scipy.stats.

yeojohnson_normplot#

scipy.stats.yeojohnson_normplot(x, la, lb, plot=None, N=80)[source]#

Compute parameters for a Yeo-Johnson normality plot, optionally show it.

A Yeo-Johnson normality plot shows graphically what the best transformation parameter is to use in yeojohnson to obtain a distribution that is close to normal.

Parameters:

xarray_like: Input array.
la, lbscalar: The lower and upper bounds for the lmbda values to pass to yeojohnson for Yeo-Johnson transformations. These are also the limits of the horizontal axis of the plot if that is generated.
plotobject, optional: If given, plots the quantiles and least squares fit. plot is an object that has to have methods “plot” and “text”. The matplotlib.pyplot module or a Matplotlib Axes object can be used, or a custom object with the same methods. Default is None, which means that no plot is created.
Nint, optional: Number of points on the horizontal axis (equally distributed from la to lb).

Returns:

lmbdasndarray: The lmbda values for which a Yeo-Johnson transform was done.
ppccndarray: Probability Plot Correlation Coefficient, as obtained from probplot when fitting the Box-Cox transformed input x against a normal distribution.

See also

probplot, yeojohnson, yeojohnson_normmax, yeojohnson_llf, ppcc_max

Notes

Even if plot is given, the figure is not shown or saved by boxcox_normplot; plt.show() or plt.savefig('figname.png') should be used after calling probplot.

Added in version 1.2.0.

Array API Standard Support

yeojohnson_normplot has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	⛔
PyTorch	⛔	⛔
JAX	⛔	⛔
Dask	⛔	n/a

See Support for the array API standard for more information.

Examples

>>> from scipy import stats
>>> import matplotlib.pyplot as plt

Generate some non-normally distributed data, and create a Yeo-Johnson plot:

>>> x = stats.loggamma.rvs(5, size=500) + 5
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> prob = stats.yeojohnson_normplot(x, -20, 20, plot=ax)

Determine and plot the optimal lmbda to transform x and plot it in the same plot:

>>> _, maxlog = stats.yeojohnson(x)
>>> ax.axvline(maxlog, color='r')

>>> plt.show()

../../_images/scipy-stats-yeojohnson_normplot-1.png