code: def from_pd_data_frame(self, local_df):
          
          def frame_to_rows(frame):
              """Convert a Pandas DataFrame into a list of Spark SQL Rows"""
              # TODO: Convert to row objects directly?
              return [r.tolist() for r in frame.to_records()]
          schema = list(local_df.columns)
          index_names = list(local_df.index.names)
          index_names = _normalize_index_names(index_names)
          schema = index_names + schema
          rows = self.spark_ctx.parallelize(frame_to_rows(local_df))
          sp_df = DataFrame.from_schema_rdd(
              self.sql_ctx.createDataFrame(
                  rows,
                  schema=schema,
                  # Look at all the rows, should be ok since coming from
                  # a local dataset
                  samplingRatio=1))
          sp_df._index_names = index_names
          return sp_df
docs: Make a Sparkling Pandas dataframe from a local Pandas DataFrame.
          The intend use is for testing or joining distributed data with local
          data.
          The types are re-infered, so they may not match.
          Parameters
          ----------
          local_df: Pandas DataFrame
              The data to turn into a distributed Sparkling Pandas DataFrame.
              See http://bit.ly/pandasDataFrame for docs.
          Returns
          -------
          A Sparkling Pandas DataFrame.