10.11.2 pandas.io.gbq.to_gbq

pandas.io.gbq.to_gbq(dataframe, destination_table, project_id, chunksize=10000, verbose=True, reauth=False, if_exists='fail', private_key=None)[source]

Write a DataFrame to a Google BigQuery table.

THIS IS AN EXPERIMENTAL LIBRARY

The main method a user calls to export pandas DataFrame contents to Google BigQuery table.

Google BigQuery API Client Library v2 for Python is used. Documentation is available at https://developers.google.com/api-client-library/python/apis/bigquery/v2

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.

Parameters:

dataframe : DataFrame DataFrame to be written destination_table : string Name of table to be written, in the form ‘dataset.tablename’ project_id : str Google BigQuery Account project ID. chunksize : int (default 10000) Number of rows to be inserted in each chunk from the dataframe. verbose : boolean (default True) Show percentage complete reauth : boolean (default False) Force Google BigQuery to reauthenticate the user. This is useful if multiple accounts are used. if_exists : {‘fail’, ‘replace’, ‘append’}, default ‘fail’ ‘fail’: If table exists, do nothing. ‘replace’: If table exists, drop it, recreate it, and insert data. ‘append’: If table exists, insert data. Create if does not exist. 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)