# pandas.read_gbq

pandas.read_gbq(query, project_id=None, index_col=None, col_order=None, reauth=False, auth_local_webserver=False, dialect=None, location=None, configuration=None, credentials=None, use_bqstorage_api=None, max_results=None, progress_bar_type=None)

  Load data from Google BigQuery.

  This function requires the pandas-gbq package.

  See the How to authenticate with Google BigQuery guide for authentication instructions.

  Parameters
    query str

      SQL-Like Query to return data values.

    project_id str, optional

      Google BigQuery Account project ID. Optional when available from the environment.

    index_col str, optional

      Name of result column to use for index in results DataFrame.

    col_order list(str), optional

      List of BigQuery column names in the desired order for results DataFrame.

    reauth bool, default False

      Force Google BigQuery to re-authenticate the user. This is useful if multiple accounts are used.

    auth_local_webserver bool, default False

      Use the local webserver flow instead of the console flow when getting user credentials.

      New in version 0.2.0 of pandas-gbq.

    dialect str, default ‘legacy’

      Note: The default value is changing to ‘standard’ in a future version.

      SQL syntax dialect to use. Value can be one of:

      `'legacy'`

        Use BigQuery’s legacy SQL dialect. For more information see BigQuery Legacy SQL Reference.

      `'standard'`

        Use BigQuery’s standard SQL, which is compliant with the SQL 2011 standard. For more information see BigQuery Standard SQL Reference.

    location str, optional

      Location where the query job should run. See the BigQuery locations documentation for a list of available locations. The location must match that of any datasets used in the query.

      New in version 0.5.0 of pandas-gbq.

    configuration dict, optional

      Query config parameters for job processing. For example:

      > configuration = {‘query’: {‘useQueryCache’: False}}

      For more information see BigQuery REST API Reference.

    credentials google.auth.credentials.Credentials, optional

      Credentials for accessing Google APIs. Use this parameter to override default credentials, such as to use Compute Engine `google.auth.compute_engine.Credentials` or Service Account `google.oauth2.service_account.Credentials` directly.

      New in version 0.8.0 of pandas-gbq.

    use_bqstorage_api bool, default False

      Use the BigQuery Storage API to download query results quickly, but at an increased cost. To use this API, first enable it in the Cloud Console. You must also have the bigquery.readsessions.create permission on the project you are billing queries to.

      This feature requires version 0.10.0 or later of the `pandas-gbq` package. It also requires the `google-cloud-bigquery-storage` and `fastavro` packages.

      New in version 0.25.0.

    max_results int, optional

      If set, limit the maximum number of rows to fetch from the query results.

      New in version 0.12.0 of pandas-gbq.

      New in version 1.1.0.

    progress_bar_type Optional, str

      If set, use the tqdm library to display a progress bar while the data downloads. Install the `tqdm` package to use this feature.

      Possible values of `progress_bar_type` include:

      `None`

        No progress bar.

      `'tqdm'`

        Use the `tqdm.tqdm()` function to print a progress bar to `sys.stderr`.

      `'tqdm_notebook'`

        Use the `tqdm.tqdm_notebook()` function to display a progress bar as a Jupyter notebook widget.

      `'tqdm_gui'`

        Use the `tqdm.tqdm_gui()` function to display a progress bar as a graphical dialog box.

      Note that this feature requires version 0.12.0 or later of the `pandas-gbq` package. And it requires the `tqdm` package. Slightly different than `pandas-gbq`, here the default is `None`.

      New in version 1.0.0.

  Returns
    df: DataFrame

      DataFrame representing results of query.

  See also

  `pandas_gbq.read_gbq`

    This function in the pandas-gbq library.

  `DataFrame.to_gbq`

    Write a DataFrame to Google BigQuery.