(self)
return self._transform
Apply transformers/transformer_resamplers, and transform with the
        final estimator.

        This also works where final estimator is ``None``: all prior
        transformations are applied.

        Parameters
        ----------
        X : iterable
            Data to transform. Must fulfill input requirements of first step
            of the pipeline.

        Returns
        -------
        Xt : array-like, shape (n_samples, n_transformed_features)
Apply transformers/transformer_resamplers, and transform with the
        final estimator.
Apply transformers / transformer_resamplers and transform with the final estimator .
def transform(self):
        """Apply transformers/transformer_resamplers, and transform with the
        final estimator.

        This also works where final estimator is ``None``: all prior
        transformations are applied.

        Parameters
        ----------
        X : iterable
            Data to transform. Must fulfill input requirements of first step
            of the pipeline.

        Returns
        -------
        Xt : array-like, shape (n_samples, n_transformed_features)
        """
        # _final_estimator is None or has transform, otherwise attribute error
        if self._final_estimator != 'passthrough':
            self._final_estimator.transform
        return self._transform
def transform ( self ) : # _final_estimator is None or has transform, otherwise attribute error if self . _final_estimator != 'passthrough' : self . _final_estimator . transform return self . _transform