# Distributions in SciPy

This page summarizes how to work with univariate probability distributions using Python's SciPy library. See also notes on working with distributions in Mathematica, Excel, and R/S-PLUS.

Probability distribution classes are located in scipy.stats.

The methods on continuous distribution classes are as follows.

 Method Meaning pdf Probability density function cdf Cumulative distribution function sf Survival function = complementary CDF ppf Percentile point function (i.e. CDF inverse) isf Inverse survival function (Complementary CDF inverse) stats Mean, variance, skew, or kurtosis moment Non-central moments rvs Random samples

Functions such as pdf and cdf are defined over the entire real line. For example, the beta distribution is commonly defined on the interval [0, 1]. If you ask for the pdf outside this interval, you simply get 0. If you ask for the cdf to the left of the interval you get 0, and to the right of the interval you get 1.

Distributions have a general form and a "frozen" form. The general form is stateless: you supply the distribution parameters as arguments to every call. The frozen form creates an object with the distribution parameters set. For example, you could evaluate the PDF of a normal(3, 4) distribution at the value 5 by

stats.norm.pdf(5, 3, 4)

or by

mydist = stats.norm(3, 4)
mydist.pdf(5)

Note that the argument of the PDF, in this example 5, comes before the distribution parameters. Note also that for discrete distributions, one would call pmf (probability mass function) rather than the pdf (probability density function).

## Distributions and parameterizations

SciPy makes every continuous distribution into a location-scale family, including some distributions that typically do not have location scale parameters. This unusual approach has its advantages. For example, the question of whether an exponential distribtion is parameterized in terms of its mean or its rate goes away: there is no mean or rate parameter per se, only a scale parameter like every other continuous distribution.

The table below only lists parameters in addition to location and scale.

 Distribution SciPy name Parameters beta beta shape1, shape2 binomial binom size, prob Cauchy cauchy chi-squared chi2 df exponential expon F f df1, df2 gamma gamma shape geometric geom p hypergeometric hypergeom M, n, N inverse gamma invgamma shape log-normal lognorm sdlog logistic logistic negative binomial nbinom size, prob normal norm Poisson poisson lambda Student t t df uniform unif Weibull exponweib exponent, shape

SciPy does not have a simple Weibull distribution but instead has a generalization of the Weibull called the exponentiated Weibull. Set the exponential parameter to 1 and you get the ordinary Weibull distribution.

The hypergeometric distribution gives the probability of various numbers of red balls when N balls are taken from an urn containing n red balls and M-n blue balls. Note that another popular convention uses the number of red and blue balls rather than the number of red balls and the total number of balls.

Note that the parameters for the log-normal are the mean and standard deviation of the log of the distribution, not the mean and standard deviation of the distribution itself.

The PDF or PMF of a distribution is contained in the extradoc string. For example: