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# pandas.read_gbq

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

  Load data from Google BigQuery.

  Deprecated since version 2.2.0: Please use `pandas_gbq.read_gbq` instead.

  This function requires the pandas-gbq package.

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

  Parameters:
    querystr

      SQL-Like Query to return data values.

    project_idstr, optional

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

    index_colstr, optional

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

    col_orderlist(str), optional

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

    reauthbool, default False

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

    auth_local_webserverbool, default True

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

      New in version 0.2.0 of pandas-gbq.

      Changed in version 1.5.0: Default value is changed to `True`. Google has deprecated the `auth_local_webserver = False` “out of band” (copy-paste) flow.

    dialectstr, 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.

    locationstr, 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.

    configurationdict, optional

      Query config parameters for job processing. For example:

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

      For more information see BigQuery REST API Reference.

    credentialsgoogle.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_apibool, 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.

    max_resultsint, optional

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

    progress_bar_typeOptional, 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.

  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.

  Examples

  Example taken from Google BigQuery documentation

      >>> sql = "SELECT name FROM table_name WHERE state = 'TX' LIMIT 100;"
      >>> df = pd.read_gbq(sql, dialect="standard")
      >>> project_id = "your-project-id"
      >>> df = pd.read_gbq(sql,
      ...                  project_id=project_id,
      ...                  dialect="standard"
      ...                  )