Table Of Contents


Enter search terms or a module, class or function name., project_id=None, index_col=None, col_order=None, reauth=False, verbose=True, private_key=None)

Load data from Google BigQuery.


The main method a user calls to execute a Query in Google BigQuery and read results into a pandas DataFrame.

Google BigQuery API Client Library v2 for Python is used. Documentation is available at

Authentication to the Google BigQuery service is via OAuth 2.0. By default user account credentials are used. You will be asked to grant permissions for product name ‘pandas GBQ’. It is also posible to authenticate via service account credentials by using private_key parameter.


query : str

SQL-Like Query to return data values

project_id : str

Google BigQuery Account project ID.

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 reauthenticate the user. This is useful if multiple accounts are used.

verbose : boolean (default True)

Verbose output

private_key : str (optional)

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)


df: DataFrame

DataFrame representing results of query