statsmodels API Reference

Python Library Documentation: package statsmodels.compat in statsmodels

NAME
    statsmodels.compat

PACKAGE CONTENTS
    _scipy_multivariate_t
    numpy
    pandas
    patsy
    platform
    pytest
    python
    scipy
    tests (package)

FUNCTIONS
    asbytes(s)

    asstr(s)

    asunicode lambda x, _

    lfilter(*args, **kwargs)

    lmap(*args, **kwargs)

    lrange(*args, **kwargs)
        # list-producing versions of the major Python iterating functions

    lzip(*args, **kwargs)

DATA
    __all__ = ['asunicode', 'asbytes', 'asstr', 'lrange', 'lzip', 'lmap', ...
    test = <statsmodels.tools._test_runner.PytestTester object>

FILE
    c:\users\youse\appdata\local\programs\python\python312\lib\site-packages\statsmodels\compat\__init__.py

Python Library Documentation: bool object

class bool(int)
 |  bool(x) -> bool
 |
 |  Returns True when the argument x is true, False otherwise.
 |  The builtins True and False are the only two instances of the class bool.
 |  The class bool is a subclass of the class int, and cannot be subclassed.
 |
 |  Method resolution order:
 |      bool
 |      int
 |      object
 |
 |  Methods defined here:
 |
 |  __and__(self, value, /)
 |      Return self&value.
 |
 |  __invert__(self, /)
 |      ~self
 |
 |  __or__(self, value, /)
 |      Return self|value.
 |
 |  __rand__(self, value, /)
 |      Return value&self.
 |
 |  __repr__(self, /)
 |      Return repr(self).
 |
 |  __ror__(self, value, /)
 |      Return value|self.
 |
 |  __rxor__(self, value, /)
 |      Return value^self.
 |
 |  __xor__(self, value, /)
 |      Return self^value.
 |
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |
 |  __new__(*args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from 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.
 |
 |  __format__(self, format_spec, /)
 |      Convert to a string according to format_spec.
 |
 |  __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 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 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

Python Library Documentation: function monkey_patch_cat_dtype in module statsmodels.compat.patsy

monkey_patch_cat_dtype()

Python Library Documentation: function test in module statsmodels

test(extra_args=None, exit=False)
    Run the test suite

    Parameters
    ----------
    extra_args : list[str]
        List of argument to pass to pytest when running the test suite. The
        default is ['--tb=short', '--disable-pytest-warnings'].
    exit : bool
        Flag indicating whether the test runner should exit when finished.

    Returns
    -------
    int
        The status code from the test run if exit is False.

Python Library Documentation: package statsmodels.tools in statsmodels

NAME
    statsmodels.tools

PACKAGE CONTENTS
    _test_runner
    _testing
    catadd
    data
    decorators
    docstring
    eval_measures
    grouputils
    linalg
    numdiff
    parallel
    print_version
    rng_qrng
    rootfinding
    sequences
    sm_exceptions
    testing
    tests (package)
    tools
    transform_model
    typing
    validation (package)
    web

FUNCTIONS
    add_constant(data, prepend=True, has_constant='skip')
        Add a column of ones to an array.

        Parameters
        ----------
        data : array_like
            A column-ordered design matrix.
        prepend : bool
            If true, the constant is in the first column.  Else the constant is
            appended (last column).
        has_constant : str {'raise', 'add', 'skip'}
            Behavior if ``data`` already has a constant. The default will return
            data without adding another constant. If 'raise', will raise an
            error if any column has a constant value. Using 'add' will add a
            column of 1s if a constant column is present.

        Returns
        -------
        array_like
            The original values with a constant (column of ones) as the first or
            last column. Returned value type depends on input type.

        Notes
        -----
        When the input is a pandas Series or DataFrame, the added column's name
        is 'const'.

    categorical(data, col=None, dictnames=False, drop=False)
        Construct a dummy matrix from categorical variables

        .. deprecated:: 0.12

           Use pandas.get_dummies instead.

        Parameters
        ----------
        data : array_like
            An array, Series or DataFrame.  This can be either a 1d vector of
            the categorical variable or a 2d array with the column specifying
            the categorical variable specified by the col argument.
        col : {str, int, None}
            If data is a DataFrame col must in a column of data. If data is a
            Series, col must be either the name of the Series or None. For arrays,
            `col` can be an int that is the (zero-based) column index
            number.  `col` can only be None for a 1d array.  The default is None.
        dictnames : bool, optional
            If True, a dictionary mapping the column number to the categorical
            name is returned.  Used to have information about plain arrays.
        drop : bool
            Whether or not keep the categorical variable in the returned matrix.

        Returns
        -------
        dummy_matrix : array_like
            A matrix of dummy (indicator/binary) float variables for the
            categorical data.
        dictnames :  dict[int, str], optional
            Mapping between column numbers and categorical names.

        Notes
        -----
        This returns a dummy variable for *each* distinct variable.  If a
        a DaataFrame is provided, the names for the new variable is the
        old variable name - underscore - category name.  So if the a variable
        'vote' had answers as 'yes' or 'no' then the returned array would have to
        new variables-- 'vote_yes' and 'vote_no'.  There is currently
        no name checking.

        Examples
        --------
        >>> import numpy as np
        >>> import statsmodels.api as sm

        Univariate examples

        >>> import string
        >>> string_var = [string.ascii_lowercase[0:5],
        ...               string.ascii_lowercase[5:10],
        ...               string.ascii_lowercase[10:15],
        ...               string.ascii_lowercase[15:20],
        ...               string.ascii_lowercase[20:25]]
        >>> string_var *= 5
        >>> string_var = np.asarray(sorted(string_var))
        >>> design = sm.tools.categorical(string_var, drop=True)

        Or for a numerical categorical variable

        >>> instr = np.floor(np.arange(10,60, step=2)/10)
        >>> design = sm.tools.categorical(instr, drop=True)

        With a structured array

        >>> num = np.random.randn(25,2)
        >>> struct_ar = np.zeros((25,1),
        ...                      dtype=[('var1', 'f4'),('var2', 'f4'),
        ...                             ('instrument','f4'),('str_instr','a5')])
        >>> struct_ar['var1'] = num[:,0][:,None]
        >>> struct_ar['var2'] = num[:,1][:,None]
        >>> struct_ar['instrument'] = instr[:,None]
        >>> struct_ar['str_instr'] = string_var[:,None]
        >>> design = sm.tools.categorical(struct_ar, col='instrument', drop=True)

        Or

        >>> design2 = sm.tools.categorical(struct_ar, col='str_instr', drop=True)

DATA
    __all__ = ['test', 'add_constant', 'categorical']
    test = <statsmodels.tools._test_runner.PytestTester object>

FILE
    c:\users\youse\appdata\local\programs\python\python312\lib\site-packages\statsmodels\tools\__init__.py