numpy.random.rand(d0, d1, ..., dn) Random values in a given shape. Create an array of the given shape and populate

numpy.random.standard_cauchy(size=None) Draw samples from a standard Cauchy distribution with mode = 0. Also

RandomState.triangular(left, mode, right, size=None) Draw samples from the triangular distribution. The

numpy.random.permutation(x) Randomly permute a sequence, or return a permuted range. If x is a

RandomState.gumbel(loc=0.0, scale=1.0, size=None) Draw samples from a Gumbel distribution. Draw samples

RandomState.binomial(n, p, size=None) Draw samples from a binomial distribution. Samples are drawn from

RandomState.randint(low, high=None, size=None, dtype='l') Return random integers from low (inclusive)

numpy.random.triangular(left, mode, right, size=None) Draw samples from the triangular distribution. The triangular

numpy.random.noncentral_f(dfnum, dfden, nonc, size=None) Draw samples from the noncentral F distribution. Samples

RandomState.beta(a, b, size=None) Draw samples from a Beta distribution. The Beta distribution is a special