def _union(self, objs, index=None, column_i=0):
        """Make a union of the features.

        The term 'union' is based on the terminology of scikit-learn.
        """
        feat_conc = []

        for feat, label in objs:

            # result is tuple of results
            if isinstance(feat, tuple):
                if label is None:
                    label = [None] * len(feat)

                partial_result = self._union(
                    zip(feat, label), column_i=column_i)
                feat_conc.append(partial_result)
                column_i = column_i + partial_result.shape[1]

            # result is pandas.Series.
            elif isinstance(feat, pandas.Series):
                feat.reset_index(drop=True, inplace=True)
                if label is None:
                    label = column_i
                feat.rename(label, inplace=True)
                feat_conc.append(feat)
                column_i = column_i + 1

            # result is pandas.DataFrame
            elif isinstance(feat, pandas.DataFrame):
                feat.reset_index(drop=True, inplace=True)
                if label is None:
                    label = np.arange(column_i, column_i + feat.shape[1])
                feat.columns = label
                feat_conc.append(feat)
                column_i = column_i + feat.shape[1]

            # result is numpy 1d array
            elif is_numpy_like(feat) and len(feat.shape) == 1:
                if label is None:
                    label = column_i
                f = pandas.Series(feat, name=label, copy=False)

                feat_conc.append(f)
                column_i = column_i + 1

            # result is numpy 2d array
            elif is_numpy_like(feat) and len(feat.shape) == 2:
                if label is None:
                    label = np.arange(column_i, column_i + feat.shape[1])
                feat_df = pandas.DataFrame(feat, columns=label, copy=False)
                if label is None:
                    feat_df.columns = [None for _ in range(feat_df.shape[1])]
                feat_conc.append(feat_df)
                column_i = column_i + feat.shape[1]

            # other results are not (yet) supported
            else:
                raise ValueError("expected numpy.ndarray or "
                                 "pandas object to be returned, "
                                 "got '{}'".format(feat.__class__.__name__))

        result = pandas.concat(feat_conc, axis=1, copy=False)
        if index is not None:
            result.set_index(index, inplace=True)

        return result
Make a union of the features.

        The term 'union' is based on the terminology of scikit-learn.