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scikit-learn 1.3.2
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# `sklearn.preprocessing`.normalize

sklearn.preprocessing.normalize(X, norm='l2', *, axis=1, copy=True, return_norm=False)

  Scale input vectors individually to unit norm (vector length).

  Read more in the User Guide.

  Parameters:
    X{array-like, sparse matrix} of shape (n_samples, n_features)

      The data to normalize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.

    norm{‘l1’, ‘l2’, ‘max’}, default=’l2’

      The norm to use to normalize each non zero sample (or each non-zero feature if axis is 0).

    axis{0, 1}, default=1

      Define axis used to normalize the data along. If 1, independently normalize each sample, otherwise (if 0) normalize each feature.

    copy bool, default=True

      Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix and if axis is 1).

    return_norm bool, default=False

      Whether to return the computed norms.

  Returns:
    X{ndarray, sparse matrix} of shape (n_samples, n_features)

      Normalized input X.

    norms ndarray of shape (n_samples, ) if axis=1 else (n_features, )

      An array of norms along given axis for X. When X is sparse, a NotImplementedError will be raised for norm ‘l1’ or ‘l2’.

  See also

  `Normalizer`

    Performs normalization using the Transformer API (e.g. as part of a preprocessing `Pipeline`).

  Notes

  For a comparison of the different scalers, transformers, and normalizers, see: Compare the effect of different scalers on data with outliers.