ngs.

    .. versionadded:: 1.0

See Also
--------
power_transform : Equivalent function without the estimator API.

QuantileTransformer : Maps data to a standard normal distribution with
    the parameter `output_distribution='normal'`.

Notes
-----
NaNs are treated as missing values: disregarded in ``fit``, and maintained
in ``transform``.

References
----------

.. [1] :doi:`I.K. Yeo and R.A. Johnson, "A new family of power
       transformations to improve normality or symmetry." Biometrika,
       87(4), pp.954-959, (2000). <10.1093/biomet/87.4.954>`

.. [2] :doi:`G.E.P. Box and D.R. Cox, "An Analysis of Transformations",
       Journal of the Royal Statistical Society B, 26, 211-252 (1964).
       <10.1111/j.2517-6161.1964.tb00553.x>`

Examples
--------
>>> import numpy as np
>>> from sklearn.preprocessing import PowerTransformer
>>> pt = PowerTransformer()
>>> data = [[1, 2], [3, 2], [4, 5]]
>>> print(pt.fit(data))
PowerTransformer()
>>> print(pt.lambdas_)
[ 1.386... -3.100...]
>>> print(pt.transform(data))
[[-1.316... -0.707...]
 [ 0.209... -0.707...]
 [ 1.106...  1.414...]]
ú