Random Generator


Generator is deprecated. You should be using numpy.random.Generator.

The Generator provides access to a wide range of distributions, and served as a replacement for RandomState. The main difference between the two is that Generator relies on an additional bit generator to manage state and generate the random bits which are then transformed into random values from useful distributions. The default bit generator used by Generator is Xoroshiro128. The bit generator can be changed by passing an instantized bit generator to Generator.

class randomgen.generator.Generator(bit_generator=None)

Random value generator using a bit generator source.

Generator exposes methods for generating random numbers drawn from a variety of probability distributions. In addition to the distribution-specific arguments, each method takes a keyword argument size that defaults to None. If size is None, then a single value is generated and returned. If size is an integer, then a 1-D array filled with generated values is returned. If size is a tuple, then an array with that shape is filled and returned.

No Compatibility Guarantee

Generator is evolving and so it is not possible to provide a compatibility guarantee like RandomState. In particular, better algorithms have already been added and bugs that change the stream have been fixed. This will change once Generator stabilizes.

bit_generatorBitGenerator, optional

Bit generator to use as the core generator. If none is provided, uses Xoroshiro128.


The Python stdlib module random contains pseudo-random number generator with a number of methods that are similar to the ones available in Generator. It uses Mersenne Twister, which is available by using the MT19937 bit generator. Generator, besides being NumPy-aware, has the advantage that it provides a much larger number of probability distributions from which to choose.


>>> from randomgen import Generator
>>> rg = Generator()
>>> rg.standard_normal()
-0.203  # random

Using a specific generator

>>> from randomgen import MT19937
>>> rg = Generator(MT19937())
>>> rg.standard_normal()
-0.203  # random

Seed and State Manipulation


Reseed the bit generator.


Get or set the bit generator's state


Gets the bit generator instance used by the generator

Simple random data

rand(d0, d1, ..., dn[, dtype])

Random values in a given shape.

randn(d0, d1, ..., dn[, dtype])

Return a sample (or samples) from the "standard normal" distribution.

integers(low[, high, size, dtype, ...])

Return random integers from low (inclusive) to high (exclusive), or if endpoint=True, low (inclusive) to high (inclusive).

random([size, dtype, out])

Return random floats in the half-open interval [0.0, 1.0).


choice(a, size=None, replace=True, p=None, axis=0, shuffle=True):


Return random bytes.

uintegers([size, bits])

Return random unsigned integers



Modify an array or a mutable sequence in-place by shuffling its contents.


Randomly permute a sequence, or return a permuted range.


beta(a, b[, size])

Draw samples from a Beta distribution.

binomial(n, p[, size])

Draw samples from a binomial distribution.

chisquare(df[, size])

Draw samples from a chi-square distribution.

complex_normal([loc, gamma, relation, size])

Draw random samples from a complex normal (Gaussian) distribution.

dirichlet(alpha[, size])

Draw samples from the Dirichlet distribution.

exponential([scale, size])

Draw samples from an exponential distribution.

f(dfnum, dfden[, size])

Draw samples from an F distribution.

gamma(shape[, scale, size])

Draw samples from a Gamma distribution.

geometric(p[, size])

Draw samples from the geometric distribution.

gumbel([loc, scale, size])

Draw samples from a Gumbel distribution.

hypergeometric(ngood, nbad, nsample[, size])

Draw samples from a Hypergeometric distribution.

laplace([loc, scale, size])

Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).

logistic([loc, scale, size])

Draw samples from a logistic distribution.

lognormal([mean, sigma, size])

Draw samples from a log-normal distribution.

logseries(p[, size])

Draw samples from a logarithmic series distribution.

multinomial(n, pvals[, size])

Draw samples from a multinomial distribution.

multivariate_normal(mean, cov[, size, ...])

Draw random samples from a multivariate normal distribution.

negative_binomial(n, p[, size])

Draw samples from a negative binomial distribution.

noncentral_chisquare(df, nonc[, size])

Draw samples from a noncentral chi-square distribution.

noncentral_f(dfnum, dfden, nonc[, size])

Draw samples from the noncentral F distribution.

normal([loc, scale, size])

Draw random samples from a normal (Gaussian) distribution.

pareto(a[, size])

Draw samples from a Pareto II or Lomax distribution with specified shape.

poisson([lam, size])

Draw samples from a Poisson distribution.

power(a[, size])

Draws samples in [0, 1] from a power distribution with positive exponent a - 1.

rayleigh([scale, size])

Draw samples from a Rayleigh distribution.


Draw samples from a standard Cauchy distribution with mode = 0.

standard_exponential([size, dtype, method, out])

Draw samples from the standard exponential distribution.

standard_gamma(shape[, size, dtype, out])

Draw samples from a standard Gamma distribution.

standard_normal([size, dtype, out])

Draw samples from a standard Normal distribution (mean=0, stdev=1).

standard_t(df[, size])

Draw samples from a standard Student's t distribution with df degrees of freedom.

triangular(left, mode, right[, size])

Draw samples from the triangular distribution over the interval [left, right].

uniform([low, high, size])

Draw samples from a uniform distribution.

vonmises(mu, kappa[, size])

Draw samples from a von Mises distribution.

wald(mean, scale[, size])

Draw samples from a Wald, or inverse Gaussian, distribution.

weibull(a[, size])

Draw samples from a Weibull distribution.

zipf(a[, size])

Draw samples from a Zipf distribution.