Function scale

sklearn/preprocessing/_data.py:146–302

Standardize a dataset along any axis. Center to the mean and component wise scale to unit variance. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to center and

(X, *, axis=0, with_mean=True, with_std=True, copy=True)

    prefer_skip_nested_validation=True,
)
def scale(X, *, axis=0, with_mean=True, with_std=True, copy=True):
    """Standardize a dataset along any axis.

    Center to the mean and component wise scale to unit variance.

    Read more in the :ref:`User Guide <preprocessing_scaler>`.

    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        The data to center and scale.

    axis : {0, 1}, default=0
        Axis used to compute the means and standard deviations along. If 0,
        independently standardize each feature, otherwise (if 1) standardize
        each sample.

    with_mean : bool, default=True
        If True, center the data before scaling.

    with_std : bool, default=True
        If True, scale the data to unit variance (or equivalently,
        unit standard deviation).

    copy : bool, default=True
        If False, try to avoid a copy and scale 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.

    Returns
    -------
    X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features)
        The transformed data.

    See Also
    --------
    StandardScaler : Performs scaling to unit variance using the Transformer
        API (e.g. as part of a preprocessing
        :class:`~sklearn.pipeline.Pipeline`).

    Notes
    -----
    This implementation will refuse to center scipy.sparse matrices
    since it would make them non-sparse 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