def partial_fit(self, X, y=None):
        X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
                        ensure_2d=False, estimator=self, dtype=FLOAT_DTYPES)

        if X.ndim == 1:
            warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)

        if sparse.issparse(X):
            mins, maxs = min_max_axis(X, axis=0)
            max_abs = np.maximum(np.abs(mins), np.abs(maxs))
        else:
            max_abs = np.abs(X).max(axis=0)

        # First pass
        if not hasattr(self, 'n_samples_seen_'):
            self.n_samples_seen_ = X.shape[0]
        # Next passes
        else:
            max_abs = np.maximum(self.max_abs_, max_abs)
            self.n_samples_seen_ += X.shape[0]

        self.max_abs_ = max_abs
        self.scale_ = _handle_zeros_in_scale(max_abs)
        return self
Online computation of max absolute value of X for later scaling.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/preprocessing/data.py/MaxAbsScaler.partial_fit