code: def _rdd(self):
          
          columns = self._schema_rdd.columns
          index_names = self._index_names
  
          def fromRecords(records):
              if not records:
                  return []
              else:
                  loaded_df = pd.DataFrame.from_records([records],
                                                        columns=columns)
                  indexed_df = _update_index_on_df(loaded_df, index_names)
                  return [indexed_df]
  
          return self._schema_rdd.rdd.flatMap(fromRecords)
docs: Return an RDD of Panda DataFrame objects. This can be expensive
          especially if we don't do a narrow transformation after and get it back
          to Spark SQL land quickly.