def fit_transform(self, Z):
return self.fit(Z).transform(Z)<EOL>
Learn a list of feature name -> indices mappings and transform Z.
        Like fit(Z) followed by transform(Z).

        Parameters
        ----------
        Z : Z : ArrayRDD or DictRDD with column 'X' containing Mapping or
            iterable over Mappings
            Dict(s) or Mapping(s) from feature names (arbitrary Python
            objects) to feature values (strings or convertible to dtype).

        Returns
        -------
        Z : transformed, containing {array, sparse matrix}
            Feature vectors; always 2-d.