scipy.stats.chi2 = <scipy.stats._continuous_distns.chi2_gen object at 0x2b652f17f3d0>[source]

A chi-squared continuous random variable.

As an instance of the rv_continuous class, chi2 object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.


The probability density function for chi2 is:

chi2.pdf(x, df) = 1 / (2*gamma(df/2)) * (x/2)**(df/2-1) * exp(-x/2)

chi2 takes df as a shape parameter.

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, chi2.pdf(x, df, loc, scale) is identically equivalent to chi2.pdf(y, df) / scale with y = (x - loc) / scale.


>>> from scipy.stats import chi2
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)

Calculate a few first moments:

>>> df = 55
>>> mean, var, skew, kurt = chi2.stats(df, moments='mvsk')

Display the probability density function (pdf):

>>> x = np.linspace(chi2.ppf(0.01, df),
...                 chi2.ppf(0.99, df), 100)
>>> ax.plot(x, chi2.pdf(x, df),
...        'r-', lw=5, alpha=0.6, label='chi2 pdf')

Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

Freeze the distribution and display the frozen pdf:

>>> rv = chi2(df)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of cdf and ppf:

>>> vals = chi2.ppf([0.001, 0.5, 0.999], df)
>>> np.allclose([0.001, 0.5, 0.999], chi2.cdf(vals, df))

Generate random numbers:

>>> r = chi2.rvs(df, size=1000)

And compare the histogram:

>>> ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)

(Source code)



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

Previous topic


Next topic