# scipy.stats.zipf¶

scipy.stats.zipf = <scipy.stats._discrete_distns.zipf_gen object at 0x7f6169c4c690>[source]

A Zipf discrete random variable.

Discrete random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:

Parameters: x : array_like quantiles q : array_like lower or upper tail probability a : array_like shape parameters loc : array_like, optional location parameter (default=0) size : int or tuple of ints, optional shape of random variates (default computed from input arguments ) moments : str, optional composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. Default is ‘mv’. Alternatively, the object may be called (as a function) to fix the shape and location parameters returning a “frozen” discrete RV object: rv = zipf(a, loc=0) Frozen RV object with the same methods but holding the given shape and location fixed.

Notes

The probability mass function for zipf is:

zipf.pmf(k, a) = 1/(zeta(a) * k**a)


for k >= 1.

zipf takes a as shape parameter.

Examples

>>> 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, 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)


Methods

 rvs(a, loc=0, size=1) Random variates. pmf(x, a, loc=0) Probability mass function. logpmf(x, a, loc=0) Log of the probability mass function. cdf(x, a, loc=0) Cumulative density function. logcdf(x, a, loc=0) Log of the cumulative density function. sf(x, a, loc=0) Survival function (1-cdf — sometimes more accurate). logsf(x, a, loc=0) Log of the survival function. ppf(q, a, loc=0) Percent point function (inverse of cdf — percentiles). isf(q, a, loc=0) Inverse survival function (inverse of sf). stats(a, loc=0, moments='mv') Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(a, loc=0) (Differential) entropy of the RV. expect(func, a, loc=0, lb=None, ub=None, conditional=False) Expected value of a function (of one argument) with respect to the distribution. median(a, loc=0) Median of the distribution. mean(a, loc=0) Mean of the distribution. var(a, loc=0) Variance of the distribution. std(a, loc=0) Standard deviation of the distribution. interval(alpha, a, loc=0) Endpoints of the range that contains alpha percent of the distribution

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