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pandas.DataFrame.to_gbq

DataFrame.to_gbq(self, 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:

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).

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).