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class numpy.random.RandomState(seed=None)

Container for the Mersenne Twister pseudo-random number generator.

RandomState exposes a number of 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.

Compatibility Guarantee A fixed seed and a fixed series of calls to ‘RandomState’ methods using the same parameters will always produce the same results up to roundoff error except when the values were incorrect. Incorrect values will be fixed and the NumPy version in which the fix was made will be noted in the relevant docstring. Extension of existing parameter ranges and the addition of new parameters is allowed as long the previous behavior remains unchanged.

seed : {None, int, array_like}, optional

Random seed used to initialize the pseudo-random number generator. Can be any integer between 0 and 2**32 - 1 inclusive, an array (or other sequence) of such integers, or None (the default). If seed is None, then RandomState will try to read data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise.


The Python stdlib module “random” also contains a Mersenne Twister pseudo-random number generator with a number of methods that are similar to the ones available in RandomState. RandomState, besides being NumPy-aware, has the advantage that it provides a much larger number of probability distributions to choose from.


beta(a, b[, size]) Draw samples from a Beta distribution.
binomial(n, p[, size]) Draw samples from a binomial distribution.
bytes(length) Return random bytes.
chisquare(df[, size]) Draw samples from a chi-square distribution.
choice(a[, size, replace, p]) Generates a random sample from a given 1-D array
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.
get_state() Return a tuple representing the internal state of the generator.
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.
permutation(x) Randomly permute a sequence, or return a permuted range.
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.
rand(d0, d1, …, dn) Random values in a given shape.
randint(low[, high, size, dtype]) Return random integers from low (inclusive) to high (exclusive).
randn(d0, d1, …, dn) Return a sample (or samples) from the “standard normal” distribution.
random_integers(low[, high, size]) Random integers of type between low and high, inclusive.
random_sample([size]) Return random floats in the half-open interval [0.0, 1.0).
rayleigh([scale, size]) Draw samples from a Rayleigh distribution.
seed([seed]) Seed the generator.
set_state(state) Set the internal state of the generator from a tuple.
shuffle(x) Modify a sequence in-place by shuffling its contents.
standard_cauchy([size]) Draw samples from a standard Cauchy distribution with mode = 0.
standard_exponential([size]) Draw samples from the standard exponential distribution.
standard_gamma(shape[, size]) Draw samples from a standard Gamma distribution.
standard_normal([size]) 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.
tomaxint([size]) Return a sample of uniformly distributed random integers in the interval [0, np.iinfo(].
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.