scipy.stats.lognorm

scipy.stats.lognorm(*args, **kwds) = <scipy.stats._continuous_distns.lognorm_gen object>

A lognormal continuous random variable.

As an instance of the rv_continuous class, lognorm 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 probability density function for lognorm is:

\[f(x, s) = \frac{1}{s x \sqrt{2\pi}} \exp\left(-\frac{\log^2(x)}{2s^2}\right)\]

for \(x > 0\), \(s > 0\).

lognorm takes s as a shape parameter for \(s\).

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, lognorm.pdf(x, s, loc, scale) is identically equivalent to lognorm.pdf(y, s) / scale with y = (x - loc) / scale. Note that shifting the location of a distribution does not make it a “noncentral” distribution; noncentral generalizations of some distributions are available in separate classes.

A common parametrization for a lognormal random variable Y is in terms of the mean, mu, and standard deviation, sigma, of the unique normally distributed random variable X such that exp(X) = Y. This parametrization corresponds to setting s = sigma and scale = exp(mu).

Examples

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

Calculate a few first moments:

>>> s = 0.954
>>> mean, var, skew, kurt = lognorm.stats(s, moments='mvsk')

Display the probability density function (pdf):

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

Check accuracy of cdf and ppf:

>>> vals = lognorm.ppf([0.001, 0.5, 0.999], s)
>>> np.allclose([0.001, 0.5, 0.999], lognorm.cdf(vals, s))
True

Generate random numbers:

>>> r = lognorm.rvs(s, size=1000)

And compare the histogram:

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

Methods

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