        -------
        schema : pyarrow.Schema

    set_cpu_count(count)
        set_cpu_count(int count)

        Set the number of threads to use in parallel operations.

        Parameters
        ----------
        count : int
            The number of concurrent threads that should be used.

        See Also
        --------
        cpu_count : Get the size of this pool.
        set_io_thread_count : The analogous function for the I/O thread pool.

    set_io_thread_count(count)
        set_io_thread_count(int count)

        Set the number of threads to use for I/O operations.

        Many operations, such as scanning a dataset, will implicitly make
        use of this pool.

        Parameters
        ----------
        count : int
            The max number of threads that may be used for I/O.
            Must be positive.

        See Also
        --------
        io_thread_count : Get the size of this pool.
        set_cpu_count : The analogous function for the CPU thread pool.

    set_memory_pool(pool)
        set_memory_pool(MemoryPool pool)

        Set the default memory pool.

        Parameters
        ----------
        pool : MemoryPool
            The memory pool that should be used by default.

    set_timezone_db_path(path)
        set_timezone_db_path(path)

        Configure the path to text timezone database on Windows.

        .. deprecated:: 24.0.0
           This function is deprecated and will be removed in a future version.
           PyArrow now uses the operating system's timezone database on Windows.

        Parameters
        ----------
        path : str
            Path to text timezone database.

    sparse_union(child_fields, type_codes=None)
        sparse_union(child_fields, type_codes=None)

        Create SparseUnionType from child fields.

        A sparse union is a nested type where each logical value is taken from
        a single child.  A buffer of 8-bit type ids indicates which child
        a given logical value is to be taken from.

        In a sparse union, each child array should have the same length as the
        union array, regardless of the actual number of union values that
        refer to it.

        Parameters
        ----------
        child_fields : sequence of Field values
            Each field must have a UTF8-encoded name, and these field names are
            part of the type metadata.
        type_codes : list of integers, default None

        Returns
        -------
        type : SparseUnionType

    string()
        string()

        Create UTF8 variable-length string type.

        Examples
        --------
        Create an instance of a string type:

        >>> import pyarrow as pa
        >>> pa.string()
        DataType(string)

        and use the string type to create an array:

        >>> pa.array(['foo', 'bar', 'baz'], type=pa.string())
        <pyarrow.lib.StringArray object at ...>
        [
          "foo",
          "bar",
          "baz"
        ]

    string_to_tzinfo(name)
        string_to_tzinfo(name)

        Convert a time zone name into a time zone object.

        Supported input strings are:
        * As used in the Olson time zone database (the "tz database" or
          "tzdata"), such as "America/New_York"
        * An absolute time zone offset of the form +XX:XX or -XX:XX, such as +07:30

        Parameters
        ----------
          name: str
            Time zone name.

        Returns
        -------
          tz : datetime.tzinfo
            Time zone object

    string_view()
        string_view()

        Create UTF8 variable-length string view type.

        Examples
        --------
        Create an instance of a string type:

        >>> import pyarrow as pa
        >>> pa.string_view()
        DataType(string_view)

    struct(fields)
        struct(fields)

        Create StructType instance from fields.

        A struct is a nested type parameterized by an ordered sequence of types
        (which can all be distinct), called its fields.

        Parameters
        ----------
        fields : iterable of Fields or tuples, or mapping of strings to DataTypes
            Each field must have a UTF8-encoded name, and these field names are
            part of the type metadata.

        Examples
        --------
        Create an instance of StructType from an iterable of tuples:

        >>> import pyarrow as pa
        >>> fields = [
        ...     ('f1', pa.int32()),
        ...     ('f2', pa.string()),
        ... ]
        >>> struct_type = pa.struct(fields)
        >>> struct_type
        StructType(struct<f1: int32, f2: string>)

        Retrieve a field from a StructType:

        >>> struct_type[0]
        pyarrow.Field<f1: int32>
        >>> struct_type['f1']
        pyarrow.Field<f1: int32>

        Create an instance of StructType from an iterable of Fields:

        >>> fields = [
        ...     pa.field('f1', pa.int32()),
        ...     pa.field('f2', pa.string(), nullable=False),
        ... ]
        >>> pa.struct(fields)
        StructType(struct<f1: int32, f2: string not null>)

        Returns
        -------
        type : DataType

    supported_memory_backends()
        supported_memory_backends()

        Return a list of available memory pool backends

    system_memory_pool()
        system_memory_pool()

        Return a memory pool based on the C malloc heap.

    table(data, names=None, schema=None, metadata=None, nthreads=None)
        table(data, names=None, schema=None, metadata=None, nthreads=None)

        Create a pyarrow.Table from a Python data structure or sequence of arrays.

        Parameters
        ----------
        data : dict, list, pandas.DataFrame, Arrow-compatible table
            A mapping of strings to Arrays or Python lists, a list of arrays or
            chunked arrays, a pandas DataFame, or any tabular object implementing
            the Arrow PyCapsule Protocol (has an ``__arrow_c_array__``,
            ``__arrow_c_device_array__`` or ``__arrow_c_stream__`` method).
        names : list, default None
            Column names if list of arrays passed as data. Mutually exclusive with
            'schema' argument.
        schema : Schema, default None
            The expected schema of the Arrow Table. If not passed, will be inferred
            from the data. Mutually exclusive with 'names' argument.
            If passed, the output will have exactly this schema (raising an error
            when columns are not found in the data and ignoring additional data not
            specified in the schema, when data is a dict or DataFrame).
        metadata : dict or Mapping, default None
            Optional metadata for the schema (if schema not passed).
        nthreads : int, default None
            For pandas.DataFrame inputs: if greater than 1, convert columns to
            Arrow in parallel using indicated number of threads. By default,
            this follows :func:`pyarrow.cpu_count` (may use up to system CPU count
            threads).

        Returns
        -------
        Table

        See Also
        --------
        Table.from_arrays, Table.from_pandas, Table.from_pydict

        Examples
        --------
        >>> import pyarrow as pa
        >>> n_legs = pa.array([2, 4, 5, 100])
        >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
        >>> names = ["n_legs", "animals"]

        Construct a Table from a python dictionary:

        >>> pa.table({"n_legs": n_legs, "animals": animals})
        pyarrow.Table
        n_legs: int64
        animals: string
        ----
        n_legs: [[2,4,5,100]]
        animals: [["Flamingo","Horse","Brittle stars","Centipede"]]

        Construct a Table from arrays:

        >>> pa.table([n_legs, animals], names=names)
        pyarrow.Table
        n_legs: int64
        animals: string
        ----
        n_legs: [[2,4,5,100]]
        animals: [["Flamingo","Horse","Brittle stars","Centipede"]]

        Construct a Table from arrays with metadata:

        >>> my_metadata={"n_legs": "Number of legs per animal"}
        >>> pa.table([n_legs, animals], names=names, metadata = my_metadata).schema
        n_legs: int64
        animals: string
        -- schema metadata --
        n_legs: 'Number of legs per animal'

        Construct a Table from pandas DataFrame:

        >>> import pandas as pd
        >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
        ...                    'n_legs': [2, 4, 5, 100],
        ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
        >>> pa.table(df)
        pyarrow.Table
        year: int64
        n_legs: int64
        animals: large_string
        ----
        year: [[2020,2022,2019,2021]]
        n_legs: [[2,4,5,100]]
        animals: [["Flamingo","Horse","Brittle stars","Centipede"]]

        Construct a Table from pandas DataFrame with pyarrow schema:

        >>> my_schema = pa.schema([
        ...     pa.field('n_legs', pa.int64()),
        ...     pa.field('animals', pa.string())],
        ...     metadata={"n_legs": "Number of legs per animal"})
        >>> pa.table(df, my_schema).schema
        n_legs: int64
        animals: string
        -- schema metadata --
        n_legs: 'Number of legs per animal'
        pandas: '{"index_columns": [], "column_indexes": [{"name": null, ...

        Construct a Table from chunked arrays:

        >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
        >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]])
        >>> table = pa.table([n_legs, animals], names=names)
        >>> table
        pyarrow.Table
        n_legs: int64
        animals: string
        ----
        n_legs: [[2,2,4],[4,5,100]]
        animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]]

    table_to_blocks(options, table, categories, extension_columns)
        table_to_blocks(options, Table table, categories, extension_columns)

    time32(unit)
        time32(unit)

        Create instance of 32-bit time (time of day) type with unit resolution.

        Parameters
        ----------
        unit : str
            one of 's' [second], or 'ms' [millisecond]

        Returns
        -------
        type : pyarrow.Time32Type

        Examples
        --------
        >>> import pyarrow as pa
        >>> pa.time32('s')
        Time32Type(time32[s])
        >>> pa.time32('ms')
        Time32Type(time32[ms])

    time64(unit)
        time64(unit)

        Create instance of 64-bit time (time of day) type with unit resolution.

        Parameters
        ----------
        unit : str
            One of 'us' [microsecond], or 'ns' [nanosecond].

        Returns
        -------
        type : pyarrow.Time64Type

        Examples
        --------
        >>> import pyarrow as pa
        >>> pa.time64('us')
        Time64Type(time64[us])
        >>> pa.time64('ns')
        Time64Type(time64[ns])

    timestamp(unit, tz=None)
        timestamp(unit, tz=None)

        Create instance of timestamp type with resolution and optional time zone.

        Parameters
        ----------
        unit : str
            one of 's' [second], 'ms' [millisecond], 'us' [microsecond], or 'ns'
            [nanosecond]
        tz : str, default None
            Time zone name. None indicates time zone naive

        Examples
        --------
        Create an instance of timestamp type:

        >>> import pyarrow as pa
        >>> pa.timestamp('us')
        TimestampType(timestamp[us])
        >>> pa.timestamp('s', tz='America/New_York')
        TimestampType(timestamp[s, tz=America/New_York])
        >>> pa.timestamp('s', tz='+07:30')
        TimestampType(timestamp[s, tz=+07:30])

        Use timestamp type when creating a scalar object:

        >>> from datetime import datetime
        >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('s', tz='UTC'))
        <pyarrow.TimestampScalar: '2012-01-01T00:00:00+0000'>
        >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('us'))
        <pyarrow.TimestampScalar: '2012-01-01T00:00:00.000000'>

        Returns
        -------
        timestamp_type : TimestampType

    tobytes(o)
        tobytes(o)

        Encode a unicode or bytes string to bytes.

        Parameters
        ----------
        o : str or bytes
            Input string.

    total_allocated_bytes()
        total_allocated_bytes()

        Return the currently allocated bytes from the default memory pool.
        Other memory pools may not be accounted for.

    transcoding_input_stream(stream, src_encoding, dest_encoding)
        transcoding_input_stream(stream, src_encoding, dest_encoding)

        Add a transcoding transformation to the stream.
        Incoming data will be decoded according to ``src_encoding`` and
        then re-encoded according to ``dest_encoding``.

        Parameters
        ----------
        stream : NativeFile
            The stream to which the transformation should be applied.
        src_encoding : str
            The codec to use when reading data.
        dest_encoding : str
            The codec to use for emitted data.

    type_for_alias(name)
        type_for_alias(name)

        Return DataType given a string alias if one exists.

        Parameters
        ----------
        name : str
            The alias of the DataType that should be retrieved.

        Returns
        -------
        type : DataType

    tzinfo_to_string(tz)
        tzinfo_to_string(tz)

        Converts a time zone object into a string indicating the name of a time
        zone, one of:
        * As used in the Olson time zone database (the "tz database" or
          "tzdata"), such as "America/New_York"
        * An absolute time zone offset of the form +XX:XX or -XX:XX, such as +07:30

        Parameters
        ----------
          tz : datetime.tzinfo
            Time zone object

        Returns
        -------
          name : str
            Time zone name

    uint16()
        uint16()

        Create instance of unsigned uint16 type.

        Examples
        --------
        Create an instance of unsigned int16 type:

        >>> import pyarrow as pa
        >>> pa.uint16()
        DataType(uint16)
        >>> print(pa.uint16())
        uint16

        Create an array with unsigned int16 type:

        >>> pa.array([0, 1, 2], type=pa.uint16())
        <pyarrow.lib.UInt16Array object at ...>
        [
          0,
          1,
          2
        ]

    uint32()
        uint32()

        Create instance of unsigned uint32 type.

        Examples
        --------
        Create an instance of unsigned int32 type:

        >>> import pyarrow as pa
        >>> pa.uint32()
        DataType(uint32)
        >>> print(pa.uint32())
        uint32

        Create an array with unsigned int32 type:

        >>> pa.array([0, 1, 2], type=pa.uint32())
        <pyarrow.lib.UInt32Array object at ...>
        [
          0,
          1,
          2
        ]

    uint64()
        uint64()

        Create instance of unsigned uint64 type.

        Examples
        --------
        Create an instance of unsigned int64 type:

        >>> import pyarrow as pa
        >>> pa.uint64()
        DataType(uint64)
        >>> print(pa.uint64())
        uint64

        Create an array with unsigned uint64 type:

        >>> pa.array([0, 1, 2], type=pa.uint64())
        <pyarrow.lib.UInt64Array object at ...>
        [
          0,
          1,
          2
        ]

    uint8()
        uint8()

        Create instance of unsigned int8 type.

        Examples
        --------
        Create an instance of unsigned int8 type:

        >>> import pyarrow as pa
        >>> pa.uint8()
        DataType(uint8)
        >>> print(pa.uint8())
        uint8

        Create an array with unsigned int8 type:

        >>> pa.array([0, 1, 2], type=pa.uint8())
        <pyarrow.lib.UInt8Array object at ...>
        [
          0,
          1,
          2
        ]

    unify_schemas(schemas, *, promote_options='default')
        unify_schemas(schemas, *, promote_options='default')

        Unify schemas by merging fields by name.

        The resulting schema will contain the union of fields from all schemas.
        Fields with the same name will be merged. Note that two fields with
        different types will fail merging by default.

        - The unified field will inherit the metadata from the schema where
            that field is first defined.
        - The first N fields in the schema will be ordered the same as the
            N fields in the first schema.

        The resulting schema will inherit its metadata from the first input
        schema.

        Parameters
        ----------
        schemas : list of Schema
            Schemas to merge into a single one.
        promote_options : str, default default
            Accepts strings "default" and "permissive".
            Default: null and only null can be unified with another type.
            Permissive: types are promoted to the greater common denominator.

        Returns
        -------
        Schema

        Raises
        ------
        ArrowInvalid :
            If any input schema contains fields with duplicate names.
            If Fields of the same name are not mergeable.

    union(child_fields, mode, type_codes=None)
        union(child_fields, mode, type_codes=None)

        Create UnionType from child fields.

        A union is a nested type where each logical value is taken from a
        single child.  A buffer of 8-bit type ids indicates which child
        a given logical value is to be taken from.

        Unions come in two flavors: sparse and dense
        (see also `pyarrow.sparse_union` and `pyarrow.dense_union`).

        Parameters
        ----------
        child_fields : sequence of Field values
            Each field must have a UTF8-encoded name, and these field names are
            part of the type metadata.
        mode : str
            Must be 'sparse' or 'dense'
        type_codes : list of integers, default None

        Returns
        -------
        type : UnionType

    unregister_extension_type(type_name)
        unregister_extension_type(type_name)

        Unregister a Python extension type.

        Parameters
        ----------
        type_name : str
            The name of the ExtensionType subclass to unregister.

        Examples
        --------
        Define a RationalType extension type subclassing ExtensionType:

        >>> import pyarrow as pa
        >>> class RationalType(pa.ExtensionType):
        ...     def __init__(self, data_type: pa.DataType):
        ...         if not pa.types.is_integer(data_type):
        ...             raise TypeError(f"data_type must be an integer type not {data_type}")
        ...         super().__init__(
        ...             pa.struct(
        ...                 [
        ...                     ("numer", data_type),
        ...                     ("denom", data_type),
        ...                 ],
        ...             ),
        ...             # N.B. This name does _not_ reference `data_type` so deserialization
        ...             # will work for _any_ integer `data_type` after registration
        ...             "my_package.rational",
        ...         )
        ...     def __arrow_ext_serialize__(self) -> bytes:
        ...         # No parameters are necessary
        ...         return b""
        ...     @classmethod
        ...     def __arrow_ext_deserialize__(cls, storage_type, serialized):
        ...         # return an instance of this subclass
        ...         return RationalType(storage_type[0].type)

        Register the extension type:

        >>> pa.register_extension_type(RationalType(pa.int64()))

        Unregister the extension type:

        >>> pa.unregister_extension_type("my_package.rational")

    utf8()
        utf8()

        Alias for string().

        Examples
        --------
        Create an instance of a string type:

        >>> import pyarrow as pa
        >>> pa.utf8()
        DataType(string)

        and use the string type to create an array:

        >>> pa.array(['foo', 'bar', 'baz'], type=pa.utf8())
        <pyarrow.lib.StringArray object at ...>
        [
          "foo",
          "bar",
          "baz"
        ]

    uuid()
        uuid()

        Create UuidType instance.

        Returns
        -------
        type : UuidType

    write_tensor(tensor, dest)
        write_tensor(Tensor tensor, NativeFile dest)

        Write pyarrow.Tensor to pyarrow.NativeFile object its current position.

        Parameters
        ----------
        tensor : pyarrow.Tensor
        dest : pyarrow.NativeFile

        Returns
        -------
        bytes_written : int
            Total number of bytes written to the file

DATA
    DEFAULT_BUFFER_SIZE = 65536
    NA = <pyarrow.NullScalar: None>
    Type_BINARY = 14
    Type_BINARY_VIEW = 40
    Type_BOOL = 1
    Type_DATE32 = 16
    Type_DATE64 = 17
    Type_DECIMAL128 = 23
    Type_DECIMAL256 = 24
    Type_DECIMAL32 = 43
    Type_DECIMAL64 = 44
    Type_DENSE_UNION = 28
    Type_DICTIONARY = 29
    Type_DOUBLE = 12
    Type_DURATION = 33
    Type_FIXED_SIZE_BINARY = 15
    Type_FIXED_SIZE_LIST = 32
    Type_FLOAT = 11
    Type_HALF_FLOAT = 10
    Type_INT16 = 5
    Type_INT32 = 7
    Type_INT64 = 9
    Type_INT8 = 3
    Type_INTERVAL_DAY_TIME = 22
    Type_INTERVAL_MONTHS = 21
    Type_INTERVAL_MONTH_DAY_NANO = 37
    Type_LARGE_BINARY = 35
    Type_LARGE_LIST = 36
    Type_LARGE_LIST_VIEW = 42
    Type_LARGE_STRING = 34
    Type_LIST = 25
    Type_LIST_VIEW = 41
    Type_MAP = 30
    Type_NA = 0
    Type_RUN_END_ENCODED = 38
    Type_SPARSE_UNION = 27
    Type_STRING = 13
    Type_STRING_VIEW = 39
    Type_STRUCT = 26
    Type_TIME32 = 19
    Type_TIME64 = 20
    Type_TIMESTAMP = 18
    Type_UINT16 = 4
    Type_UINT32 = 6
    Type_UINT64 = 8
    Type_UINT8 = 2
    UnionMode_DENSE = 1
    UnionMode_SPARSE = 0
    __pyx_capi__ = {'_parse_field_merge_options': <capsule object " arrow:...
    __test__ = {'Array.diff (line 1127)': '\n        Compare contents of t...
    build_info = BuildInfo(build_type='release', cpp_build_info=C...nd='py...
    cpp_build_info = CppBuildInfo(version='24.0.0', version_info=Vers...in...
    cpp_version = '24.0.0'
    cpp_version_info = VersionInfo(major=24, minor=0, patch=0)
    have_signal_refcycle = False

FILE
    c:\users\youse\appdata\local\programs\python\python312\lib\site-packages\pyarrow\lib.cp312-win_amd64.pyd

Python Library Documentation: cython_function_or_method in module pyarrow.lib

list_(value_type, list_size=-1)
    list_(value_type, int list_size=-1)

    Create ListType instance from child data type or field.

    Parameters
    ----------
    value_type : DataType or Field
    list_size : int, optional, default -1
        If length == -1 then return a variable length list type. If length is
        greater than or equal to 0 then return a fixed size list type.

    Returns
    -------
    list_type : DataType

    Examples
    --------
    Create an instance of ListType:

    >>> import pyarrow as pa
    >>> pa.list_(pa.string())
    ListType(list<item: string>)
    >>> pa.list_(pa.int32(), 2)
    FixedSizeListType(fixed_size_list<item: int32>[2])

    Use the ListType to create a scalar:

    >>> pa.scalar(['foo', None], type=pa.list_(pa.string(), 2))
    <pyarrow.FixedSizeListScalar: ['foo', None]>

    or an array:

    >>> pa.array([[1, 2], [3, 4]], pa.list_(pa.int32(), 2))
    <pyarrow.lib.FixedSizeListArray object at ...>
    [
      [
        1,
        2
      ],
      [
        3,
        4
      ]
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

list_view(value_type)
    list_view(value_type) -> ListViewType

    Create ListViewType instance from child data type or field.

    This data type may not be supported by all Arrow implementations
    because it is an alternative to the ListType.

    Parameters
    ----------
    value_type : DataType or Field

    Returns
    -------
    list_view_type : DataType

    Examples
    --------
    Create an instance of ListViewType:

    >>> import pyarrow as pa
    >>> pa.list_view(pa.string())
    ListViewType(list_view<item: string>)

Python Library Documentation: cython_function_or_method in module pyarrow.lib

log_memory_allocations(enable=True)
    log_memory_allocations(enable=True)

    Enable or disable memory allocator logging for debugging purposes

    Parameters
    ----------
    enable : bool, default True
        Pass False to disable logging

Python Library Documentation: cython_function_or_method in module pyarrow.lib

logging_memory_pool(parent)
    logging_memory_pool(MemoryPool parent)

    Create and return a MemoryPool instance that redirects to the
    *parent*, but also dumps allocation logs on stderr.

    Parameters
    ----------
    parent : MemoryPool
        The real memory pool that should be used for allocations.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

map_(key_type, item_type, keys_sorted=False)
    map_(key_type, item_type, keys_sorted=False) -> MapType

    Create MapType instance from key and item data types or fields.

    Parameters
    ----------
    key_type : DataType or Field
    item_type : DataType or Field
    keys_sorted : bool

    Returns
    -------
    map_type : DataType

    Examples
    --------
    Create an instance of MapType:

    >>> import pyarrow as pa
    >>> pa.map_(pa.string(), pa.int32())
    MapType(map<string, int32>)
    >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True)
    MapType(map<string, int32, keys_sorted>)

    Use MapType to create an array:

    >>> data = [[{'key': 'a', 'value': 1}, {'key': 'b', 'value': 2}], [{'key': 'c', 'value': 3}]]
    >>> pa.array(data, type=pa.map_(pa.string(), pa.int32(), keys_sorted=True))
    <pyarrow.lib.MapArray object at ...>
    [
      keys:
      [
        "a",
        "b"
      ]
      values:
      [
        1,
        2
      ],
      keys:
      [
        "c"
      ]
      values:
      [
        3
      ]
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

memory_map(path, mode='r')
    memory_map(path, mode='r')

    Open memory map at file path. Size of the memory map cannot change.

    Parameters
    ----------
    path : str
    mode : {'r', 'r+', 'w'}, default 'r'
        Whether the file is opened for reading ('r'), writing ('w')
        or both ('r+').

    Returns
    -------
    mmap : MemoryMappedFile

    Examples
    --------
    Reading from a memory map without any memory allocation or copying:

    >>> import pyarrow as pa
    >>> with pa.output_stream('example_mmap.txt') as stream:
    ...     stream.write(b'Constructing a buffer referencing the mapped memory')
    ...
    51
    >>> with pa.memory_map('example_mmap.txt') as mmap:
    ...     mmap.read_at(6,45)
    ...
    b'memory'

Python Library Documentation: cython_function_or_method in module pyarrow.lib

mimalloc_memory_pool()
    mimalloc_memory_pool()

    Return a memory pool based on the mimalloc heap.

    NotImplementedError is raised if mimalloc support is not enabled.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

month_day_nano_interval()
    month_day_nano_interval()

    Create instance of an interval type representing months, days and
    nanoseconds between two dates.

    Examples
    --------
    Create an instance of an month_day_nano_interval type:

    >>> import pyarrow as pa
    >>> pa.month_day_nano_interval()
    DataType(month_day_nano_interval)

    Create a scalar with month_day_nano_interval type:

    >>> pa.scalar((1, 15, -30), type=pa.month_day_nano_interval())
    <pyarrow.MonthDayNanoIntervalScalar: MonthDayNano(months=1, days=15, nanoseconds=-30)>

Python Library Documentation: cython_function_or_method in module pyarrow.lib

null()
    null()

    Create instance of null type.

    Examples
    --------
    Create an instance of a null type:

    >>> import pyarrow as pa
    >>> pa.null()
    DataType(null)
    >>> print(pa.null())
    null

    Create a ``Field`` type with a null type and a name:

    >>> pa.field('null_field', pa.null())
    pyarrow.Field<null_field: null>

Python Library Documentation: cython_function_or_method in module pyarrow.lib

nulls(size, type=None, memory_pool=None)
    nulls(size, type=None, MemoryPool memory_pool=None)

    Create a strongly-typed Array instance with all elements null.

    Parameters
    ----------
    size : int
        Array length.
    type : pyarrow.DataType, default None
        Explicit type for the array. By default use NullType.
    memory_pool : MemoryPool, default None
        Arrow MemoryPool to use for allocations. Uses the default memory
        pool if not passed.

    Returns
    -------
    arr : Array

    Examples
    --------
    >>> import pyarrow as pa
    >>> pa.nulls(10)
    <pyarrow.lib.NullArray object at ...>
    10 nulls

    >>> pa.nulls(3, pa.uint32())
    <pyarrow.lib.UInt32Array object at ...>
    [
      null,
      null,
      null
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

opaque(storage_type, type_name, vendor_name)
    opaque(DataType storage_type, str type_name, str vendor_name)

    Create instance of opaque extension type.

    Parameters
    ----------
    storage_type : DataType
        The underlying data type.
    type_name : str
        The name of the type in the external system.
    vendor_name : str
        The name of the external system.

    Examples
    --------
    Create an instance of an opaque extension type:

    >>> import pyarrow as pa
    >>> type = pa.opaque(pa.binary(), "other", "jdbc")
    >>> type
    OpaqueType(extension<arrow.opaque[storage_type=binary, type_name=other, vendor_name=jdbc]>)

    Inspect the data type:

    >>> type.storage_type
    DataType(binary)
    >>> type.type_name
    'other'
    >>> type.vendor_name
    'jdbc'

    Create a table with an opaque array:

    >>> arr = [None, b"foobar"]
    >>> storage = pa.array(arr, pa.binary())
    >>> other = pa.ExtensionArray.from_storage(type, storage)
    >>> pa.table([other], names=["unknown_col"])
    pyarrow.Table
    unknown_col: extension<arrow.opaque[storage_type=binary, type_name=other, vendor_name=jdbc]>
    ----
    unknown_col: [[null,666F6F626172]]

    Returns
    -------
    type : OpaqueType

Python Library Documentation: cython_function_or_method in module pyarrow.lib

output_stream(source, compression='detect', buffer_size=None)
    output_stream(source, compression='detect', buffer_size=None)

    Create an Arrow output stream.

    Parameters
    ----------
    source : str, Path, buffer, file-like object
        The source to open for writing.
    compression : str optional, default 'detect'
        The compression algorithm to use for on-the-fly compression.
        If "detect" and source is a file path, then compression will be
        chosen based on the file extension.
        If None, no compression will be applied.
        Otherwise, a well-known algorithm name must be supplied (e.g. "gzip").
    buffer_size : int, default None
        If None or 0, no buffering will happen. Otherwise the size of the
        temporary write buffer.

    Examples
    --------
    Create a writable NativeFile from a pyarrow Buffer:

    >>> import pyarrow as pa
    >>> data = b"buffer data"
    >>> empty_obj = bytearray(11)
    >>> buf = pa.py_buffer(empty_obj)
    >>> with pa.output_stream(buf) as stream:
    ...     stream.write(data)
    ...
    11
    >>> with pa.input_stream(buf) as stream:
    ...     stream.read(6)
    ...
    b'buffer'

    or from a memoryview object:

    >>> buf = memoryview(empty_obj)
    >>> with pa.output_stream(buf) as stream:
    ...     stream.write(data)
    ...
    11
    >>> with pa.input_stream(buf) as stream:
    ...     stream.read()
    ...
    b'buffer data'

    Create a writable NativeFile from a string or file path:

    >>> with pa.output_stream('example_second.txt') as stream:
    ...     stream.write(b'Write some data')
    ...
    15
    >>> with pa.input_stream('example_second.txt') as stream:
    ...     stream.read()
    ...
    b'Write some data'

Python Library Documentation: cython_function_or_method in module pyarrow.lib

proxy_memory_pool(parent)
    proxy_memory_pool(MemoryPool parent)

    Create and return a MemoryPool instance that redirects to the
    *parent*, but with separate allocation statistics.

    Parameters
    ----------
    parent : MemoryPool
        The real memory pool that should be used for allocations.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

py_buffer(obj)
    py_buffer(obj)

    Construct an Arrow buffer from a Python bytes-like or buffer-like object

    Parameters
    ----------
    obj : object
        the object from which the buffer should be constructed.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

record_batch(data, names=None, schema=None, metadata=None)
    record_batch(data, names=None, schema=None, metadata=None)

    Create a pyarrow.RecordBatch from another Python data structure or sequence
    of arrays.

    Parameters
    ----------
    data : dict, list, pandas.DataFrame, Arrow-compatible table
        A mapping of strings to Arrays or Python lists, a list of Arrays,
        a pandas DataFame, or any tabular object implementing the
        Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or
        ``__arrow_c_device_array__`` method).
    names : list, default None
        Column names if list of arrays passed as data. Mutually exclusive with
        'schema' argument.
    schema : Schema, default None
        The expected schema of the RecordBatch. If not passed, will be inferred
        from the data. Mutually exclusive with 'names' argument.
    metadata : dict or Mapping, default None
        Optional metadata for the schema (if schema not passed).

    Returns
    -------
    RecordBatch

    See Also
    --------
    RecordBatch.from_arrays, RecordBatch.from_pandas, table

    Examples
    --------
    >>> import pyarrow as pa
    >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
    >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
    >>> names = ["n_legs", "animals"]

    Construct a RecordBatch from a python dictionary:

    >>> pa.record_batch({"n_legs": n_legs, "animals": animals})
    pyarrow.RecordBatch
    n_legs: int64
    animals: string
    ----
    n_legs: [2,2,4,4,5,100]
    animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
    >>> pa.record_batch({"n_legs": n_legs, "animals": animals}).to_pandas()
       n_legs        animals
    0       2       Flamingo
    1       2         Parrot
    2       4            Dog
    3       4          Horse
    4       5  Brittle stars
    5     100      Centipede

    Creating a RecordBatch from a list of arrays with names:

    >>> pa.record_batch([n_legs, animals], names=names)
    pyarrow.RecordBatch
    n_legs: int64
    animals: string
    ----
    n_legs: [2,2,4,4,5,100]
    animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]

    Creating a RecordBatch from a list of arrays with names and metadata:

    >>> my_metadata={"n_legs": "How many legs does an animal have?"}
    >>> pa.record_batch([n_legs, animals],
    ...                  names=names,
    ...                  metadata = my_metadata)
    pyarrow.RecordBatch
    n_legs: int64
    animals: string
    ----
    n_legs: [2,2,4,4,5,100]
    animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
    >>> pa.record_batch([n_legs, animals],
    ...                  names=names,
    ...                  metadata = my_metadata).schema
    n_legs: int64
    animals: string
    -- schema metadata --
    n_legs: 'How many legs does an animal have?'

    Creating a RecordBatch from a pandas DataFrame:

    >>> import pandas as pd
    >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022],
    ...                    'month': [3, 5, 7, 9],
    ...                    'day': [1, 5, 9, 13],
    ...                    'n_legs': [2, 4, 5, 100],
    ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
    >>> pa.record_batch(df)
    pyarrow.RecordBatch
    year: int64
    month: int64
    day: int64
    n_legs: int64
    animals: large_string
    ----
    year: [2020,2022,2021,2022]
    month: [3,5,7,9]
    day: [1,5,9,13]
    n_legs: [2,4,5,100]
    animals: ["Flamingo","Horse","Brittle stars","Centipede"]

    >>> pa.record_batch(df).to_pandas()
       year  month  day  n_legs        animals
    0  2020      3    1       2       Flamingo
    1  2022      5    5       4          Horse
    2  2021      7    9       5  Brittle stars
    3  2022      9   13     100      Centipede

    Creating a RecordBatch from a pandas DataFrame with schema:

    >>> my_schema = pa.schema([
    ...     pa.field('n_legs', pa.int64()),
    ...     pa.field('animals', pa.string())],
    ...     metadata={"n_legs": "Number of legs per animal"})
    >>> pa.record_batch(df, my_schema).schema
    n_legs: int64
    animals: string
    -- schema metadata --
    n_legs: 'Number of legs per animal'
    pandas: ...
    >>> pa.record_batch(df, my_schema).to_pandas()
       n_legs        animals
    0       2       Flamingo
    1       4          Horse
    2       5  Brittle stars
    3     100      Centipede

Python Library Documentation: cython_function_or_method in module pyarrow.lib

register_extension_type(ext_type)
    register_extension_type(ext_type)

    Register a Python extension type.

    Registration is based on the extension name (so different registered types
    need unique extension names). Registration needs an extension type
    instance, but then works for any instance of the same subclass regardless
    of parametrization of the type.

    Parameters
    ----------
    ext_type : BaseExtensionType instance
        The ExtensionType subclass to register.

    Examples
    --------
    Define a RationalType extension type subclassing ExtensionType:

    >>> import pyarrow as pa
    >>> class RationalType(pa.ExtensionType):
    ...     def __init__(self, data_type: pa.DataType):
    ...         if not pa.types.is_integer(data_type):
    ...             raise TypeError(f"data_type must be an integer type not {data_type}")
    ...         super().__init__(
    ...             pa.struct(
    ...                 [
    ...                     ("numer", data_type),
    ...                     ("denom", data_type),
    ...                 ],
    ...             ),
    ...             # N.B. This name does _not_ reference `data_type` so deserialization
    ...             # will work for _any_ integer `data_type` after registration
    ...             "my_package.rational",
    ...         )
    ...     def __arrow_ext_serialize__(self) -> bytes:
    ...         # No parameters are necessary
    ...         return b""
    ...     @classmethod
    ...     def __arrow_ext_deserialize__(cls, storage_type, serialized):
    ...         # return an instance of this subclass
    ...         return RationalType(storage_type[0].type)

    Register the extension type:

    >>> pa.register_extension_type(RationalType(pa.int64()))

    Unregister the extension type:

    >>> pa.unregister_extension_type("my_package.rational")

Python Library Documentation: cython_function_or_method in module pyarrow.lib

repeat(value, size, memory_pool=None)
    repeat(value, size, MemoryPool memory_pool=None)

    Create an Array instance whose slots are the given scalar.

    Parameters
    ----------
    value : Scalar-like object
        Either a pyarrow.Scalar or any python object coercible to a Scalar.
    size : int
        Number of times to repeat the scalar in the output Array.
    memory_pool : MemoryPool, default None
        Arrow MemoryPool to use for allocations. Uses the default memory
        pool if not passed.

    Returns
    -------
    arr : Array

    Examples
    --------
    >>> import pyarrow as pa
    >>> pa.repeat(10, 3)
    <pyarrow.lib.Int64Array object at ...>
    [
      10,
      10,
      10
    ]

    >>> pa.repeat([1, 2], 2)
    <pyarrow.lib.ListArray object at ...>
    [
      [
        1,
        2
      ],
      [
        1,
        2
      ]
    ]

    >>> pa.repeat("string", 3)
    <pyarrow.lib.StringArray object at ...>
    [
      "string",
      "string",
      "string"
    ]

    >>> pa.repeat(pa.scalar({'a': 1, 'b': [1, 2]}), 2)
    <pyarrow.lib.StructArray object at ...>
    -- is_valid: all not null
    -- child 0 type: int64
      [
        1,
        1
      ]
    -- child 1 type: list<item: int64>
      [
        [
          1,
          2
        ],
        [
          1,
          2
        ]
      ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

run_end_encoded(run_end_type, value_type)
    run_end_encoded(run_end_type, value_type)

    Create RunEndEncodedType from run-end and value types.

    Parameters
    ----------
    run_end_type : pyarrow.DataType
        The integer type of the run_ends array. Must be 'int16', 'int32', or 'int64'.
    value_type : pyarrow.DataType
        The type of the values array.

    Returns
    -------
    type : RunEndEncodedType

Python Library Documentation: cython_function_or_method in module pyarrow.lib

runtime_info()
    runtime_info()

    Get runtime information.

    Returns
    -------
    info : pyarrow.RuntimeInfo

Python Library Documentation: cython_function_or_method in module pyarrow.lib

scalar(value, type=None, *, from_pandas=None, memory_pool=None)
    scalar(value, type=None, *, from_pandas=None, MemoryPool memory_pool=None)

    Create a pyarrow.Scalar instance from a Python object.

    Parameters
    ----------
    value : Any
        Python object coercible to arrow's type system.
    type : pyarrow.DataType
        Explicit type to attempt to coerce to, otherwise will be inferred from
        the value.
    from_pandas : bool, default None
        Use pandas's semantics for inferring nulls from values in
        ndarray-like data. Defaults to False if not passed explicitly by user,
        or True if a pandas object is passed in.
    memory_pool : pyarrow.MemoryPool, optional
        If not passed, will allocate memory from the currently-set default
        memory pool.

    Returns
    -------
    scalar : pyarrow.Scalar

    Examples
    --------
    >>> import pyarrow as pa

    >>> pa.scalar(42)
    <pyarrow.Int64Scalar: 42>

    >>> pa.scalar("string")
    <pyarrow.StringScalar: 'string'>

    >>> pa.scalar([1, 2])
    <pyarrow.ListScalar: [1, 2]>

    >>> pa.scalar([1, 2], type=pa.list_(pa.int16()))
    <pyarrow.ListScalar: [1, 2]>

Python Library Documentation: cython_function_or_method in module pyarrow.lib

schema(fields, metadata=None)
    schema(fields, metadata=None)

    Construct pyarrow.Schema from collection of fields.

    Parameters
    ----------
    fields : iterable of Fields or tuples, or mapping of strings to DataTypes
        Can also pass an object that implements the Arrow PyCapsule Protocol
        for schemas (has an ``__arrow_c_schema__`` method).
    metadata : dict, default None
        Keys and values must be coercible to bytes.

    Examples
    --------
    Create a Schema from iterable of tuples:

    >>> import pyarrow as pa
    >>> pa.schema([
    ...     ('some_int', pa.int32()),
    ...     ('some_string', pa.string()),
    ...     pa.field('some_required_string', pa.string(), nullable=False)
    ... ])
    some_int: int32
    some_string: string
    some_required_string: string not null

    Create a Schema from iterable of Fields:

    >>> pa.schema([
    ...     pa.field('some_int', pa.int32()),
    ...     pa.field('some_string', pa.string())
    ... ])
    some_int: int32
    some_string: string

    DataTypes can also be passed as strings. The following is equivalent to the
    above example:

    >>> pa.schema([
    ...     pa.field('some_int', "int32"),
    ...     pa.field('some_string', "string")
    ... ])
    some_int: int32
    some_string: string

    Or more concisely:

    >>> pa.schema([
    ...     ('some_int', "int32"),
    ...     ('some_string', "string")
    ... ])
    some_int: int32
    some_string: string

    Returns
    -------
    schema : pyarrow.Schema

Python Library Documentation: function serialize_pandas in module pyarrow.ipc

serialize_pandas(df, *, nthreads=None, preserve_index=None)
    Serialize a pandas DataFrame into a buffer protocol compatible object.

    Parameters
    ----------
    df : pandas.DataFrame
    nthreads : int, default None
        Number of threads to use for conversion to Arrow, default all CPUs.
    preserve_index : bool, default None
        The default of None will store the index as a column, except for
        RangeIndex which is stored as metadata only. If True, always
        preserve the pandas index data as a column. If False, no index
        information is saved and the result will have a default RangeIndex.

    Returns
    -------
    buf : buffer
        An object compatible with the buffer protocol.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

set_cpu_count(count)
    set_cpu_count(int count)

    Set the number of threads to use in parallel operations.

    Parameters
    ----------
    count : int
        The number of concurrent threads that should be used.

    See Also
    --------
    cpu_count : Get the size of this pool.
    set_io_thread_count : The analogous function for the I/O thread pool.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

set_io_thread_count(count)
    set_io_thread_count(int count)

    Set the number of threads to use for I/O operations.

    Many operations, such as scanning a dataset, will implicitly make
    use of this pool.

    Parameters
    ----------
    count : int
        The max number of threads that may be used for I/O.
        Must be positive.

    See Also
    --------
    io_thread_count : Get the size of this pool.
    set_cpu_count : The analogous function for the CPU thread pool.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

set_memory_pool(pool)
    set_memory_pool(MemoryPool pool)

    Set the default memory pool.

    Parameters
    ----------
    pool : MemoryPool
        The memory pool that should be used by default.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

set_timezone_db_path(path)
    set_timezone_db_path(path)

    Configure the path to text timezone database on Windows.

    .. deprecated:: 24.0.0
       This function is deprecated and will be removed in a future version.
       PyArrow now uses the operating system's timezone database on Windows.

    Parameters
    ----------
    path : str
        Path to text timezone database.

Python Library Documentation: function show_info in module pyarrow

show_info()
    Print detailed version and platform information, for error reporting

Python Library Documentation: function show_versions in module pyarrow

show_versions()
    Print various version information, to help with error reporting.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

sparse_union(child_fields, type_codes=None)
    sparse_union(child_fields, type_codes=None)

    Create SparseUnionType from child fields.

    A sparse union is a nested type where each logical value is taken from
    a single child.  A buffer of 8-bit type ids indicates which child
    a given logical value is to be taken from.

    In a sparse union, each child array should have the same length as the
    union array, regardless of the actual number of union values that
    refer to it.

    Parameters
    ----------
    child_fields : sequence of Field values
        Each field must have a UTF8-encoded name, and these field names are
        part of the type metadata.
    type_codes : list of integers, default None

    Returns
    -------
    type : SparseUnionType

Python Library Documentation: cython_function_or_method in module pyarrow.lib

string()
    string()

    Create UTF8 variable-length string type.

    Examples
    --------
    Create an instance of a string type:

    >>> import pyarrow as pa
    >>> pa.string()
    DataType(string)

    and use the string type to create an array:

    >>> pa.array(['foo', 'bar', 'baz'], type=pa.string())
    <pyarrow.lib.StringArray object at ...>
    [
      "foo",
      "bar",
      "baz"
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

string_view()
    string_view()

    Create UTF8 variable-length string view type.

    Examples
    --------
    Create an instance of a string type:

    >>> import pyarrow as pa
    >>> pa.string_view()
    DataType(string_view)

Python Library Documentation: cython_function_or_method in module pyarrow.lib

struct(fields)
    struct(fields)

    Create StructType instance from fields.

    A struct is a nested type parameterized by an ordered sequence of types
    (which can all be distinct), called its fields.

    Parameters
    ----------
    fields : iterable of Fields or tuples, or mapping of strings to DataTypes
        Each field must have a UTF8-encoded name, and these field names are
        part of the type metadata.

    Examples
    --------
    Create an instance of StructType from an iterable of tuples:

    >>> import pyarrow as pa
    >>> fields = [
    ...     ('f1', pa.int32()),
    ...     ('f2', pa.string()),
    ... ]
    >>> struct_type = pa.struct(fields)
    >>> struct_type
    StructType(struct<f1: int32, f2: string>)

    Retrieve a field from a StructType:

    >>> struct_type[0]
    pyarrow.Field<f1: int32>
    >>> struct_type['f1']
    pyarrow.Field<f1: int32>

    Create an instance of StructType from an iterable of Fields:

    >>> fields = [
    ...     pa.field('f1', pa.int32()),
    ...     pa.field('f2', pa.string(), nullable=False),
    ... ]
    >>> pa.struct(fields)
    StructType(struct<f1: int32, f2: string not null>)

    Returns
    -------
    type : DataType

Python Library Documentation: cython_function_or_method in module pyarrow.lib

supported_memory_backends()
    supported_memory_backends()

    Return a list of available memory pool backends

Python Library Documentation: cython_function_or_method in module pyarrow.lib

system_memory_pool()
    system_memory_pool()

    Return a memory pool based on the C malloc heap.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

table(data, names=None, schema=None, metadata=None, nthreads=None)
    table(data, names=None, schema=None, metadata=None, nthreads=None)

    Create a pyarrow.Table from a Python data structure or sequence of arrays.

    Parameters
    ----------
    data : dict, list, pandas.DataFrame, Arrow-compatible table
        A mapping of strings to Arrays or Python lists, a list of arrays or
        chunked arrays, a pandas DataFame, or any tabular object implementing
        the Arrow PyCapsule Protocol (has an ``__arrow_c_array__``,
        ``__arrow_c_device_array__`` or ``__arrow_c_stream__`` method).
    names : list, default None
        Column names if list of arrays passed as data. Mutually exclusive with
        'schema' argument.
    schema : Schema, default None
        The expected schema of the Arrow Table. If not passed, will be inferred
        from the data. Mutually exclusive with 'names' argument.
        If passed, the output will have exactly this schema (raising an error
        when columns are not found in the data and ignoring additional data not
        specified in the schema, when data is a dict or DataFrame).
    metadata : dict or Mapping, default None
        Optional metadata for the schema (if schema not passed).
    nthreads : int, default None
        For pandas.DataFrame inputs: if greater than 1, convert columns to
        Arrow in parallel using indicated number of threads. By default,
        this follows :func:`pyarrow.cpu_count` (may use up to system CPU count
        threads).

    Returns
    -------
    Table

    See Also
    --------
    Table.from_arrays, Table.from_pandas, Table.from_pydict

    Examples
    --------
    >>> import pyarrow as pa
    >>> n_legs = pa.array([2, 4, 5, 100])
    >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
    >>> names = ["n_legs", "animals"]

    Construct a Table from a python dictionary:

    >>> pa.table({"n_legs": n_legs, "animals": animals})
    pyarrow.Table
    n_legs: int64
    animals: string
    ----
    n_legs: [[2,4,5,100]]
    animals: [["Flamingo","Horse","Brittle stars","Centipede"]]

    Construct a Table from arrays:

    >>> pa.table([n_legs, animals], names=names)
    pyarrow.Table
    n_legs: int64
    animals: string
    ----
    n_legs: [[2,4,5,100]]
    animals: [["Flamingo","Horse","Brittle stars","Centipede"]]

    Construct a Table from arrays with metadata:

    >>> my_metadata={"n_legs": "Number of legs per animal"}
    >>> pa.table([n_legs, animals], names=names, metadata = my_metadata).schema
    n_legs: int64
    animals: string
    -- schema metadata --
    n_legs: 'Number of legs per animal'

    Construct a Table from pandas DataFrame:

    >>> import pandas as pd
    >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
    ...                    'n_legs': [2, 4, 5, 100],
    ...                    'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
    >>> pa.table(df)
    pyarrow.Table
    year: int64
    n_legs: int64
    animals: large_string
    ----
    year: [[2020,2022,2019,2021]]
    n_legs: [[2,4,5,100]]
    animals: [["Flamingo","Horse","Brittle stars","Centipede"]]

    Construct a Table from pandas DataFrame with pyarrow schema:

    >>> my_schema = pa.schema([
    ...     pa.field('n_legs', pa.int64()),
    ...     pa.field('animals', pa.string())],
    ...     metadata={"n_legs": "Number of legs per animal"})
    >>> pa.table(df, my_schema).schema
    n_legs: int64
    animals: string
    -- schema metadata --
    n_legs: 'Number of legs per animal'
    pandas: '{"index_columns": [], "column_indexes": [{"name": null, ...

    Construct a Table from chunked arrays:

    >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
    >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]])
    >>> table = pa.table([n_legs, animals], names=names)
    >>> table
    pyarrow.Table
    n_legs: int64
    animals: string
    ----
    n_legs: [[2,2,4],[4,5,100]]
    animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

time32(unit)
    time32(unit)

    Create instance of 32-bit time (time of day) type with unit resolution.

    Parameters
    ----------
    unit : str
        one of 's' [second], or 'ms' [millisecond]

    Returns
    -------
    type : pyarrow.Time32Type

    Examples
    --------
    >>> import pyarrow as pa
    >>> pa.time32('s')
    Time32Type(time32[s])
    >>> pa.time32('ms')
    Time32Type(time32[ms])

Python Library Documentation: cython_function_or_method in module pyarrow.lib

time64(unit)
    time64(unit)

    Create instance of 64-bit time (time of day) type with unit resolution.

    Parameters
    ----------
    unit : str
        One of 'us' [microsecond], or 'ns' [nanosecond].

    Returns
    -------
    type : pyarrow.Time64Type

    Examples
    --------
    >>> import pyarrow as pa
    >>> pa.time64('us')
    Time64Type(time64[us])
    >>> pa.time64('ns')
    Time64Type(time64[ns])

Python Library Documentation: cython_function_or_method in module pyarrow.lib

timestamp(unit, tz=None)
    timestamp(unit, tz=None)

    Create instance of timestamp type with resolution and optional time zone.

    Parameters
    ----------
    unit : str
        one of 's' [second], 'ms' [millisecond], 'us' [microsecond], or 'ns'
        [nanosecond]
    tz : str, default None
        Time zone name. None indicates time zone naive

    Examples
    --------
    Create an instance of timestamp type:

    >>> import pyarrow as pa
    >>> pa.timestamp('us')
    TimestampType(timestamp[us])
    >>> pa.timestamp('s', tz='America/New_York')
    TimestampType(timestamp[s, tz=America/New_York])
    >>> pa.timestamp('s', tz='+07:30')
    TimestampType(timestamp[s, tz=+07:30])

    Use timestamp type when creating a scalar object:

    >>> from datetime import datetime
    >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('s', tz='UTC'))
    <pyarrow.TimestampScalar: '2012-01-01T00:00:00+0000'>
    >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('us'))
    <pyarrow.TimestampScalar: '2012-01-01T00:00:00.000000'>

    Returns
    -------
    timestamp_type : TimestampType

Python Library Documentation: cython_function_or_method in module pyarrow.lib

total_allocated_bytes()
    total_allocated_bytes()

    Return the currently allocated bytes from the default memory pool.
    Other memory pools may not be accounted for.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

transcoding_input_stream(stream, src_encoding, dest_encoding)
    transcoding_input_stream(stream, src_encoding, dest_encoding)

    Add a transcoding transformation to the stream.
    Incoming data will be decoded according to ``src_encoding`` and
    then re-encoded according to ``dest_encoding``.

    Parameters
    ----------
    stream : NativeFile
        The stream to which the transformation should be applied.
    src_encoding : str
        The codec to use when reading data.
    dest_encoding : str
        The codec to use for emitted data.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

type_for_alias(name)
    type_for_alias(name)

    Return DataType given a string alias if one exists.

    Parameters
    ----------
    name : str
        The alias of the DataType that should be retrieved.

    Returns
    -------
    type : DataType

Python Library Documentation: module pyarrow.types in pyarrow

NAME
    pyarrow.types

DESCRIPTION
    # Licensed to the Apache Software Foundation (ASF) under one
    # or more contributor license agreements.  See the NOTICE file
    # distributed with this work for additional information
    # regarding copyright ownership.  The ASF licenses this file
    # to you under the Apache License, Version 2.0 (the
    # "License"); you may not use this file except in compliance
    # with the License.  You may obtain a copy of the License at
    #
    #   http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing,
    # software distributed under the License is distributed on an
    # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    # KIND, either express or implied.  See the License for the
    # specific language governing permissions and limitations
    # under the License.

CLASSES
    enum.IntEnum(builtins.int, enum.ReprEnum)
        TypesEnum

    class TypesEnum(enum.IntEnum)
     |  TypesEnum(*values)
     |
     |  An Enum that maps constant values to data types.
     |  Exposes the underlying data types representation for type checking purposes.
     |  Note that some of the types listed here are not supported by PyArrow yet:
     |  INTERVAL_MONTHS and INTERVAL_DAY_TIME.
     |
     |  Examples
     |  --------
     |  >>> import pyarrow as pa
     |  >>> from pyarrow.types import TypesEnum
     |  >>> int8_field = pa.field('int8_field', pa.int8())
     |  >>> int8_field.type.id == TypesEnum.INT8
     |  True
     |
     |  >>> fixed_size_list = pa.list_(pa.uint16(), 3)
     |  >>> fixed_size_list.id == TypesEnum.LIST
     |  False
     |
     |  >>> fixed_size_list.id == TypesEnum.FIXED_SIZE_LIST
     |  True
     |
     |  Method resolution order:
     |      TypesEnum
     |      enum.IntEnum
     |      builtins.int
     |      enum.ReprEnum
     |      enum.Enum
     |      builtins.object
     |
     |  Methods defined here:
     |
     |  __format__(self, format_spec, /) from builtins.int
     |      Convert to a string according to format_spec.
     |
     |  __new__(cls, value) from enum.Enum
     |      Create and return a new object.  See help(type) for accurate signature.
     |
     |  ----------------------------------------------------------------------
     |  Data and other attributes defined here:
     |
     |  BINARY = <TypesEnum.BINARY: 14>
     |
     |  BINARY_VIEW = <TypesEnum.BINARY_VIEW: 40>
     |
     |  BOOL = <TypesEnum.BOOL: 1>
     |
     |  DATE32 = <TypesEnum.DATE32: 16>
     |
     |  DATE64 = <TypesEnum.DATE64: 17>
     |
     |  DECIMAL128 = <TypesEnum.DECIMAL128: 23>
     |
     |  DECIMAL256 = <TypesEnum.DECIMAL256: 24>
     |
     |  DECIMAL32 = <TypesEnum.DECIMAL32: 43>
     |
     |  DECIMAL64 = <TypesEnum.DECIMAL64: 44>
     |
     |  DENSE_UNION = <TypesEnum.DENSE_UNION: 28>
     |
     |  DICTIONARY = <TypesEnum.DICTIONARY: 29>
     |
     |  DOUBLE = <TypesEnum.DOUBLE: 12>
     |
     |  DURATION = <TypesEnum.DURATION: 33>
     |
     |  FIXED_SIZE_BINARY = <TypesEnum.FIXED_SIZE_BINARY: 15>
     |
     |  FIXED_SIZE_LIST = <TypesEnum.FIXED_SIZE_LIST: 32>
     |
     |  FLOAT = <TypesEnum.FLOAT: 11>
     |
     |  HALF_FLOAT = <TypesEnum.HALF_FLOAT: 10>
     |
     |  INT16 = <TypesEnum.INT16: 5>
     |
     |  INT32 = <TypesEnum.INT32: 7>
     |
     |  INT64 = <TypesEnum.INT64: 9>
     |
     |  INT8 = <TypesEnum.INT8: 3>
     |
     |  INTERVAL_DAY_TIME = <TypesEnum.INTERVAL_DAY_TIME: 22>
     |
     |  INTERVAL_MONTHS = <TypesEnum.INTERVAL_MONTHS: 21>
     |
     |  INTERVAL_MONTH_DAY_NANO = <TypesEnum.INTERVAL_MONTH_DAY_NANO: 37>
     |
     |  LARGE_BINARY = <TypesEnum.LARGE_BINARY: 35>
     |
     |  LARGE_LIST = <TypesEnum.LARGE_LIST: 36>
     |
     |  LARGE_LIST_VIEW = <TypesEnum.LARGE_LIST_VIEW: 42>
     |
     |  LARGE_STRING = <TypesEnum.LARGE_STRING: 34>
     |
     |  LIST = <TypesEnum.LIST: 25>
     |
     |  LIST_VIEW = <TypesEnum.LIST_VIEW: 41>
     |
     |  MAP = <TypesEnum.MAP: 30>
     |
     |  NA = <TypesEnum.NA: 0>
     |
     |  RUN_END_ENCODED = <TypesEnum.RUN_END_ENCODED: 38>
     |
     |  SPARSE_UNION = <TypesEnum.SPARSE_UNION: 27>
     |
     |  STRING = <TypesEnum.STRING: 13>
     |
     |  STRING_VIEW = <TypesEnum.STRING_VIEW: 39>
     |
     |  STRUCT = <TypesEnum.STRUCT: 26>
     |
     |  TIME32 = <TypesEnum.TIME32: 19>
     |
     |  TIME64 = <TypesEnum.TIME64: 20>
     |
     |  TIMESTAMP = <TypesEnum.TIMESTAMP: 18>
     |
     |  UINT16 = <TypesEnum.UINT16: 4>
     |
     |  UINT32 = <TypesEnum.UINT32: 6>
     |
     |  UINT64 = <TypesEnum.UINT64: 8>
     |
     |  UINT8 = <TypesEnum.UINT8: 2>
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from enum.IntEnum:
     |
     |  __repr__(self) from enum.Enum
     |      Return repr(self).
     |
     |  __str__ = __repr__(self, /) from builtins.int
     |      Return repr(self).
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from builtins.int:
     |
     |  __abs__(self, /)
     |      abs(self)
     |
     |  __add__(self, value, /)
     |      Return self+value.
     |
     |  __and__(self, value, /)
     |      Return self&value.
     |
     |  __bool__(self, /)
     |      True if self else False
     |
     |  __ceil__(...)
     |      Ceiling of an Integral returns itself.
     |
     |  __divmod__(self, value, /)
     |      Return divmod(self, value).
     |
     |  __eq__(self, value, /)
     |      Return self==value.
     |
     |  __float__(self, /)
     |      float(self)
     |
     |  __floor__(...)
     |      Flooring an Integral returns itself.
     |
     |  __floordiv__(self, value, /)
     |      Return self//value.
     |
     |  __ge__(self, value, /)
     |      Return self>=value.
     |
     |  __getattribute__(self, name, /)
     |      Return getattr(self, name).
     |
     |  __getnewargs__(self, /)
     |
     |  __gt__(self, value, /)
     |      Return self>value.
     |
     |  __hash__(self, /)
     |      Return hash(self).
     |
     |  __index__(self, /)
     |      Return self converted to an integer, if self is suitable for use as an index into a list.
     |
     |  __int__(self, /)
     |      int(self)
     |
     |  __invert__(self, /)
     |      ~self
     |
     |  __le__(self, value, /)
     |      Return self<=value.
     |
     |  __lshift__(self, value, /)
     |      Return self<<value.
     |
     |  __lt__(self, value, /)
     |      Return self<value.
     |
     |  __mod__(self, value, /)
     |      Return self%value.
     |
     |  __mul__(self, value, /)
     |      Return self*value.
     |
     |  __ne__(self, value, /)
     |      Return self!=value.
     |
     |  __neg__(self, /)
     |      -self
     |
     |  __or__(self, value, /)
     |      Return self|value.
     |
     |  __pos__(self, /)
     |      +self
     |
     |  __pow__(self, value, mod=None, /)
     |      Return pow(self, value, mod).
     |
     |  __radd__(self, value, /)
     |      Return value+self.
     |
     |  __rand__(self, value, /)
     |      Return value&self.
     |
     |  __rdivmod__(self, value, /)
     |      Return divmod(value, self).
     |
     |  __rfloordiv__(self, value, /)
     |      Return value//self.
     |
     |  __rlshift__(self, value, /)
     |      Return value<<self.
     |
     |  __rmod__(self, value, /)
     |      Return value%self.
     |
     |  __rmul__(self, value, /)
     |      Return value*self.
     |
     |  __ror__(self, value, /)
     |      Return value|self.
     |
     |  __round__(...)
     |      Rounding an Integral returns itself.
     |
     |      Rounding with an ndigits argument also returns an integer.
     |
     |  __rpow__(self, value, mod=None, /)
     |      Return pow(value, self, mod).
     |
     |  __rrshift__(self, value, /)
     |      Return value>>self.
     |
     |  __rshift__(self, value, /)
     |      Return self>>value.
     |
     |  __rsub__(self, value, /)
     |      Return value-self.
     |
     |  __rtruediv__(self, value, /)
     |      Return value/self.
     |
     |  __rxor__(self, value, /)
     |      Return value^self.
     |
     |  __sizeof__(self, /)
     |      Returns size in memory, in bytes.
     |
     |  __sub__(self, value, /)
     |      Return self-value.
     |
     |  __truediv__(self, value, /)
     |      Return self/value.
     |
     |  __trunc__(...)
     |      Truncating an Integral returns itself.
     |
     |  __xor__(self, value, /)
     |      Return self^value.
     |
     |  as_integer_ratio(self, /)
     |      Return a pair of integers, whose ratio is equal to the original int.
     |
     |      The ratio is in lowest terms and has a positive denominator.
     |
     |      >>> (10).as_integer_ratio()
     |      (10, 1)
     |      >>> (-10).as_integer_ratio()
     |      (-10, 1)
     |      >>> (0).as_integer_ratio()
     |      (0, 1)
     |
     |  bit_count(self, /)
     |      Number of ones in the binary representation of the absolute value of self.
     |
     |      Also known as the population count.
     |
     |      >>> bin(13)
     |      '0b1101'
     |      >>> (13).bit_count()
     |      3
     |
     |  bit_length(self, /)
     |      Number of bits necessary to represent self in binary.
     |
     |      >>> bin(37)
     |      '0b100101'
     |      >>> (37).bit_length()
     |      6
     |
     |  conjugate(...)
     |      Returns self, the complex conjugate of any int.
     |
     |  is_integer(self, /)
     |      Returns True. Exists for duck type compatibility with float.is_integer.
     |
     |  to_bytes(self, /, length=1, byteorder='big', *, signed=False)
     |      Return an array of bytes representing an integer.
     |
     |      length
     |        Length of bytes object to use.  An OverflowError is raised if the
     |        integer is not representable with the given number of bytes.  Default
     |        is length 1.
     |      byteorder
     |        The byte order used to represent the integer.  If byteorder is 'big',
     |        the most significant byte is at the beginning of the byte array.  If
     |        byteorder is 'little', the most significant byte is at the end of the
     |        byte array.  To request the native byte order of the host system, use
     |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
     |      signed
     |        Determines whether two's complement is used to represent the integer.
     |        If signed is False and a negative integer is given, an OverflowError
     |        is raised.
     |
     |  ----------------------------------------------------------------------
     |  Class methods inherited from builtins.int:
     |
     |  from_bytes(bytes, byteorder='big', *, signed=False)
     |      Return the integer represented by the given array of bytes.
     |
     |      bytes
     |        Holds the array of bytes to convert.  The argument must either
     |        support the buffer protocol or be an iterable object producing bytes.
     |        Bytes and bytearray are examples of built-in objects that support the
     |        buffer protocol.
     |      byteorder
     |        The byte order used to represent the integer.  If byteorder is 'big',
     |        the most significant byte is at the beginning of the byte array.  If
     |        byteorder is 'little', the most significant byte is at the end of the
     |        byte array.  To request the native byte order of the host system, use
     |        `sys.byteorder' as the byte order value.  Default is to use 'big'.
     |      signed
     |        Indicates whether two's complement is used to represent the integer.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from builtins.int:
     |
     |  denominator
     |      the denominator of a rational number in lowest terms
     |
     |  imag
     |      the imaginary part of a complex number
     |
     |  numerator
     |      the numerator of a rational number in lowest terms
     |
     |  real
     |      the real part of a complex number
     |
     |  ----------------------------------------------------------------------
     |  Methods inherited from enum.Enum:
     |
     |  __dir__(self)
     |      Returns public methods and other interesting attributes.
     |
     |  __init__(self, *args, **kwds)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |
     |  __reduce_ex__(self, proto)
     |      Helper for pickle.
     |
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from enum.Enum:
     |
     |  name
     |      The name of the Enum member.
     |
     |  value
     |      The value of the Enum member.
     |
     |  ----------------------------------------------------------------------
     |  Static methods inherited from enum.EnumType:
     |
     |  __contains__(value)
     |      Return True if `value` is in `cls`.
     |
     |      `value` is in `cls` if:
     |      1) `value` is a member of `cls`, or
     |      2) `value` is the value of one of the `cls`'s members.
     |
     |  __getitem__(name)
     |      Return the member matching `name`.
     |
     |  __iter__()
     |      Return members in definition order.
     |
     |  __len__()
     |      Return the number of members (no aliases)
     |
     |  ----------------------------------------------------------------------
     |  Readonly properties inherited from enum.EnumType:
     |
     |  __members__
     |      Returns a mapping of member name->value.
     |
     |      This mapping lists all enum members, including aliases. Note that this
     |      is a read-only view of the internal mapping.

FUNCTIONS
    is_binary(t)
        Return True if value is an instance of type: variable-length binary.

        Parameters
        ----------
        t : DataType

    is_binary_view(t)
        Return True if value is an instance of type: variable-length binary view.

        Parameters
        ----------
        t : DataType

    is_boolean(t)
        Return True if value is an instance of type: boolean.

        Parameters
        ----------
        t : DataType

    is_date(t)
        Return True if value is an instance of type: date.

        Parameters
        ----------
        t : DataType

    is_date32(t)
        Return True if value is an instance of type: date32 (days).

        Parameters
        ----------
        t : DataType

    is_date64(t)
        Return True if value is an instance of type: date64 (milliseconds).

        Parameters
        ----------
        t : DataType

    is_decimal(t)
        Return True if value is an instance of type: decimal.

        Parameters
        ----------
        t : DataType

    is_decimal128(t)
        Return True if value is an instance of type: decimal128.

        Parameters
        ----------
        t : DataType

    is_decimal256(t)
        Return True if value is an instance of type: decimal256.

        Parameters
        ----------
        t : DataType

    is_decimal32(t)
        Return True if value is an instance of type: decimal32.

        Parameters
        ----------
        t : DataType

    is_decimal64(t)
        Return True if value is an instance of type: decimal64.

        Parameters
        ----------
        t : DataType

    is_dictionary(t)
        Return True if value is an instance of type: dictionary-encoded.

        Parameters
        ----------
        t : DataType

    is_duration(t)
        Return True if value is an instance of type: duration.

        Parameters
        ----------
        t : DataType

    is_fixed_size_binary(t)
        Return True if value is an instance of type: fixed size binary.

        Parameters
        ----------
        t : DataType

    is_fixed_size_list(t)
        Return True if value is an instance of type: fixed size list.

        Parameters
        ----------
        t : DataType

    is_float16(t)
        Return True if value is an instance of type: float16 (half-precision).

        Parameters
        ----------
        t : DataType

    is_float32(t)
        Return True if value is an instance of type: float32 (single precision).

        Parameters
        ----------
        t : DataType

    is_float64(t)
        Return True if value is an instance of type: float64 (double precision).

        Parameters
        ----------
        t : DataType

    is_floating(t)
        Return True if value is an instance of type: floating point numeric.

        Parameters
        ----------
        t : DataType

    is_int16(t)
        Return True if value is an instance of type: int16.

        Parameters
        ----------
        t : DataType

    is_int32(t)
        Return True if value is an instance of type: int32.

        Parameters
        ----------
        t : DataType

    is_int64(t)
        Return True if value is an instance of type: int64.

        Parameters
        ----------
        t : DataType

    is_int8(t)
        Return True if value is an instance of type: int8.

        Parameters
        ----------
        t : DataType

    is_integer(t)
        Return True if value is an instance of type: any integer.

        Parameters
        ----------
        t : DataType

    is_interval(t)
        Return True if value is an instance of type: interval.

        Parameters
        ----------
        t : DataType

    is_large_binary(t)
        Return True if value is an instance of type: large variable-length binary.

        Parameters
        ----------
        t : DataType

    is_large_list(t)
        Return True if value is an instance of type: large list.

        Parameters
        ----------
        t : DataType

    is_large_list_view(t)
        Return True if value is an instance of type: large list view.

        Parameters
        ----------
        t : DataType

    is_large_string(t)
        Return True if value is an instance of type: large string (utf8 unicode).

        Parameters
        ----------
        t : DataType

    is_large_unicode(t)
        Alias for is_large_string.

        Parameters
        ----------
        t : DataType

    is_list(t)
        Return True if value is an instance of type: list.

        Parameters
        ----------
        t : DataType

    is_list_view(t)
        Return True if value is an instance of type: list view.

        Parameters
        ----------
        t : DataType

    is_map(t)
        Return True if value is an instance of type: map.

        Parameters
        ----------
        t : DataType

    is_nested(t)
        Return True if value is an instance of type: nested type.

        Parameters
        ----------
        t : DataType

    is_null(t)
        Return True if value is an instance of type: null.

        Parameters
        ----------
        t : DataType

    is_primitive(t)
        Return True if value is an instance of type: primitive type.

        Parameters
        ----------
        t : DataType

    is_run_end_encoded(t)
        Return True if value is an instance of type: run-end encoded.

        Parameters
        ----------
        t : DataType

    is_signed_integer(t)
        Return True if value is an instance of type: signed integer.

        Parameters
        ----------
        t : DataType

    is_string(t)
        Return True if value is an instance of type: string (utf8 unicode).

        Parameters
        ----------
        t : DataType

    is_string_view(t)
        Return True if value is an instance of type: variable-length string (utf-8) view.

        Parameters
        ----------
        t : DataType

    is_struct(t)
        Return True if value is an instance of type: struct.

        Parameters
        ----------
        t : DataType

    is_temporal(t)
        Return True if value is an instance of type: date, time, timestamp or duration.

        Parameters
        ----------
        t : DataType

    is_time(t)
        Return True if value is an instance of type: time.

        Parameters
        ----------
        t : DataType

    is_time32(t)
        Return True if value is an instance of type: time32.

        Parameters
        ----------
        t : DataType

    is_time64(t)
        Return True if value is an instance of type: time64.

        Parameters
        ----------
        t : DataType

    is_timestamp(t)
        Return True if value is an instance of type: timestamp.

        Parameters
        ----------
        t : DataType

    is_uint16(t)
        Return True if value is an instance of type: uint16.

        Parameters
        ----------
        t : DataType

    is_uint32(t)
        Return True if value is an instance of type: uint32.

        Parameters
        ----------
        t : DataType

    is_uint64(t)
        Return True if value is an instance of type: uint64.

        Parameters
        ----------
        t : DataType

    is_uint8(t)
        Return True if value is an instance of type: uint8.

        Parameters
        ----------
        t : DataType

    is_unicode(t)
        Alias for is_string.

        Parameters
        ----------
        t : DataType

    is_union(t)
        Return True if value is an instance of type: union.

        Parameters
        ----------
        t : DataType

    is_unsigned_integer(t)
        Return True if value is an instance of type: unsigned integer.

        Parameters
        ----------
        t : DataType

FILE
    c:\users\youse\appdata\local\programs\python\python312\lib\site-packages\pyarrow\types.py

Python Library Documentation: cython_function_or_method in module pyarrow.lib

uint16()
    uint16()

    Create instance of unsigned uint16 type.

    Examples
    --------
    Create an instance of unsigned int16 type:

    >>> import pyarrow as pa
    >>> pa.uint16()
    DataType(uint16)
    >>> print(pa.uint16())
    uint16

    Create an array with unsigned int16 type:

    >>> pa.array([0, 1, 2], type=pa.uint16())
    <pyarrow.lib.UInt16Array object at ...>
    [
      0,
      1,
      2
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

uint32()
    uint32()

    Create instance of unsigned uint32 type.

    Examples
    --------
    Create an instance of unsigned int32 type:

    >>> import pyarrow as pa
    >>> pa.uint32()
    DataType(uint32)
    >>> print(pa.uint32())
    uint32

    Create an array with unsigned int32 type:

    >>> pa.array([0, 1, 2], type=pa.uint32())
    <pyarrow.lib.UInt32Array object at ...>
    [
      0,
      1,
      2
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

uint64()
    uint64()

    Create instance of unsigned uint64 type.

    Examples
    --------
    Create an instance of unsigned int64 type:

    >>> import pyarrow as pa
    >>> pa.uint64()
    DataType(uint64)
    >>> print(pa.uint64())
    uint64

    Create an array with unsigned uint64 type:

    >>> pa.array([0, 1, 2], type=pa.uint64())
    <pyarrow.lib.UInt64Array object at ...>
    [
      0,
      1,
      2
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

uint8()
    uint8()

    Create instance of unsigned int8 type.

    Examples
    --------
    Create an instance of unsigned int8 type:

    >>> import pyarrow as pa
    >>> pa.uint8()
    DataType(uint8)
    >>> print(pa.uint8())
    uint8

    Create an array with unsigned int8 type:

    >>> pa.array([0, 1, 2], type=pa.uint8())
    <pyarrow.lib.UInt8Array object at ...>
    [
      0,
      1,
      2
    ]

Python Library Documentation: cython_function_or_method in module pyarrow.lib

unify_schemas(schemas, *, promote_options='default')
    unify_schemas(schemas, *, promote_options='default')

    Unify schemas by merging fields by name.

    The resulting schema will contain the union of fields from all schemas.
    Fields with the same name will be merged. Note that two fields with
    different types will fail merging by default.

    - The unified field will inherit the metadata from the schema where
        that field is first defined.
    - The first N fields in the schema will be ordered the same as the
        N fields in the first schema.

    The resulting schema will inherit its metadata from the first input
    schema.

    Parameters
    ----------
    schemas : list of Schema
        Schemas to merge into a single one.
    promote_options : str, default default
        Accepts strings "default" and "permissive".
        Default: null and only null can be unified with another type.
        Permissive: types are promoted to the greater common denominator.

    Returns
    -------
    Schema

    Raises
    ------
    ArrowInvalid :
        If any input schema contains fields with duplicate names.
        If Fields of the same name are not mergeable.

Python Library Documentation: cython_function_or_method in module pyarrow.lib

union(child_fields, mode, type_codes=None)
    union(child_fields, mode, type_codes=None)

    Create UnionType from child fields.

    A union is a nested type where each logical value is taken from a
    single child.  A buffer of 8-bit type ids indicates which child
    a given logical value is to be taken from.

    Unions come in two flavors: sparse and dense
    (see also `pyarrow.sparse_union` and `pyarrow.dense_union`).

    Parameters
    ----------
    child_fields : sequence of Field values
        Each field must have a UTF8-encoded name, and these field names are
        part of the type metadata.
    mode : str
        Must be 'sparse' or 'dense'
    type_codes : list of integers, default None

    Returns
    -------
    type : UnionType

Python Library Documentation: cython_function_or_method in module pyarrow.lib

unregister_extension_type(type_name)
    unregister_extension_type(type_name)

    Unregister a Python extension type.

    Parameters
    ----------
    type_name : str
        The name of the ExtensionType subclass to unregister.

    Examples
    --------
    Define a RationalType extension type subclassing ExtensionType:

    >>> import pyarrow as pa
    >>> class RationalType(pa.ExtensionType):
    ...     def __init__(self, data_type: pa.DataType):
    ...         if not pa.types.is_integer(data_type):
    ...             raise TypeError(f"data_type must be an integer type not {data_type}")
    ...         super().__init__(
    ...             pa.struct(
    ...                 [
    ...                     ("numer", data_type),
    ...                     ("denom", data_type),
    ...                 ],
    ...             ),
    ...             # N.B. This name does _not_ reference `data_type` so deserialization
    ...             # will work for _any_ integer `data_type` after registration
    ...             "my_package.rational",
    ...         )
    ...     def __arrow_ext_serialize__(self) -> bytes:
    ...         # No parameters are necessary
    ...         return b""
    ...     @classmethod
    ...     def __arrow_ext_deserialize__(cls, storage_type, serialized):
    ...         # return an instance of this subclass
    ...         return RationalType(storage_type[0].type)

    Register the extension type:

    >>> pa.register_extension_type(RationalType(pa.int64()))

    Unregister the extension type:

    >>> pa.unregister_extension_type("my_package.rational")

Python Library Documentation: cython_function_or_method in module pyarrow.lib

utf8()
    utf8()

    Alias for string().

    Examples
    --------
    Create an instance of a string type:

    >>> import pyarrow as pa
    >>> pa.utf8()
    DataType(string)

    and use the string type to create an array:

    >>> pa.array(['foo', 'bar', 'baz'], type=pa.utf8())
    <pyarrow.lib.StringArray object at ...>
    [
      "foo",
      "bar",
      "baz"
    ]

Python Library Documentation: module pyarrow.util in pyarrow

NAME
    pyarrow.util

DESCRIPTION
    # Licensed to the Apache Software Foundation (ASF) under one
    # or more contributor license agreements.  See the NOTICE file
    # distributed with this work for additional information
    # regarding copyright ownership.  The ASF licenses this file
    # to you under the Apache License, Version 2.0 (the
    # "License"); you may not use this file except in compliance
    # with the License.  You may obtain a copy of the License at
    #
    #   http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing,
    # software distributed under the License is distributed on an
    # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    # KIND, either express or implied.  See the License for the
    # specific language governing permissions and limitations
    # under the License.

FUNCTIONS
    doc(*docstrings, **params)
        A decorator that takes docstring templates, concatenates them, and finally
        performs string substitution on them.
        This decorator will add a variable "_docstring_components" to the wrapped
        callable to keep track of the original docstring template for potential future use.
        If the docstring is a template, it will be saved as a string.
        Otherwise, it will be saved as a callable and the docstring will be obtained via
        the __doc__ attribute.
        This decorator cannot be used on Cython classes due to a CPython constraint,
        which enforces the __doc__ attribute to be read-only.
        See https://github.com/python/cpython/issues/91309

        Parameters
        ----------
        *docstrings : None, str, or callable
            The string / docstring / docstring template to be prepended in order
            before the default docstring under the callable.
        **params
            The key/value pairs used to format the docstring template.

    download_tzdata_on_windows()
        Download and extract latest IANA timezone database into the
        location expected by Arrow which is %USERPROFILE%\Downloads\tzdata.

        .. deprecated:: 24.0.0
           This function is deprecated and will be removed in a future version.
           PyArrow now uses the operating system's timezone database on Windows.

    find_free_port()

    get_contiguous_span(shape, strides, itemsize)
        Return a contiguous span of N-D array data.

        Parameters
        ----------
        shape : tuple
        strides : tuple
        itemsize : int
          Specify array shape data

        Returns
        -------
        start, end : int
          The span end points.

    guid()

    product(seq)
        Return a product of sequence items.

FILE
    c:\users\youse\appdata\local\programs\python\python312\lib\site-packages\pyarrow\util.py

Python Library Documentation: cython_function_or_method in module pyarrow.lib

uuid()
    uuid()

    Create UuidType instance.

    Returns
    -------
    type : UuidType