(self, X, y=None, **fit_params)
return Xt
Fit all transformers, transform the data and concatenate results.
        Parameters
        ----------
        X : iterable or array-like, depending on transformers
            Input data to be transformed.
        y : array-like of shape (n_samples, ...), optional
            Targets for supervised learning.
        Returns
        -------
        Xt : list of ndarray
            List of results of transformers.
Fit all transformers, transform the data and concatenate results.
        Parameters
        ----------
        X : iterable or array-like, depending on transformers
            Input data to be transformed.
        y : array-like of shape (n_samples, ...), optional
            Targets for supervised learning.
        Returns
        -------
        Xt : list of ndarray
            List of results of transformers.
Fit all transformers transform the data and concatenate results . Parameters ---------- X : iterable or array - like depending on transformers Input data to be transformed . y : array - like of shape ( n_samples ... ) optional Targets for supervised learning . Returns ------- Xt : list of ndarray List of results of transformers .
def fit_transform(self, X, y=None, **fit_params):
        """Fit all transformers, transform the data and concatenate results.
        Parameters
        ----------
        X : iterable or array-like, depending on transformers
            Input data to be transformed.
        y : array-like of shape (n_samples, ...), optional
            Targets for supervised learning.
        Returns
        -------
        Xt : list of ndarray
            List of results of transformers.

        """
        results = self._parallel_func(X, y, fit_params, _fit_transform_one)
        if not results:
            # All transformers are None
            return np.zeros((X.shape[0], 0))

        Xt, transformers = zip(*results)
        self._update_transformer_list(transformers)
        Xt = list(Xt)
        return Xt
def fit_transform ( self , X , y = None , * * fit_params ) : results = self . _parallel_func ( X , y , fit_params , _fit_transform_one ) if not results : # All transformers are None return np . zeros ( ( X . shape [ 0 ] , 0 ) ) Xt , transformers = zip ( * results ) self . _update_transformer_list ( transformers ) Xt = list ( Xt ) return Xt