scipy.stats.hypergeom = <scipy.stats.distributions.hypergeom_gen object at 0x434ab90>

A hypergeometric discrete random variable.

The hypergeometric distribution models drawing objects from a bin. M is the total number of objects, n is total number of Type I objects. The random variate represents the number of Type I objects in N drawn without replacement from the total population.

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


q : array_like

lower or upper tail probability

M, n, N : array_like

shape parameters

loc : array_like, optional

location parameter (default=0)

scale : array_like, optional

scale parameter (default=1)

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=’mv’)

Alternatively, the object may be called (as a function) to fix the shape and :

location parameters returning a “frozen” discrete RV object: :

rv = hypergeom(M, n, N, loc=0) :

  • Frozen RV object with the same methods but holding the given shape and location fixed.


The probability mass function is defined as:

pmf(k, M, n, N) = choose(n, k) * choose(M - n, N - k) / choose(M, N),
                                       for N - (M-n) <= k <= min(m,N)


>>> from scipy.stats import hypergeom
>>> numargs = hypergeom.numargs
>>> [ M, n, N ] = Replace with reasonable value * numargs
>>> rv = hypergeom(M, n, N)

Display frozen pdf

>>> x = np.linspace(0, np.minimum(rv.dist.b, 3))
>>> h = plt.plot(x, rv.pdf(x))

Check accuracy of cdf and ppf

>>> prb = hypergeom.cdf(x, M, n, N)
>>> h = plt.semilogy(np.abs(x - hypergeom.ppf(prb, M, n, N)) + 1e-20)

Random number generation

>>> R = hypergeom.rvs(M, n, N, size=100)


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

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