pandas.read_gbq

pandas.read_gbq(query, project_id=None, index_col=None, col_order=None, reauth=False, verbose=None, private_key=None, dialect='legacy', **kwargs)

Load data from Google BigQuery.

This function requires the pandas-gbq package.

Authentication to the Google BigQuery service is via OAuth 2.0.

• If “private_key” is not provided:

  By default “application default credentials” are used.

  If default application credentials are not found or are restrictive, user account credentials are used. In this case, you will be asked to grant permissions for product name ‘pandas GBQ’.

• If “private_key” is provided:

  Service account credentials will be used to authenticate.

Parameters:

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.

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).

dialect : str, default ‘legacy’

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.

verbose : boolean, deprecated

Deprecated in Pandas-GBQ 0.4.0. Use the logging module to adjust verbosity instead.

kwargs : dict

Arbitrary keyword arguments. configuration (dict): query config parameters for job processing. For example:

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

For more information see BigQuery SQL Reference

Returns:

df: DataFrame

DataFrame representing results of query.

See also

pandas_gbq.read_gbq

This function in the pandas-gbq library.

pandas.DataFrame.to_gbq

Write a DataFrame to Google BigQuery.