scipy.stats.skewnorm = <scipy.stats._continuous_distns.skew_norm_gen object at 0x2b2318ed1d50>[source]

A skew-normal random variable.

As an instance of the rv_continuous class, skewnorm 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 pdf is:

skewnorm.pdf(x, a) = 2*norm.pdf(x)*norm.cdf(ax)

skewnorm takes a as a skewness parameter When a=0 the distribution is identical to a normal distribution. rvs implements the method of [R636].

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


[R636](1, 2) A. Azzalini and A. Capitanio (1999). Statistical applications of the multivariate skew-normal distribution. J. Roy. Statist. Soc., B 61, 579-602.


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

Calculate a few first moments:

>>> a = 4
>>> mean, var, skew, kurt = skewnorm.stats(a, moments='mvsk')

Display the probability density function (pdf):

>>> x = np.linspace(skewnorm.ppf(0.01, a),
...                 skewnorm.ppf(0.99, a), 100)
>>> ax.plot(x, skewnorm.pdf(x, a),
...        'r-', lw=5, alpha=0.6, label='skewnorm 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 = skewnorm(a)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of cdf and ppf:

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

Generate random numbers:

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

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