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} 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.
copybool, default=True: If False, try to avoid a copy and normalize in place. This is not guaranteed to always work in place; e.g. if the data is a numpy array with an int dtype, a copy will be returned even with copy=False.
return_normbool, default=False: Whether to return the computed norms.

Returns:

X{ndarray, sparse matrix} of shape (n_samples, n_features): Normalized input X.
normsndarray 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.

Examples

&gt;&gt;&gt; from sklearn.preprocessing import normalize
&gt;&gt;&gt; X = [[-2, 1, 2], [-1, 0, 1]]
&gt;&gt;&gt; normalize(X, norm="l1")  # L1 normalization each row independently
array([[-0.4,  0.2,  0.4],
       [-0.5,  0. ,  0.5]])
&gt;&gt;&gt; normalize(X, norm="l2")  # L2 normalization each row independently
array([[-0.66...,  0.33...,  0.66...],
       [-0.70...,  0.     ,  0.70...]])