numpy.random.logistic(loc=0.0, scale=1.0, size=None) Draw samples from a logistic distribution. Samples are drawn

numpy.random.standard_t(df, size=None) Draw samples from a standard Student?s t distribution with df degrees

RandomState.tomaxint(size=None) Random integers between 0 and sys.maxint, inclusive. Return

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

RandomState.geometric(p, size=None) Draw samples from the geometric distribution. Bernoulli trials

numpy.random.wald(mean, scale, size=None) Draw samples from a Wald, or inverse Gaussian, distribution. As the scale

numpy.random.logseries(p, size=None) Draw samples from a logarithmic series distribution. Samples are drawn from

RandomState.random_integers(low, high=None, size=None) Random integers of type np.int between low

numpy.random.hypergeometric(ngood, nbad, nsample, size=None) Draw samples from a Hypergeometric distribution.

RandomState.choice(a, size=None, replace=True, p=None) Generates a random sample from a given 1-D array