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

RandomState.weibull(a, size=None) Draw samples from a Weibull distribution. Draw samples from a 1-parameter

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

numpy.random.exponential(scale=1.0, size=None) Draw samples from an exponential distribution. Its probability

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

numpy.random.random_sample(size=None) Return random floats in the half-open interval [0.0, 1.0). Results

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

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

numpy.random.weibull(a, size=None) Draw samples from a Weibull distribution. Draw samples from a 1-parameter Weibull

numpy.random.seed(seed=None) Seed the generator. This method is called when