numpy.random

numpy.random implements pseudo random number generators.

__all__

list object

beta()

binomial()

BitGenerator

numpy.random.bit_generator.BitGenerator class

bit_generator

Module

_bounded_integers

Module

__builtins__

dict object

bytes()

__cached__

str object

chisquare()

choice()

_common

Module

default_rng()

dirichlet()

__doc__

str object

exponential()

f()

__file__

str object

gamma()

Generator

numpy.random._generator.Generator class

_generator

Module

geometric()

get_bit_generator()

get_state()

gumbel()

hypergeometric()

laplace()

__loader__

logistic()

lognormal()

logseries()

MT19937

numpy.random._mt19937.MT19937 class

_mt19937

Module

mtrand

Module

multinomial()

multivariate_normal()

__name__

str object

negative_binomial()

noncentral_chisquare()

noncentral_f()

normal()

Draws random samples from a normal (Gaussian) distribution. See also randn()

__package__

str object

pareto()

__path__

list object

PCG64

numpy.random._pcg64.PCG64 class

_pcg64

Module

PCG64DXSM

numpy.random._pcg64.PCG64DXSM class

permutation()

Philox

numpy.random._philox.Philox class

_philox

Module

_pickle

Module

poisson()

power()

rand()

randint()

randn()

Returns a sample (or samples) from the standard normal distribution. See also normal()

random()

Return random floats in the half-open interval [0.0, 1.0). random is an alias for random_sample to ease forward-porting to the new random API.

random_integers()

random_sample()

See random()

RandomState

numpy.random.mtrand.RandomState class

__RandomState_ctor()

Function

ranf()

rayleigh()

sample()

seed()

SeedSequence

numpy.random.bit_generator.SeedSequence class

set_bit_generator()

set_state()

SFC64

numpy.random._sfc64.SFC64 class

_sfc64

Module

shuffle()

__spec__

standard_cauchy()

standard_exponential()

standard_gamma()

standard_normal()

standard_t()

test

triangular()

uniform()

vonmises()

wald()

weibull()

zipf()

>>> np.random.seed(42) >>> np.random.random() 0.3745401188473625 >>> np.random.random(5) array([0.95071431, 0.73199394, 0.59865848, 0.15601864, 0.15599452]) >>> np.random.random( (2, 3) ) array([[0.05808361, 0.86617615, 0.60111501], [0.70807258, 0.02058449, 0.96990985]])