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# 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
`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.

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