numpy.random¶
Source: stdlib/numpy/random/__init__.codon
numpy.random¶Source: stdlib/numpy/random/__init__.codon
default_rng(seed = None)¶beta(a, b, size = None)¶binomial(n, p, size = None)¶bytes(length: int)¶chisquare(df, size = None)¶choice(a, size = None, replace: bool = True, p = None)¶dirichlet(alpha, size = None)¶exponential(scale = 1.0, size = None)¶f(dfnum, dfden, size = None)¶gamma(shape, scale = 1.0, size = None)¶geometric(p, size = None)¶get_state(legacy: bool)¶gumbel(loc = 0.0, scale = 1.0, size = None)¶hypergeometric(ngood, nbad, nsample, size = None)¶laplace(loc = 0.0, scale = 1.0, size = None)¶logistic(loc = 0.0, scale = 1.0, size = None)¶lognormal(mean = 0.0, sigma = 1.0, size = None)¶logseries(p, size = None)¶multinomial(n, pvals, size = None)¶multivariate_normal(mean, cov, size = None, check_valid: Literal[str] = "warn", tol: float = 1e-8)¶negative_binomial(n, p, size = None)¶noncentral_chisquare(df, nonc, size = None)¶noncentral_f(dfnum, dfden, nonc, size = None)¶normal(loc = 0.0, scale = 1.0, size = None)¶pareto(a, size = None)¶permutation(x)¶poisson(lam = 1.0, size = None)¶power(a, size = None)¶rand(*d)¶randint(low, high = None, size = None, dtype: type = int)¶randn(*d)¶random(size = None)¶random_integers(low, high = None, size = None)¶random_sample(size = None)¶ranf(size = None)¶rayleigh(scale = 1.0, size = None)¶sample(size = None)¶seed(seed = None)¶set_state(state)¶shuffle(x)¶standard_cauchy(size = None)¶standard_exponential(size = None)¶standard_gamma(shape, size = None)¶standard_normal(size = None)¶standard_t(df, size = None)¶triangular(left, mode, right, size = None)¶uniform(low = 0.0, high = 1.0, size = None)¶vonmises(mu, kappa, size = None)¶wald(mean, scale, size = None)¶weibull(a, size = None)¶zipf(a, size = None)¶