pandas.DataFrame.to_gbq

DataFrame.to_gbq(destination_table, project_id=None, chunksize=None, reauth=False, if_exists='fail', auth_local_webserver=False, table_schema=None, location=None, progress_bar=True, credentials=None, verbose=None, private_key=None) [source]

Write a DataFrame to a Google BigQuery table.

This function requires the pandas-gbq package.

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

Parameters:

destination_table : str Name of table to be written, in the form dataset.tablename. project_id : str, optional Google BigQuery Account project ID. Optional when available from the environment. chunksize : int, optional Number of rows to be inserted in each chunk from the dataframe. Set to None to load the whole dataframe at once. reauth : bool, default False Force Google BigQuery to re-authenticate the user. This is useful if multiple accounts are used. if_exists : str, default ‘fail’ Behavior when the destination table exists. Value can be one of: '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. auth_local_webserver : bool, 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. table_schema : list of dicts, optional List of BigQuery table fields to which according DataFrame columns conform to, e.g. [{'name': 'col1', 'type': 'STRING'},...]. If schema is not provided, it will be generated according to dtypes of DataFrame columns. See BigQuery API documentation on available names of a field. New in version 0.3.1 of pandas-gbq. location : str, optional Location where the load job should run. See the BigQuery locations documentation for a list of available locations. The location must match that of the target dataset. New in version 0.5.0 of pandas-gbq. progress_bar : bool, default True Use the library tqdm to show the progress bar for the upload, chunk by chunk. New in version 0.5.0 of pandas-gbq. 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. verbose : bool, deprecated Deprecated in pandas-gbq version 0.4.0. Use the logging module to adjust verbosity instead. 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).

See also

pandas_gbq.to_gbq

This function in the pandas-gbq library.

pandas.read_gbq

Read a DataFrame from Google BigQuery.