SciPy

scipy.stats.skewnorm

scipy.stats.skewnorm(*args, **kwds) = <scipy.stats._continuous_distns.skew_norm_gen object>[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.

Notes

The pdf is:

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

skewnorm takes a real number \(a\) as a skewness parameter When a = 0 the distribution is identical to a normal distribution (norm). rvs implements the method of [1].

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.

References

1

A. Azzalini and A. Capitanio (1999). Statistical applications of the multivariate skew-normal distribution. J. Roy. Statist. Soc., B 61, 579-602. arXiv:0911.2093

Examples

>>> 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))
True

Generate random numbers:

>>> r = skewnorm.rvs(a, size=1000)

And compare the histogram:

>>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()
../_images/scipy-stats-skewnorm-1.png

Methods

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)

Parameter estimates for generic data. See scipy.stats.rv_continuous.fit for detailed documentation of the keyword arguments.

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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