sklearn.preprocessing
.scale¶

sklearn.preprocessing.
scale
(X, axis=0, with_mean=True, with_std=True, copy=True)[source]¶ Standardize a dataset along any axis
Center to the mean and component wise scale to unit variance.
Read more in the User Guide.
Parameters:  X : {arraylike, sparse matrix}
The data to center and scale.
 axis : int (0 by default)
axis used to compute the means and standard deviations along. If 0, independently standardize each feature, otherwise (if 1) standardize each sample.
 with_mean : boolean, True by default
If True, center the data before scaling.
 with_std : boolean, True by default
If True, scale the data to unit variance (or equivalently, unit standard deviation).
 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 CSC matrix and if axis is 1).
See also
StandardScaler
 Performs scaling to unit variance using the``Transformer`` API (e.g. as part of a preprocessing
sklearn.pipeline.Pipeline
).
Notes
This implementation will refuse to center scipy.sparse matrices since it would make them nonsparse and would potentially crash the program with memory exhaustion problems.
Instead the caller is expected to either set explicitly with_mean=False (in that case, only variance scaling will be performed on the features of the CSC matrix) or to call X.toarray() if he/she expects the materialized dense array to fit in memory.
To avoid memory copy the caller should pass a CSC matrix.
NaNs are treated as missing values: disregarded to compute the statistics, and maintained during the data transformation.
We use a biased estimator for the standard deviation, equivalent to numpy.std(x, ddof=0). Note that the choice of ddof is unlikely to affect model performance.
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.