random API Reference
Full documentation for random (Standard Library / Native)
random.BPF
Python Library Documentation: int object
class int(object)
| int([x]) -> integer
| int(x, base=10) -> integer
|
| Convert a number or string to an integer, or return 0 if no arguments
| are given. If x is a number, return x.__int__(). For floating-point
| numbers, this truncates towards zero.
|
| If x is not a number or if base is given, then x must be a string,
| bytes, or bytearray instance representing an integer literal in the
| given base. The literal can be preceded by '+' or '-' and be surrounded
| by whitespace. The base defaults to 10. Valid bases are 0 and 2-36.
| Base 0 means to interpret the base from the string as an integer literal.
| >>> int('0b100', base=0)
| 4
|
| Built-in subclasses:
| bool
|
| Methods defined here:
|
| __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.
|
| __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)
|
| __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).
|
| __repr__(self, /)
| Return repr(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 defined here:
|
| 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.
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs)
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| 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
random.LOG4
Python Library Documentation: float object
class float(object)
| float(x=0, /)
|
| Convert a string or number to a floating-point number, if possible.
|
| Methods defined here:
|
| __abs__(self, /)
| abs(self)
|
| __add__(self, value, /)
| Return self+value.
|
| __bool__(self, /)
| True if self else False
|
| __ceil__(self, /)
| Return the ceiling as an Integral.
|
| __divmod__(self, value, /)
| Return divmod(self, value).
|
| __eq__(self, value, /)
| Return self==value.
|
| __float__(self, /)
| float(self)
|
| __floor__(self, /)
| Return the floor as an Integral.
|
| __floordiv__(self, value, /)
| Return self//value.
|
| __format__(self, format_spec, /)
| Formats the float according to format_spec.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getnewargs__(self, /)
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __int__(self, /)
| int(self)
|
| __le__(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).
|
| __repr__(self, /)
| Return repr(self).
|
| __rfloordiv__(self, value, /)
| Return value//self.
|
| __rmod__(self, value, /)
| Return value%self.
|
| __rmul__(self, value, /)
| Return value*self.
|
| __round__(self, ndigits=None, /)
| Return the Integral closest to x, rounding half toward even.
|
| When an argument is passed, work like built-in round(x, ndigits).
|
| __rpow__(self, value, mod=None, /)
| Return pow(value, self, mod).
|
| __rsub__(self, value, /)
| Return value-self.
|
| __rtruediv__(self, value, /)
| Return value/self.
|
| __sub__(self, value, /)
| Return self-value.
|
| __truediv__(self, value, /)
| Return self/value.
|
| __trunc__(self, /)
| Return the Integral closest to x between 0 and x.
|
| as_integer_ratio(self, /)
| Return a pair of integers, whose ratio is exactly equal to the original float.
|
| The ratio is in lowest terms and has a positive denominator. Raise
| OverflowError on infinities and a ValueError on NaNs.
|
| >>> (10.0).as_integer_ratio()
| (10, 1)
| >>> (0.0).as_integer_ratio()
| (0, 1)
| >>> (-.25).as_integer_ratio()
| (-1, 4)
|
| conjugate(self, /)
| Return self, the complex conjugate of any float.
|
| hex(self, /)
| Return a hexadecimal representation of a floating-point number.
|
| >>> (-0.1).hex()
| '-0x1.999999999999ap-4'
| >>> 3.14159.hex()
| '0x1.921f9f01b866ep+1'
|
| is_integer(self, /)
| Return True if the float is an integer.
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| __getformat__(typestr, /)
| You probably don't want to use this function.
|
| typestr
| Must be 'double' or 'float'.
|
| It exists mainly to be used in Python's test suite.
|
| This function returns whichever of 'unknown', 'IEEE, big-endian' or 'IEEE,
| little-endian' best describes the format of floating-point numbers used by the
| C type named by typestr.
|
| fromhex(string, /)
| Create a floating-point number from a hexadecimal string.
|
| >>> float.fromhex('0x1.ffffp10')
| 2047.984375
| >>> float.fromhex('-0x1p-1074')
| -5e-324
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs)
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| imag
| the imaginary part of a complex number
|
| real
| the real part of a complex number
random.NV_MAGICCONST
Python Library Documentation: float object
class float(object)
| float(x=0, /)
|
| Convert a string or number to a floating-point number, if possible.
|
| Methods defined here:
|
| __abs__(self, /)
| abs(self)
|
| __add__(self, value, /)
| Return self+value.
|
| __bool__(self, /)
| True if self else False
|
| __ceil__(self, /)
| Return the ceiling as an Integral.
|
| __divmod__(self, value, /)
| Return divmod(self, value).
|
| __eq__(self, value, /)
| Return self==value.
|
| __float__(self, /)
| float(self)
|
| __floor__(self, /)
| Return the floor as an Integral.
|
| __floordiv__(self, value, /)
| Return self//value.
|
| __format__(self, format_spec, /)
| Formats the float according to format_spec.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getnewargs__(self, /)
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __int__(self, /)
| int(self)
|
| __le__(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).
|
| __repr__(self, /)
| Return repr(self).
|
| __rfloordiv__(self, value, /)
| Return value//self.
|
| __rmod__(self, value, /)
| Return value%self.
|
| __rmul__(self, value, /)
| Return value*self.
|
| __round__(self, ndigits=None, /)
| Return the Integral closest to x, rounding half toward even.
|
| When an argument is passed, work like built-in round(x, ndigits).
|
| __rpow__(self, value, mod=None, /)
| Return pow(value, self, mod).
|
| __rsub__(self, value, /)
| Return value-self.
|
| __rtruediv__(self, value, /)
| Return value/self.
|
| __sub__(self, value, /)
| Return self-value.
|
| __truediv__(self, value, /)
| Return self/value.
|
| __trunc__(self, /)
| Return the Integral closest to x between 0 and x.
|
| as_integer_ratio(self, /)
| Return a pair of integers, whose ratio is exactly equal to the original float.
|
| The ratio is in lowest terms and has a positive denominator. Raise
| OverflowError on infinities and a ValueError on NaNs.
|
| >>> (10.0).as_integer_ratio()
| (10, 1)
| >>> (0.0).as_integer_ratio()
| (0, 1)
| >>> (-.25).as_integer_ratio()
| (-1, 4)
|
| conjugate(self, /)
| Return self, the complex conjugate of any float.
|
| hex(self, /)
| Return a hexadecimal representation of a floating-point number.
|
| >>> (-0.1).hex()
| '-0x1.999999999999ap-4'
| >>> 3.14159.hex()
| '0x1.921f9f01b866ep+1'
|
| is_integer(self, /)
| Return True if the float is an integer.
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| __getformat__(typestr, /)
| You probably don't want to use this function.
|
| typestr
| Must be 'double' or 'float'.
|
| It exists mainly to be used in Python's test suite.
|
| This function returns whichever of 'unknown', 'IEEE, big-endian' or 'IEEE,
| little-endian' best describes the format of floating-point numbers used by the
| C type named by typestr.
|
| fromhex(string, /)
| Create a floating-point number from a hexadecimal string.
|
| >>> float.fromhex('0x1.ffffp10')
| 2047.984375
| >>> float.fromhex('-0x1p-1074')
| -5e-324
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs)
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| imag
| the imaginary part of a complex number
|
| real
| the real part of a complex number
random.RECIP_BPF
Python Library Documentation: float object
class float(object)
| float(x=0, /)
|
| Convert a string or number to a floating-point number, if possible.
|
| Methods defined here:
|
| __abs__(self, /)
| abs(self)
|
| __add__(self, value, /)
| Return self+value.
|
| __bool__(self, /)
| True if self else False
|
| __ceil__(self, /)
| Return the ceiling as an Integral.
|
| __divmod__(self, value, /)
| Return divmod(self, value).
|
| __eq__(self, value, /)
| Return self==value.
|
| __float__(self, /)
| float(self)
|
| __floor__(self, /)
| Return the floor as an Integral.
|
| __floordiv__(self, value, /)
| Return self//value.
|
| __format__(self, format_spec, /)
| Formats the float according to format_spec.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getnewargs__(self, /)
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __int__(self, /)
| int(self)
|
| __le__(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).
|
| __repr__(self, /)
| Return repr(self).
|
| __rfloordiv__(self, value, /)
| Return value//self.
|
| __rmod__(self, value, /)
| Return value%self.
|
| __rmul__(self, value, /)
| Return value*self.
|
| __round__(self, ndigits=None, /)
| Return the Integral closest to x, rounding half toward even.
|
| When an argument is passed, work like built-in round(x, ndigits).
|
| __rpow__(self, value, mod=None, /)
| Return pow(value, self, mod).
|
| __rsub__(self, value, /)
| Return value-self.
|
| __rtruediv__(self, value, /)
| Return value/self.
|
| __sub__(self, value, /)
| Return self-value.
|
| __truediv__(self, value, /)
| Return self/value.
|
| __trunc__(self, /)
| Return the Integral closest to x between 0 and x.
|
| as_integer_ratio(self, /)
| Return a pair of integers, whose ratio is exactly equal to the original float.
|
| The ratio is in lowest terms and has a positive denominator. Raise
| OverflowError on infinities and a ValueError on NaNs.
|
| >>> (10.0).as_integer_ratio()
| (10, 1)
| >>> (0.0).as_integer_ratio()
| (0, 1)
| >>> (-.25).as_integer_ratio()
| (-1, 4)
|
| conjugate(self, /)
| Return self, the complex conjugate of any float.
|
| hex(self, /)
| Return a hexadecimal representation of a floating-point number.
|
| >>> (-0.1).hex()
| '-0x1.999999999999ap-4'
| >>> 3.14159.hex()
| '0x1.921f9f01b866ep+1'
|
| is_integer(self, /)
| Return True if the float is an integer.
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| __getformat__(typestr, /)
| You probably don't want to use this function.
|
| typestr
| Must be 'double' or 'float'.
|
| It exists mainly to be used in Python's test suite.
|
| This function returns whichever of 'unknown', 'IEEE, big-endian' or 'IEEE,
| little-endian' best describes the format of floating-point numbers used by the
| C type named by typestr.
|
| fromhex(string, /)
| Create a floating-point number from a hexadecimal string.
|
| >>> float.fromhex('0x1.ffffp10')
| 2047.984375
| >>> float.fromhex('-0x1p-1074')
| -5e-324
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs)
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| imag
| the imaginary part of a complex number
|
| real
| the real part of a complex number
random.Random
Python Library Documentation: class Random in module random class Random(_random.Random) | Random(x=None) | | Random number generator base class used by bound module functions. | | Used to instantiate instances of Random to get generators that don't | share state. | | Class Random can also be subclassed if you want to use a different basic | generator of your own devising: in that case, override the following | methods: random(), seed(), getstate(), and setstate(). | Optionally, implement a getrandbits() method so that randrange() | can cover arbitrarily large ranges. | | Method resolution order: | Random | _random.Random | builtins.object | | Methods defined here: | | __getstate__(self) | Helper for pickle. | | __init__(self, x=None) | Initialize an instance. | | Optional argument x controls seeding, as for Random.seed(). | | __reduce__(self) | Helper for pickle. | | __setstate__(self, state) | | betavariate(self, alpha, beta) | Beta distribution. | | Conditions on the parameters are alpha > 0 and beta > 0. | Returned values range between 0 and 1. | | The mean (expected value) and variance of the random variable are: | | E[X] = alpha / (alpha + beta) | Var[X] = alpha * beta / ((alpha + beta)**2 * (alpha + beta + 1)) | | binomialvariate(self, n=1, p=0.5) | Binomial random variable. | | Gives the number of successes for *n* independent trials | with the probability of success in each trial being *p*: | | sum(random() < p for i in range(n)) | | Returns an integer in the range: 0 <= X <= n | | The mean (expected value) and variance of the random variable are: | | E[X] = n * p | Var[x] = n * p * (1 - p) | | choice(self, seq) | Choose a random element from a non-empty sequence. | | choices(self, population, weights=None, *, cum_weights=None, k=1) | Return a k sized list of population elements chosen with replacement. | | If the relative weights or cumulative weights are not specified, | the selections are made with equal probability. | | expovariate(self, lambd=1.0) | Exponential distribution. | | lambd is 1.0 divided by the desired mean. It should be | nonzero. (The parameter would be called "lambda", but that is | a reserved word in Python.) Returned values range from 0 to | positive infinity if lambd is positive, and from negative | infinity to 0 if lambd is negative. | | The mean (expected value) and variance of the random variable are: | | E[X] = 1 / lambd | Var[X] = 1 / lambd ** 2 | | gammavariate(self, alpha, beta) | Gamma distribution. Not the gamma function! | | Conditions on the parameters are alpha > 0 and beta > 0. | | The probability distribution function is: | | x ** (alpha - 1) * math.exp(-x / beta) | pdf(x) = -------------------------------------- | math.gamma(alpha) * beta ** alpha | | The mean (expected value) and variance of the random variable are: | | E[X] = alpha * beta | Var[X] = alpha * beta ** 2 | | gauss(self, mu=0.0, sigma=1.0) | Gaussian distribution. | | mu is the mean, and sigma is the standard deviation. This is | slightly faster than the normalvariate() function. | | Not thread-safe without a lock around calls. | | getstate(self) | Return internal state; can be passed to setstate() later. | | lognormvariate(self, mu, sigma) | Log normal distribution. | | If you take the natural logarithm of this distribution, you'll get a | normal distribution with mean mu and standard deviation sigma. | mu can have any value, and sigma must be greater than zero. | | normalvariate(self, mu=0.0, sigma=1.0) | Normal distribution. | | mu is the mean, and sigma is the standard deviation. | | paretovariate(self, alpha) | Pareto distribution. alpha is the shape parameter. | | randbytes(self, n) | Generate n random bytes. | | randint(self, a, b) | Return random integer in range [a, b], including both end points. | | randrange(self, start, stop=None, step=1) | Choose a random item from range(stop) or range(start, stop[, step]). | | Roughly equivalent to ``choice(range(start, stop, step))`` but | supports arbitrarily large ranges and is optimized for common cases. | | sample(self, population, k, *, counts=None) | Chooses k unique random elements from a population sequence. | | Returns a new list containing elements from the population while | leaving the original population unchanged. The resulting list is | in selection order so that all sub-slices will also be valid random | samples. This allows raffle winners (the sample) to be partitioned | into grand prize and second place winners (the subslices). | | Members of the population need not be hashable or unique. If the | population contains repeats, then each occurrence is a possible | selection in the sample. | | Repeated elements can be specified one at a time or with the optional | counts parameter. For example: | | sample(['red', 'blue'], counts=[4, 2], k=5) | | is equivalent to: | | sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5) | | To choose a sample from a range of integers, use range() for the | population argument. This is especially fast and space efficient | for sampling from a large population: | | sample(range(10000000), 60) | | seed(self, a=None, version=2) | Initialize internal state from a seed. | | The only supported seed types are None, int, float, | str, bytes, and bytearray. | | None or no argument seeds from current time or from an operating | system specific randomness source if available. | | If *a* is an int, all bits are used. | | For version 2 (the default), all of the bits are used if *a* is a str, | bytes, or bytearray. For version 1 (provided for reproducing random | sequences from older versions of Python), the algorithm for str and | bytes generates a narrower range of seeds. | | setstate(self, state) | Restore internal state from object returned by getstate(). | | shuffle(self, x) | Shuffle list x in place, and return None. | | triangular(self, low=0.0, high=1.0, mode=None) | Triangular distribution. | | Continuous distribution bounded by given lower and upper limits, | and having a given mode value in-between. | | http://en.wikipedia.org/wiki/Triangular_distribution | | The mean (expected value) and variance of the random variable are: | | E[X] = (low + high + mode) / 3 | Var[X] = (low**2 + high**2 + mode**2 - low*high - low*mode - high*mode) / 18 | | uniform(self, a, b) | Get a random number in the range [a, b) or [a, b] depending on rounding. | | The mean (expected value) and variance of the random variable are: | | E[X] = (a + b) / 2 | Var[X] = (b - a) ** 2 / 12 | | vonmisesvariate(self, mu, kappa) | Circular data distribution. | | mu is the mean angle, expressed in radians between 0 and 2*pi, and | kappa is the concentration parameter, which must be greater than or | equal to zero. If kappa is equal to zero, this distribution reduces | to a uniform random angle over the range 0 to 2*pi. | | weibullvariate(self, alpha, beta) | Weibull distribution. | | alpha is the scale parameter and beta is the shape parameter. | | ---------------------------------------------------------------------- | Class methods defined here: | | __init_subclass__(**kwargs) | Control how subclasses generate random integers. | | The algorithm a subclass can use depends on the random() and/or | getrandbits() implementation available to it and determines | whether it can generate random integers from arbitrarily large | ranges. | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables | | __weakref__ | list of weak references to the object | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | VERSION = 3 | | ---------------------------------------------------------------------- | Methods inherited from _random.Random: | | getrandbits(self, k, /) | getrandbits(k) -> x. Generates an int with k random bits. | | random(self, /) | random() -> x in the interval [0, 1). | | ---------------------------------------------------------------------- | Static methods inherited from _random.Random: | | __new__(*args, **kwargs) class method of _random.Random | Create and return a new object. See help(type) for accurate signature.
random.SG_MAGICCONST
Python Library Documentation: float object
class float(object)
| float(x=0, /)
|
| Convert a string or number to a floating-point number, if possible.
|
| Methods defined here:
|
| __abs__(self, /)
| abs(self)
|
| __add__(self, value, /)
| Return self+value.
|
| __bool__(self, /)
| True if self else False
|
| __ceil__(self, /)
| Return the ceiling as an Integral.
|
| __divmod__(self, value, /)
| Return divmod(self, value).
|
| __eq__(self, value, /)
| Return self==value.
|
| __float__(self, /)
| float(self)
|
| __floor__(self, /)
| Return the floor as an Integral.
|
| __floordiv__(self, value, /)
| Return self//value.
|
| __format__(self, format_spec, /)
| Formats the float according to format_spec.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getnewargs__(self, /)
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __int__(self, /)
| int(self)
|
| __le__(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).
|
| __repr__(self, /)
| Return repr(self).
|
| __rfloordiv__(self, value, /)
| Return value//self.
|
| __rmod__(self, value, /)
| Return value%self.
|
| __rmul__(self, value, /)
| Return value*self.
|
| __round__(self, ndigits=None, /)
| Return the Integral closest to x, rounding half toward even.
|
| When an argument is passed, work like built-in round(x, ndigits).
|
| __rpow__(self, value, mod=None, /)
| Return pow(value, self, mod).
|
| __rsub__(self, value, /)
| Return value-self.
|
| __rtruediv__(self, value, /)
| Return value/self.
|
| __sub__(self, value, /)
| Return self-value.
|
| __truediv__(self, value, /)
| Return self/value.
|
| __trunc__(self, /)
| Return the Integral closest to x between 0 and x.
|
| as_integer_ratio(self, /)
| Return a pair of integers, whose ratio is exactly equal to the original float.
|
| The ratio is in lowest terms and has a positive denominator. Raise
| OverflowError on infinities and a ValueError on NaNs.
|
| >>> (10.0).as_integer_ratio()
| (10, 1)
| >>> (0.0).as_integer_ratio()
| (0, 1)
| >>> (-.25).as_integer_ratio()
| (-1, 4)
|
| conjugate(self, /)
| Return self, the complex conjugate of any float.
|
| hex(self, /)
| Return a hexadecimal representation of a floating-point number.
|
| >>> (-0.1).hex()
| '-0x1.999999999999ap-4'
| >>> 3.14159.hex()
| '0x1.921f9f01b866ep+1'
|
| is_integer(self, /)
| Return True if the float is an integer.
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| __getformat__(typestr, /)
| You probably don't want to use this function.
|
| typestr
| Must be 'double' or 'float'.
|
| It exists mainly to be used in Python's test suite.
|
| This function returns whichever of 'unknown', 'IEEE, big-endian' or 'IEEE,
| little-endian' best describes the format of floating-point numbers used by the
| C type named by typestr.
|
| fromhex(string, /)
| Create a floating-point number from a hexadecimal string.
|
| >>> float.fromhex('0x1.ffffp10')
| 2047.984375
| >>> float.fromhex('-0x1p-1074')
| -5e-324
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs)
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| imag
| the imaginary part of a complex number
|
| real
| the real part of a complex number
random.SystemRandom
Python Library Documentation: class SystemRandom in module random class SystemRandom(Random) | SystemRandom(x=None) | | Alternate random number generator using sources provided | by the operating system (such as /dev/urandom on Unix or | CryptGenRandom on Windows). | | Not available on all systems (see os.urandom() for details). | | Method resolution order: | SystemRandom | Random | _random.Random | builtins.object | | Methods defined here: | | getrandbits(self, k) | getrandbits(k) -> x. Generates an int with k random bits. | | getstate = _notimplemented(self, *args, **kwds) | | randbytes(self, n) | Generate n random bytes. | | random(self) | Get the next random number in the range 0.0 <= X < 1.0. | | seed(self, *args, **kwds) | Stub method. Not used for a system random number generator. | | setstate = _notimplemented(self, *args, **kwds) | | ---------------------------------------------------------------------- | Methods inherited from Random: | | __getstate__(self) | Helper for pickle. | | __init__(self, x=None) | Initialize an instance. | | Optional argument x controls seeding, as for Random.seed(). | | __reduce__(self) | Helper for pickle. | | __setstate__(self, state) | | betavariate(self, alpha, beta) | Beta distribution. | | Conditions on the parameters are alpha > 0 and beta > 0. | Returned values range between 0 and 1. | | The mean (expected value) and variance of the random variable are: | | E[X] = alpha / (alpha + beta) | Var[X] = alpha * beta / ((alpha + beta)**2 * (alpha + beta + 1)) | | binomialvariate(self, n=1, p=0.5) | Binomial random variable. | | Gives the number of successes for *n* independent trials | with the probability of success in each trial being *p*: | | sum(random() < p for i in range(n)) | | Returns an integer in the range: 0 <= X <= n | | The mean (expected value) and variance of the random variable are: | | E[X] = n * p | Var[x] = n * p * (1 - p) | | choice(self, seq) | Choose a random element from a non-empty sequence. | | choices(self, population, weights=None, *, cum_weights=None, k=1) | Return a k sized list of population elements chosen with replacement. | | If the relative weights or cumulative weights are not specified, | the selections are made with equal probability. | | expovariate(self, lambd=1.0) | Exponential distribution. | | lambd is 1.0 divided by the desired mean. It should be | nonzero. (The parameter would be called "lambda", but that is | a reserved word in Python.) Returned values range from 0 to | positive infinity if lambd is positive, and from negative | infinity to 0 if lambd is negative. | | The mean (expected value) and variance of the random variable are: | | E[X] = 1 / lambd | Var[X] = 1 / lambd ** 2 | | gammavariate(self, alpha, beta) | Gamma distribution. Not the gamma function! | | Conditions on the parameters are alpha > 0 and beta > 0. | | The probability distribution function is: | | x ** (alpha - 1) * math.exp(-x / beta) | pdf(x) = -------------------------------------- | math.gamma(alpha) * beta ** alpha | | The mean (expected value) and variance of the random variable are: | | E[X] = alpha * beta | Var[X] = alpha * beta ** 2 | | gauss(self, mu=0.0, sigma=1.0) | Gaussian distribution. | | mu is the mean, and sigma is the standard deviation. This is | slightly faster than the normalvariate() function. | | Not thread-safe without a lock around calls. | | lognormvariate(self, mu, sigma) | Log normal distribution. | | If you take the natural logarithm of this distribution, you'll get a | normal distribution with mean mu and standard deviation sigma. | mu can have any value, and sigma must be greater than zero. | | normalvariate(self, mu=0.0, sigma=1.0) | Normal distribution. | | mu is the mean, and sigma is the standard deviation. | | paretovariate(self, alpha) | Pareto distribution. alpha is the shape parameter. | | randint(self, a, b) | Return random integer in range [a, b], including both end points. | | randrange(self, start, stop=None, step=1) | Choose a random item from range(stop) or range(start, stop[, step]). | | Roughly equivalent to ``choice(range(start, stop, step))`` but | supports arbitrarily large ranges and is optimized for common cases. | | sample(self, population, k, *, counts=None) | Chooses k unique random elements from a population sequence. | | Returns a new list containing elements from the population while | leaving the original population unchanged. The resulting list is | in selection order so that all sub-slices will also be valid random | samples. This allows raffle winners (the sample) to be partitioned | into grand prize and second place winners (the subslices). | | Members of the population need not be hashable or unique. If the | population contains repeats, then each occurrence is a possible | selection in the sample. | | Repeated elements can be specified one at a time or with the optional | counts parameter. For example: | | sample(['red', 'blue'], counts=[4, 2], k=5) | | is equivalent to: | | sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5) | | To choose a sample from a range of integers, use range() for the | population argument. This is especially fast and space efficient | for sampling from a large population: | | sample(range(10000000), 60) | | shuffle(self, x) | Shuffle list x in place, and return None. | | triangular(self, low=0.0, high=1.0, mode=None) | Triangular distribution. | | Continuous distribution bounded by given lower and upper limits, | and having a given mode value in-between. | | http://en.wikipedia.org/wiki/Triangular_distribution | | The mean (expected value) and variance of the random variable are: | | E[X] = (low + high + mode) / 3 | Var[X] = (low**2 + high**2 + mode**2 - low*high - low*mode - high*mode) / 18 | | uniform(self, a, b) | Get a random number in the range [a, b) or [a, b] depending on rounding. | | The mean (expected value) and variance of the random variable are: | | E[X] = (a + b) / 2 | Var[X] = (b - a) ** 2 / 12 | | vonmisesvariate(self, mu, kappa) | Circular data distribution. | | mu is the mean angle, expressed in radians between 0 and 2*pi, and | kappa is the concentration parameter, which must be greater than or | equal to zero. If kappa is equal to zero, this distribution reduces | to a uniform random angle over the range 0 to 2*pi. | | weibullvariate(self, alpha, beta) | Weibull distribution. | | alpha is the scale parameter and beta is the shape parameter. | | ---------------------------------------------------------------------- | Class methods inherited from Random: | | __init_subclass__(**kwargs) | Control how subclasses generate random integers. | | The algorithm a subclass can use depends on the random() and/or | getrandbits() implementation available to it and determines | whether it can generate random integers from arbitrarily large | ranges. | | ---------------------------------------------------------------------- | Data descriptors inherited from Random: | | __dict__ | dictionary for instance variables | | __weakref__ | list of weak references to the object | | ---------------------------------------------------------------------- | Data and other attributes inherited from Random: | | VERSION = 3 | | ---------------------------------------------------------------------- | Static methods inherited from _random.Random: | | __new__(*args, **kwargs) class method of _random.Random | Create and return a new object. See help(type) for accurate signature.
random.TWOPI
Python Library Documentation: float object
class float(object)
| float(x=0, /)
|
| Convert a string or number to a floating-point number, if possible.
|
| Methods defined here:
|
| __abs__(self, /)
| abs(self)
|
| __add__(self, value, /)
| Return self+value.
|
| __bool__(self, /)
| True if self else False
|
| __ceil__(self, /)
| Return the ceiling as an Integral.
|
| __divmod__(self, value, /)
| Return divmod(self, value).
|
| __eq__(self, value, /)
| Return self==value.
|
| __float__(self, /)
| float(self)
|
| __floor__(self, /)
| Return the floor as an Integral.
|
| __floordiv__(self, value, /)
| Return self//value.
|
| __format__(self, format_spec, /)
| Formats the float according to format_spec.
|
| __ge__(self, value, /)
| Return self>=value.
|
| __getnewargs__(self, /)
|
| __gt__(self, value, /)
| Return self>value.
|
| __hash__(self, /)
| Return hash(self).
|
| __int__(self, /)
| int(self)
|
| __le__(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).
|
| __repr__(self, /)
| Return repr(self).
|
| __rfloordiv__(self, value, /)
| Return value//self.
|
| __rmod__(self, value, /)
| Return value%self.
|
| __rmul__(self, value, /)
| Return value*self.
|
| __round__(self, ndigits=None, /)
| Return the Integral closest to x, rounding half toward even.
|
| When an argument is passed, work like built-in round(x, ndigits).
|
| __rpow__(self, value, mod=None, /)
| Return pow(value, self, mod).
|
| __rsub__(self, value, /)
| Return value-self.
|
| __rtruediv__(self, value, /)
| Return value/self.
|
| __sub__(self, value, /)
| Return self-value.
|
| __truediv__(self, value, /)
| Return self/value.
|
| __trunc__(self, /)
| Return the Integral closest to x between 0 and x.
|
| as_integer_ratio(self, /)
| Return a pair of integers, whose ratio is exactly equal to the original float.
|
| The ratio is in lowest terms and has a positive denominator. Raise
| OverflowError on infinities and a ValueError on NaNs.
|
| >>> (10.0).as_integer_ratio()
| (10, 1)
| >>> (0.0).as_integer_ratio()
| (0, 1)
| >>> (-.25).as_integer_ratio()
| (-1, 4)
|
| conjugate(self, /)
| Return self, the complex conjugate of any float.
|
| hex(self, /)
| Return a hexadecimal representation of a floating-point number.
|
| >>> (-0.1).hex()
| '-0x1.999999999999ap-4'
| >>> 3.14159.hex()
| '0x1.921f9f01b866ep+1'
|
| is_integer(self, /)
| Return True if the float is an integer.
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| __getformat__(typestr, /)
| You probably don't want to use this function.
|
| typestr
| Must be 'double' or 'float'.
|
| It exists mainly to be used in Python's test suite.
|
| This function returns whichever of 'unknown', 'IEEE, big-endian' or 'IEEE,
| little-endian' best describes the format of floating-point numbers used by the
| C type named by typestr.
|
| fromhex(string, /)
| Create a floating-point number from a hexadecimal string.
|
| >>> float.fromhex('0x1.ffffp10')
| 2047.984375
| >>> float.fromhex('-0x1p-1074')
| -5e-324
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs)
| Create and return a new object. See help(type) for accurate signature.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| imag
| the imaginary part of a complex number
|
| real
| the real part of a complex number
random.betavariate
Python Library Documentation: method betavariate in module random
betavariate(alpha, beta) method of random.Random instance
Beta distribution.
Conditions on the parameters are alpha > 0 and beta > 0.
Returned values range between 0 and 1.
The mean (expected value) and variance of the random variable are:
E[X] = alpha / (alpha + beta)
Var[X] = alpha * beta / ((alpha + beta)**2 * (alpha + beta + 1))
random.binomialvariate
Python Library Documentation: method binomialvariate in module random
binomialvariate(n=1, p=0.5) method of random.Random instance
Binomial random variable.
Gives the number of successes for *n* independent trials
with the probability of success in each trial being *p*:
sum(random() < p for i in range(n))
Returns an integer in the range: 0 <= X <= n
The mean (expected value) and variance of the random variable are:
E[X] = n * p
Var[x] = n * p * (1 - p)
random.choice
Python Library Documentation: method choice in module random
choice(seq) method of random.Random instance
Choose a random element from a non-empty sequence.
random.choices
Python Library Documentation: method choices in module random
choices(population, weights=None, *, cum_weights=None, k=1) method of random.Random instance
Return a k sized list of population elements chosen with replacement.
If the relative weights or cumulative weights are not specified,
the selections are made with equal probability.
random.expovariate
Python Library Documentation: method expovariate in module random
expovariate(lambd=1.0) method of random.Random instance
Exponential distribution.
lambd is 1.0 divided by the desired mean. It should be
nonzero. (The parameter would be called "lambda", but that is
a reserved word in Python.) Returned values range from 0 to
positive infinity if lambd is positive, and from negative
infinity to 0 if lambd is negative.
The mean (expected value) and variance of the random variable are:
E[X] = 1 / lambd
Var[X] = 1 / lambd ** 2
random.gammavariate
Python Library Documentation: method gammavariate in module random
gammavariate(alpha, beta) method of random.Random instance
Gamma distribution. Not the gamma function!
Conditions on the parameters are alpha > 0 and beta > 0.
The probability distribution function is:
x ** (alpha - 1) * math.exp(-x / beta)
pdf(x) = --------------------------------------
math.gamma(alpha) * beta ** alpha
The mean (expected value) and variance of the random variable are:
E[X] = alpha * beta
Var[X] = alpha * beta ** 2
random.gauss
Python Library Documentation: method gauss in module random
gauss(mu=0.0, sigma=1.0) method of random.Random instance
Gaussian distribution.
mu is the mean, and sigma is the standard deviation. This is
slightly faster than the normalvariate() function.
Not thread-safe without a lock around calls.
random.getrandbits
Python Library Documentation: built-in function getrandbits
getrandbits(k, /) method of random.Random instance
getrandbits(k) -> x. Generates an int with k random bits.
random.getstate
Python Library Documentation: method getstate in module random
getstate() method of random.Random instance
Return internal state; can be passed to setstate() later.
random.lognormvariate
Python Library Documentation: method lognormvariate in module random
lognormvariate(mu, sigma) method of random.Random instance
Log normal distribution.
If you take the natural logarithm of this distribution, you'll get a
normal distribution with mean mu and standard deviation sigma.
mu can have any value, and sigma must be greater than zero.
random.normalvariate
Python Library Documentation: method normalvariate in module random
normalvariate(mu=0.0, sigma=1.0) method of random.Random instance
Normal distribution.
mu is the mean, and sigma is the standard deviation.
random.paretovariate
Python Library Documentation: method paretovariate in module random
paretovariate(alpha) method of random.Random instance
Pareto distribution. alpha is the shape parameter.
random.randbytes
Python Library Documentation: method randbytes in module random
randbytes(n) method of random.Random instance
Generate n random bytes.
random.randint
Python Library Documentation: method randint in module random
randint(a, b) method of random.Random instance
Return random integer in range [a, b], including both end points.
random.random
Python Library Documentation: built-in function random
random() method of random.Random instance
random() -> x in the interval [0, 1).
random.randrange
Python Library Documentation: method randrange in module random
randrange(start, stop=None, step=1) method of random.Random instance
Choose a random item from range(stop) or range(start, stop[, step]).
Roughly equivalent to ``choice(range(start, stop, step))`` but
supports arbitrarily large ranges and is optimized for common cases.
random.sample
Python Library Documentation: method sample in module random
sample(population, k, *, counts=None) method of random.Random instance
Chooses k unique random elements from a population sequence.
Returns a new list containing elements from the population while
leaving the original population unchanged. The resulting list is
in selection order so that all sub-slices will also be valid random
samples. This allows raffle winners (the sample) to be partitioned
into grand prize and second place winners (the subslices).
Members of the population need not be hashable or unique. If the
population contains repeats, then each occurrence is a possible
selection in the sample.
Repeated elements can be specified one at a time or with the optional
counts parameter. For example:
sample(['red', 'blue'], counts=[4, 2], k=5)
is equivalent to:
sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)
To choose a sample from a range of integers, use range() for the
population argument. This is especially fast and space efficient
for sampling from a large population:
sample(range(10000000), 60)
random.seed
Python Library Documentation: method seed in module random
seed(a=None, version=2) method of random.Random instance
Initialize internal state from a seed.
The only supported seed types are None, int, float,
str, bytes, and bytearray.
None or no argument seeds from current time or from an operating
system specific randomness source if available.
If *a* is an int, all bits are used.
For version 2 (the default), all of the bits are used if *a* is a str,
bytes, or bytearray. For version 1 (provided for reproducing random
sequences from older versions of Python), the algorithm for str and
bytes generates a narrower range of seeds.
random.setstate
Python Library Documentation: method setstate in module random
setstate(state) method of random.Random instance
Restore internal state from object returned by getstate().
random.shuffle
Python Library Documentation: method shuffle in module random
shuffle(x) method of random.Random instance
Shuffle list x in place, and return None.
random.triangular
Python Library Documentation: method triangular in module random
triangular(low=0.0, high=1.0, mode=None) method of random.Random instance
Triangular distribution.
Continuous distribution bounded by given lower and upper limits,
and having a given mode value in-between.
http://en.wikipedia.org/wiki/Triangular_distribution
The mean (expected value) and variance of the random variable are:
E[X] = (low + high + mode) / 3
Var[X] = (low**2 + high**2 + mode**2 - low*high - low*mode - high*mode) / 18
random.uniform
Python Library Documentation: method uniform in module random
uniform(a, b) method of random.Random instance
Get a random number in the range [a, b) or [a, b] depending on rounding.
The mean (expected value) and variance of the random variable are:
E[X] = (a + b) / 2
Var[X] = (b - a) ** 2 / 12
random.vonmisesvariate
Python Library Documentation: method vonmisesvariate in module random
vonmisesvariate(mu, kappa) method of random.Random instance
Circular data distribution.
mu is the mean angle, expressed in radians between 0 and 2*pi, and
kappa is the concentration parameter, which must be greater than or
equal to zero. If kappa is equal to zero, this distribution reduces
to a uniform random angle over the range 0 to 2*pi.
random.weibullvariate
Python Library Documentation: method weibullvariate in module random
weibullvariate(alpha, beta) method of random.Random instance
Weibull distribution.
alpha is the scale parameter and beta is the shape parameter.