e
    Transformer API (e.g. as part of a preprocessing
    :class:`~sklearn.pipeline.Pipeline`).
power_transform : Maps data to a normal distribution using a
    power transformation.
scale : Performs standardization that is faster, but less robust
    to outliers.
robust_scale : Performs robust standardization that removes the influence
    of outliers but does not put outliers and inliers on the same scale.

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

.. warning:: Risk of data leak

    Do not use :func:`~sklearn.preprocessing.quantile_transform` unless
    you know what you are doing. A common mistake is to apply it
    to the entire data *before* splitting into training and
    test sets. This will bias the model evaluation because
    information would have leaked from the test set to the
    training set.
    In general, we recommend using
    :class:`~sklearn.preprocessing.QuantileTransformer` within a
    :ref:`Pipeline <pipeline>` in order to prevent most risks of data
    leaking:`pipe = make_pipeline(QuantileTransformer(),
    LogisticRegression())`.

For a comparison of the different scalers, transformers, and normalizers,
see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`.

Examples
--------
>>> 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, copy=True)
array([...])
)