Table Of Contents

Search

Enter search terms or a module, class or function name.

pandas.DataFrame.to_gbq

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

Write a DataFrame to a Google BigQuery table.

This function requires the pandas-gbq package.

Authentication to the Google BigQuery service is via OAuth 2.0.

  • If private_key is provided, the library loads the JSON service account credentials and uses those to authenticate.
  • If no private_key is provided, the library tries application default credentials.
  • If application default credentials are not found or cannot be used with BigQuery, the library authenticates with user account credentials. In this case, you will be asked to grant permissions for product name ‘pandas GBQ’.
Parameters:

destination_table : str

Name of table to be written, in the form ‘dataset.tablename’.

project_id : str

Google BigQuery Account project ID.

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 reauthenticate 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.

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).

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.

verbose : boolean, deprecated

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

See also

pandas_gbq.to_gbq
This function in the pandas-gbq library.
pandas.read_gbq
Read a DataFrame from Google BigQuery.
Scroll To Top