'Perform standardization by centering and scaling
Parameters
X : array-like, shape [n_samples, n_features]
The data used to scale along the features axis.
y : (ignored)
.. deprecated:: 0.19
This parameter will be removed in 0.21.
copy : bool, optional (default: None)
Copy the input X or not.'

def transform(self, X, y='deprecated', copy=None):

    if ((not isinstance(y, string_types)) or (y != 'deprecated')):
        warnings.warn('The    parameter    y    on    transform()    is    deprecated    since    0.19    and    will    be    removed    in    0.21', DeprecationWarning)
    check_is_fitted(self, 'scale_')
    copy = (copy if (copy is not None) else self.copy)
    X = check_array(X, accept_sparse='csr', copy=copy, warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES)
    if sparse.issparse(X):
        if self.with_mean:
            raise ValueError('Cannot    center    sparse    matrices:    pass    `with_mean=False`    instead.    See    docstring    for    motivation    and    alternatives.')
        if (self.scale_ is not None):
            inplace_column_scale(X, (1 / self.scale_))
    else:
        if self.with_mean:
            X -= self.mean_
        if self.with_std:
            X /= self.scale_
    return X
