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This article mainly shows you "what are the numpy random modules", the content is easy to understand, clear, hope to help you solve your doubts, the following let the editor lead you to study and learn "what are the numpy random modules" this article?

Add np.random to every method. Prefix

1. Simple random data

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] 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 1murD arraybytes (length) Return random bytes.

two。 Generate random distribution

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. Rearrange

Namedescribeshuffle (x) Modify a sequence in-place by shuffling its contents.permutation (x) Randomly permute a sequence, or return a permuted range. These are all the contents of the article "what are the numpy random modules?" Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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