shape (n_samples, n_features)
    The transformed data.

.. warning:: Risk of data leak

    Do not use :func:`~sklearn.preprocessing.minmax_scale` 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.MinMaxScaler` within a
    :ref:`Pipeline <pipeline>` in order to prevent most risks of data
    leaking: `pipe = make_pipeline(MinMaxScaler(), LogisticRegression())`.

See Also
--------
MinMaxScaler : Performs scaling to a given range using the Transformer
    API (e.g. as part of a preprocessing
    :class:`~sklearn.pipeline.Pipeline`).

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

Examples
--------
>>> from sklearn.preprocessing import minmax_scale
>>> X = [[-2, 1, 2], [-1, 0, 1]]
>>> minmax_scale(X, axis=0)  # scale each column independently
array([[0., 1., 1.],
       [1., 0., 0.]])
>>> minmax_scale(X, axis=1)  # scale each row independently
array([[0.  , 0.75, 1.  ],
       [0.  , 0.5 , 1.  ]])
Fr^