sklearn.preprocessing.quantile_transform(X, axis=0, n_quantiles=1000, output_distribution=’uniform’, ignore_implicit_zeros=False, subsample=100000, random_state=None, copy=False)
[source]
Transform features using quantiles information.
This method transforms the features to follow a uniform or a normal distribution. Therefore, for a given feature, this transformation tends to spread out the most frequent values. It also reduces the impact of (marginal) outliers: this is therefore a robust preprocessing scheme.
The transformation is applied on each feature independently. The cumulative density function of a feature is used to project the original values. Features values of new/unseen data that fall below or above the fitted range will be mapped to the bounds of the output distribution. Note that this transform is nonlinear. It may distort linear correlations between variables measured at the same scale but renders variables measured at different scales more directly comparable.
Read more in the User Guide.
Parameters: 


Attributes: 

See also
QuantileTransformer
Transformer
API (e.g. as part of a preprocessing sklearn.pipeline.Pipeline
).power_transform
scale
robust_scale
NaNs are treated as missing values: disregarded in fit, and maintained in transform.
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
>>> import numpy as np >>> from sklearn.preprocessing import quantile_transform >>> rng = np.random.RandomState(0) >>> X = np.sort(rng.normal(loc=0.5, scale=0.25, size=(25, 1)), axis=0) >>> quantile_transform(X, n_quantiles=10, random_state=0) ... array([...])
sklearn.preprocessing.quantile_transform
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.preprocessing.quantile_transform.html