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 Accountgoogle.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 andgoogle.oauth2.service_account.Credentials.from_service_account_info()
orgoogle.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.