numpy random模块有哪些

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一下方法都要加 np.random. 前缀
1. 简单随机数据

namedescriberand(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 arraybytes(length)Return random bytes.

2. 生成随机分布

namedescribebeta(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 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.

3. 重排

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

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