sklearn.preprocessing
.power_transform¶

sklearn.preprocessing.
power_transform
(X, method=’warn’, standardize=True, copy=True)[source]¶ Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussianlike. This is useful for modeling issues related to heteroscedasticity (nonconstant variance), or other situations where normality is desired.
Currently, power_transform supports the BoxCox transform and the YeoJohnson transform. The optimal parameter for stabilizing variance and minimizing skewness is estimated through maximum likelihood.
BoxCox requires input data to be strictly positive, while YeoJohnson supports both positive or negative data.
By default, zeromean, unitvariance normalization is applied to the transformed data.
Read more in the User Guide.
Parameters:  X : arraylike, shape (n_samples, n_features)
The data to be transformed using a power transformation.
 method : str
The power transform method. Available methods are:
 ‘yeojohnson’ [1], works with positive and negative values
 ‘boxcox’ [2], only works with strictly positive values
The default method will be changed from ‘boxcox’ to ‘yeojohnson’ in version 0.23. To suppress the FutureWarning, explicitly set the parameter.
 standardize : boolean, default=True
Set to True to apply zeromean, unitvariance normalization to the transformed output.
 copy : boolean, optional, default=True
Set to False to perform inplace computation during transformation.
Returns:  X_trans : arraylike, shape (n_samples, n_features)
The transformed data.
See also
PowerTransformer
 Equivalent transformation with the
Transformer
API (e.g. as part of a preprocessingsklearn.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 in
fit
, and maintained intransform
.For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
References
[1] (1, 2) I.K. Yeo and R.A. Johnson, “A new family of power transformations to improve normality or symmetry.” Biometrika, 87(4), pp.954959, (2000). [2] (1, 2) G.E.P. Box and D.R. Cox, “An Analysis of Transformations”, Journal of the Royal Statistical Society B, 26, 211252 (1964). Examples
>>> import numpy as np >>> from sklearn.preprocessing import power_transform >>> data = [[1, 2], [3, 2], [4, 5]] >>> print(power_transform(data, method='boxcox')) [[1.332... 0.707...] [ 0.256... 0.707...] [ 1.076... 1.414...]]