### Navigation

- index
- modules |
- next |
- previous |
- NumPy v1.3 Manual (DRAFT) »
- NumPy Reference »
- Routines »

# Random sampling (`numpy.random`)

## Simple random data

`rand` (d0, d1, ..., dn) | Random values in a given shape.
`randn` (d0, d1, ..., dn) | Returns zero-mean, unit-variance Gaussian random numbers in an array of shape (d0, d1, ..., dn).
`randint` (low[, high, size]) | Return random integers x such that low <= x < high.
`random_integers` (low[, high, size]) | Return random integers x such that low <= x <= high.
`random_sample` ([size]) | Return random floats in the half-open interval [0.0, 1.0).
`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.
`mtrand.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]) | Laplace or double exponential distribution.
`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]) | Negative Binomial distribution.
`noncentral_chisquare` (df, nonc[, size]) | Draw samples from a noncentral chi-square distribution.
`noncentral_f` (dfnum, dfden, nonc[, size]) | Noncentral F distribution.
`normal` ([loc, scale, size]) | Draw random samples from a normal (Gaussian) distribution.
`pareto` (a[, size]) | Draw samples from a Pareto distribution with specified shape.
`poisson` ([lam, size]) | Poisson distribution.
`power` (a[, size]) | Power distribution.
`rayleigh` ([scale, size]) | Rayleigh distribution.
`standard_cauchy` ([size]) | Standard Cauchy with mode=0.
`standard_exponential` ([size]) | Standard exponential distribution (scale=1).
`standard_gamma` (shape[, size]) | Standard Gamma distribution.
`standard_normal` ([size]) | 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]) | Triangular distribution starting at left, peaking at mode, and ending at right (left <= mode <= 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]) | Wald (inverse Gaussian) distribution.
`weibull` (a[, size]) | Weibull distribution.
`zipf` (a[, size]) | Draw samples from a Zipf distribution.

## Random generator

`mtrand.RandomState` ([seed]) | Container for the Mersenne Twister PRNG.
`seed` ([seed]) | Seed the generator.
`get_state` () | Return a tuple representing the internal state of the generator:
`set_state` (state) | Set the state from a tuple.

### Navigation

- index
- modules |
- next |
- previous |
- NumPy v1.3 Manual (DRAFT) »
- NumPy Reference »
- Routines »