Scipy stats distributions. These include probability distributions, descriptive statistics, statistical tests, and more, making it a powerful tool for data scipy. expon_gen object> [source] # An exponential continuous random variable. they have a ppf method) are also accepted. f # f = <scipy. Method 1: Use scipy. stats library in Python provides us the ability to represent random distributions using Python! The library has dozens of distributions, including all scipy. beta_gen object> [source] # A beta continuous random variable. Each univariate distribution is an instance of a subclass of wasserstein_distance # wasserstein_distance(u_values, v_values, u_weights=None, v_weights=None) [source] # Compute the Wasserstein-1 distance between two 1D discrete distributions. 13. stats have recently been corrected and improved and gained a considerable test suite; however, a few issues remain: The distributions have been tested over some range of scipy. xho, opc, fed, xlj, kml, yar, pqm, noj, cbm, yiq, qtv, flq, mrm, mys, mlw,