from scipy.stats import zipf import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) # Calculate a few first moments: a = 6.5 mean, var, skew, kurt = zipf.stats(a, moments='mvsk') # Display the probability mass function (``pmf``): x = np.arange(zipf.ppf(0.01, a), zipf.ppf(0.99, a)) ax.plot(x, zipf.pmf(x, a), 'bo', ms=8, label='zipf pmf') ax.vlines(x, 0, zipf.pmf(x, a), colors='b', lw=5, alpha=0.5) # Alternatively, the distribution object can be called (as a function) # to fix the shape and location. This returns a "frozen" RV object holding # the given parameters fixed. # Freeze the distribution and display the frozen ``pmf``: rv = zipf(a) ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1, label='frozen pmf') ax.legend(loc='best', frameon=False) plt.show() # Check accuracy of ``cdf`` and ``ppf``: prob = zipf.cdf(x, a) np.allclose(x, zipf.ppf(prob, a)) # True # Generate random numbers: r = zipf.rvs(a, size=1000)