# Random sampling (numpy.random)¶

## Simple random data¶

 rand(d0, d1, ..., dn) Random values in a given shape. randn(d0, d1, ..., dn) Return a sample (or samples) from the “standard normal” distribution. randint(low[, high, size]) Return random integers from low (inclusive) to high (exclusive). random_integers(low[, high, size]) Return random integers between low and high, inclusive. random_sample([size]) Return random floats in the half-open interval [0.0, 1.0). random([size]) Return random floats in the half-open interval [0.0, 1.0). ranf([size]) Return random floats in the half-open interval [0.0, 1.0). sample([size]) Return random floats in the half-open interval [0.0, 1.0). choice(a[, size, replace, p]) Generates a random sample from a given 1-D array .. bytes(length) Return random bytes.

## Permutations¶

 shuffle(x) Modify a sequence in-place by shuffling its contents. permutation(x) Randomly permute a sequence, or return a permuted range.

## Distributions¶

 beta(a, b[, size]) The Beta distribution over [0, 1]. binomial(n, p[, size]) Draw samples from a binomial distribution. chisquare(df[, size]) Draw samples from a chi-square distribution. dirichlet(alpha[, size]) Draw samples from the Dirichlet distribution. exponential([scale, size]) Exponential distribution. f(dfnum, dfden[, size]) Draw samples from a 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]) 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]) Return samples drawn 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. standard_cauchy([size]) 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]) Returns samples from a Standard Normal distribution (mean=0, stdev=1). standard_t(df[, size]) Standard Student’s t distribution with df degrees of freedom. triangular(left, mode, right[, size]) Draw samples from the triangular distribution. 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]) Weibull distribution. zipf(a[, size]) Draw samples from a Zipf distribution.

## Random generator¶

 RandomState Container for the Mersenne Twister pseudo-random number generator. seed([seed]) Seed the generator. get_state() Return a tuple representing the internal state of the generator. set_state(state) Set the internal state of the generator from a tuple.