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

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

Read more in the User Guide.

X{array-like, sparse matrix}, 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’, or ‘max’, optional (‘l2’ by default)

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

axis0 or 1, optional (1 by default)

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

copyboolean, optional, 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_normboolean, default False

whether to return the computed norms

X{array-like, sparse matrix}, shape [n_samples, n_features]

Normalized input X.

normsarray, 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


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


For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/