# 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, private_key=None, verbose=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 : boolean, default False

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

  auth_local_webserver : boolean, 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 verion.

  > 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.

  > Changed in version 0.24.0.

  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.

  > New in version 0.24.0.

  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.

  private_key : str, deprecated

  > Deprecated in pandas-gbq version 0.8.0. Use the `credentials` parameter and `google.oauth2.service_account.Credentials.from_service_account_info()` or `google.oauth2.service_account.Credentials.from_service_account_file()` instead.

  > Service account private key in JSON format. Can be file path or string contents. This is useful for remote server authentication (eg. Jupyter/IPython notebook on remote host).

  verbose : None, deprecated

  > Deprecated in pandas-gbq version 0.4.0. Use the logging module to adjust verbosity instead.

  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.

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