# sklearn.preprocessing.normalize¶

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.

Parameters: 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). axis : 0 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. copy : boolean, 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_norm : boolean, default False whether to return the computed norms X : {array-like, sparse matrix}, shape [n_samples, n_features] Normalized input X. norms : array, 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’.

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

Notes

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