al when available from
            the environment.
        chunksize : int, optional
            Number of rows to be inserted in each chunk from the dataframe.
            Set to ``None`` to load the whole dataframe at once.
        reauth : bool, default False
            Force Google BigQuery to re-authenticate the user. This is useful
            if multiple accounts are used.
        if_exists : str, default 'fail'
            Behavior when the destination table exists. Value can be one of:

            ``'fail'``
                If table exists raise pandas_gbq.gbq.TableCreationError.
            ``'replace'``
                If table exists, drop it, recreate it, and insert data.
            ``'append'``
                If table exists, insert data. Create if does not exist.
        auth_local_webserver : bool, default True
            Use the `local webserver flow`_ instead of the `console flow`_
            when getting user credentials.

            .. _local webserver flow:
                https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
            .. _console flow:
                https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console

            *New in version 0.2.0 of pandas-gbq*.

            .. versionchanged:: 1.5.0
               Default value is changed to ``True``. Google has deprecated the
               ``auth_local_webserver = False`` `"out of band" (copy-paste)
               flow
               <https://developers.googleblog.com/2022/02/making-oauth-flows-safer.html?m=1#disallowed-oob>`_.
        table_schema : list of dicts, optional
            List of BigQuery table fields to which according DataFrame
            columns conform to, e.g. ``[{'name': 'col1', 'type':
            'STRING'},...]``. If schema is not provided, it will be
            generated according to dtypes of DataFrame columns. See
            BigQuery API documentation on available names of a field.

            *New in version 0.3.1 of pandas-gbq*.
        location : str, optional
            Location where the load job should run. See the `BigQuery locations
            documentation
            <https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
            list of available locations. The location must match that of the
            target dataset.

            *New in version 0.5.0 of pandas-gbq*.
        progress_bar : bool, default True
            Use the library `tqdm` to show the progress bar for the upload,
            chunk by chunk.

            *New in version 0.5.0 of pandas-gbq*.
        credentials : google.auth.credentials.Credentials, optional
            Credentials for accessing Google APIs. Use this parameter to
            override default credentials, such as to use Compute Engine
            :class:`google.auth.compute_engine.Credentials` or Service
            Account :class:`google.oauth2.service_account.Credentials`
            directly.

            *New in version 0.8.0 of pandas-gbq*.

        See Also
        --------
        pandas_gbq.to_gbq : This function in the pandas-gbq library.
        read_gbq : Read a DataFrame from Google BigQuery.

        Examples
        --------
        Example taken from `Google BigQuery documentation
        <https://cloud.google.com/bigquery/docs/samples/bigquery-pandas-gbq-to-gbq-simple>`_

        >>> project_id = "my-project"
        >>> table_id = 'my_dataset.my_table'
        >>> df = pd.DataFrame({
        ...                   "my_string": ["a", "b", "c"],
        ...                   "my_int64": [1, 2, 3],
        ...                   "my_float64": [4.0, 5.0, 6.0],
        ...                   "my_bool1": [True, False, True],
        ...                   "my_bool2": [False, True, False],
        ...                   "my_dates": pd.date_range("now", periods=3),
        ...                   }
        ...                   )

        >>> df.to_gbq(table_id, project_id=project_id)  # doctest: +SKIP
        r