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numpy.random.rand

# 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, dtype]) Return random integers from low (inclusive) to high (exclusive). `random_integers`(low[, high, size]) Random integers of type np.int 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]) 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. `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. `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. `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.

## Random generator¶

 `RandomState`([seed]) 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.