recarray.argsort(axis=-1, kind='quicksort', order=None) Returns the indices that would sort this array. Refer to numpy.argsort for full documentation. See also numpy.argsort equivalent function

recarray.argpartition(kth, axis=-1, kind='introselect', order=None) Returns the indices that would partition this array. Refer to numpy.argpartition for full documentation. New in version 1.8.0. See also numpy.argpartition equivalent function

recarray.argmin(axis=None, out=None) Return indices of the minimum values along the given axis of a. Refer to numpy.argmin for detailed documentation. See also numpy.argmin equivalent function

recarray.argmax(axis=None, out=None) Return indices of the maximum values along the given axis. Refer to numpy.argmax for full documentation. See also numpy.argmax equivalent function

recarray.all(axis=None, out=None, keepdims=False) Returns True if all elements evaluate to True. Refer to numpy.all for full documentation. See also numpy.all equivalent function

recarray.any(axis=None, out=None, keepdims=False) Returns True if any of the elements of a evaluate to True. Refer to numpy.any for full documentation. See also numpy.any equivalent function

RandomState.zipf(a, size=None) Draw samples from a Zipf distribution. Samples are drawn from a Zipf distribution with specified parameter a > 1. The Zipf distribution (also known as the zeta distribution) is a continuous probability distribution that satisfies Zipf?s law: the frequency of an item is inversely proportional to its rank in a frequency table. Parameters: a : float > 1 Distribution parameter. size : int or tuple of ints, optional Output shape. If the given shape is, e.

RandomState.weibull(a, size=None) Draw samples from a Weibull distribution. Draw samples from a 1-parameter Weibull distribution with the given shape parameter a. Here, U is drawn from the uniform distribution over (0,1]. The more common 2-parameter Weibull, including a scale parameter is just . Parameters: a : float Shape of the distribution. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None

RandomState.wald(mean, scale, size=None) Draw samples from a Wald, or inverse Gaussian, distribution. As the scale approaches infinity, the distribution becomes more like a Gaussian. Some references claim that the Wald is an inverse Gaussian with mean equal to 1, but this is by no means universal. The inverse Gaussian distribution was first studied in relationship to Brownian motion. In 1956 M.C.K. Tweedie used the name inverse Gaussian because there is an inverse relationship between the t

RandomState.vonmises(mu, kappa, size=None) Draw samples from a von Mises distribution. Samples are drawn from a von Mises distribution with specified mode (mu) and dispersion (kappa), on the interval [-pi, pi]. The von Mises distribution (also known as the circular normal distribution) is a continuous probability distribution on the unit circle. It may be thought of as the circular analogue of the normal distribution. Parameters: mu : float Mode (?center?) of the distribution. kappa : fl