# sklearn.preprocessing.power_transform¶

sklearn.preprocessing.power_transform(X, method=’box-cox’, standardize=True, copy=True)[source]

Apply a power transform featurewise to make data more Gaussian-like.

Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. This is useful for modeling issues related to heteroscedasticity (non-constant variance), or other situations where normality is desired.

Currently, power_transform() supports the Box-Cox transform. Box-Cox requires input data to be strictly positive. The optimal parameter for stabilizing variance and minimizing skewness is estimated through maximum likelihood.

By default, zero-mean, unit-variance normalization is applied to the transformed data.

Read more in the User Guide.

Parameters: X : array-like, shape (n_samples, n_features) The data to be transformed using a power transformation. method : str, (default=’box-cox’) The power transform method. Currently, ‘box-cox’ (Box-Cox transform) is the only option available. standardize : boolean, default=True Set to True to apply zero-mean, unit-variance normalization to the transformed output. copy : boolean, optional, default=True Set to False to perform inplace computation.

PowerTransformer
Performs power transformation using the Transformer API (as part of a preprocessing sklearn.pipeline.Pipeline).
quantile_transform
Maps data to a standard normal distribution with the parameter output_distribution=’normal’.

Notes

NaNs are treated as missing values: disregarded to compute the statistics, and maintained during the data transformation.

For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.

References

G.E.P. Box and D.R. Cox, “An Analysis of Transformations”, Journal of the Royal Statistical Society B, 26, 211-252 (1964).

Examples

>>> import numpy as np
>>> from sklearn.preprocessing import power_transform
>>> data = [[1, 2], [3, 2], [4, 5]]
>>> print(power_transform(data))
[[-1.332... -0.707...]
[ 0.256... -0.707...]
[ 1.076...  1.414...]]