Method fit

sklearn/preprocessing/_data.py:903–928

Compute the mean and std to be used for later scaling. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data used to compute the mean and standard deviation used for later scaling along the features axis.

(self, X, y=None, sample_weight=None)

            del self.var_

    def fit(self, X, y=None, sample_weight=None):
        """Compute the mean and std to be used for later scaling.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The data used to compute the mean and standard deviation
            used for later scaling along the features axis.

        y : None
            Ignored.

        sample_weight : array-like of shape (n_samples,), default=None
            Individual weights for each sample.

            .. versionadded:: 0.24
               parameter *sample_weight* support to StandardScaler.

        Returns
        -------
        self : object
            Fitted scaler.
        """
        # Reset internal state before fitting
        self._reset()
        return self.partial_fit(X, y, sample_weight)

    @_fit_context(prefer_skip_nested_validation=True)
    def partial_fit(self, X, y=None, sample_weight=None):