class dask_ml.preprocessing.QuantileTransformer(*, n_quantiles=1000, output_distribution='uniform', ignore_implicit_zeros=False, subsample=100000, random_state=None, copy=True)

Transforms features using quantile information.

This implementation differs from the scikit-learn implementation by using approximate quantiles. The scikit-learn docstring follows.

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. First an estimate of the cumulative distribution function of a feature is used to map the original values to a uniform distribution. The obtained values are then mapped to the desired output distribution using the associated quantile function. 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 non-linear. 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.

New in version 0.19.

n_quantilesint, default=1000 or n_samples

Number of quantiles to be computed. It corresponds to the number of landmarks used to discretize the cumulative distribution function. If n_quantiles is larger than the number of samples, n_quantiles is set to the number of samples as a larger number of quantiles does not give a better approximation of the cumulative distribution function estimator.

output_distribution{‘uniform’, ‘normal’}, default=’uniform’

Marginal distribution for the transformed data. The choices are ‘uniform’ (default) or ‘normal’.

ignore_implicit_zerosbool, default=False

Only applies to sparse matrices. If True, the sparse entries of the matrix are discarded to compute the quantile statistics. If False, these entries are treated as zeros.

subsampleint, default=1e5

Maximum number of samples used to estimate the quantiles for computational efficiency. Note that the subsampling procedure may differ for value-identical sparse and dense matrices.

random_stateint, RandomState instance or None, default=None

Determines random number generation for subsampling and smoothing noise. Please see subsample for more details. Pass an int for reproducible results across multiple function calls. See Glossary.

copybool, default=True

Set to False to perform inplace transformation and avoid a copy (if the input is already a numpy array).


The actual number of quantiles used to discretize the cumulative distribution function.

quantiles_ndarray of shape (n_quantiles, n_features)

The values corresponding the quantiles of reference.

references_ndarray of shape (n_quantiles, )

Quantiles of references.


Number of features seen during fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

See also


Equivalent function without the estimator API.


Perform mapping to a normal distribution using a power transform.


Perform standardization that is faster, but less robust to outliers.


Perform robust standardization that removes the influence of outliers but does not put outliers and inliers on the same 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 QuantileTransformer
>>> rng = np.random.RandomState(0)
>>> X = np.sort(rng.normal(loc=0.5, scale=0.25, size=(25, 1)), axis=0)
>>> qt = QuantileTransformer(n_quantiles=10, random_state=0)
>>> qt.fit_transform(X)


fit(X[, y])

Compute the quantiles used for transforming.

fit_transform(X[, y])

Fit to data, then transform it.


Get output feature names for transformation.


Get parameters for this estimator.


Back-projection to the original space.


Set the parameters of this estimator.


Feature-wise transformation of the data.

__init__(*, n_quantiles=1000, output_distribution='uniform', ignore_implicit_zeros=False, subsample=100000, random_state=None, copy=True)