code: def to_dataframe(self, bqstorage_client=None, dtypes=None, progress_bar_type=None):
          
          return self.result().to_dataframe(
              bqstorage_client=bqstorage_client,
              dtypes=dtypes,
              progress_bar_type=progress_bar_type,
          )
docs: Return a pandas DataFrame from a QueryJob
  
          Args:
              bqstorage_client ( \
                  google.cloud.bigquery_storage_v1beta1.BigQueryStorageClient \
              ):
                  **Alpha Feature** Optional. A BigQuery Storage API client. If
                  supplied, use the faster BigQuery Storage API to fetch rows
                  from BigQuery. This API is a billable API.
  
                  This method requires the ``fastavro`` and
                  ``google-cloud-bigquery-storage`` libraries.
  
                  Reading from a specific partition or snapshot is not
                  currently supported by this method.
  
                  **Caution**: There is a known issue reading small anonymous
                  query result tables with the BQ Storage API. Write your query
                  results to a destination table to work around this issue.
              dtypes ( \
                  Map[str, Union[str, pandas.Series.dtype]] \
              ):
                  Optional. A dictionary of column names pandas ``dtype``s. The
                  provided ``dtype`` is used when constructing the series for
                  the column specified. Otherwise, the default pandas behavior
                  is used.
              progress_bar_type (Optional[str]):
                  If set, use the `tqdm <https://tqdm.github.io/>`_ library to
                  display a progress bar while the data downloads. Install the
                  ``tqdm`` package to use this feature.
  
                  See
                  :func:`~google.cloud.bigquery.table.RowIterator.to_dataframe`
                  for details.
  
                  ..versionadded:: 1.11.0
  
          Returns:
              A :class:`~pandas.DataFrame` populated with row data and column
              headers from the query results. The column headers are derived
              from the destination table's schema.
  
          Raises:
              ValueError: If the `pandas` library cannot be imported.