numpy.random
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)
Modules
9