xorbits.pandas.DataFrame.to_gbq#
- DataFrame.to_gbq(destination_table: str, project_id: str | None = None, chunksize: int | None = None, reauth: bool = False, if_exists: ToGbqIfexist = 'fail', auth_local_webserver: bool = True, table_schema: list[dict[str, str]] | None = None, location: str | None = None, progress_bar: bool = True, credentials=None) 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 raise pandas_gbq.gbq.TableCreationError.
'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 True) –
Use the local webserver flow instead of the console flow when getting user credentials.
New in version 0.2.0 of pandas-gbq.
Changed in version 1.5.0(pandas): Default value is changed to
True
. Google has deprecated theauth_local_webserver = False
“out of band” (copy-paste) flow.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.
See also
pandas_gbq.to_gbq
This function in the pandas-gbq library.
read_gbq
Read a DataFrame from Google BigQuery.
Examples
Example taken from Google BigQuery documentation
>>> project_id = "my-project" >>> table_id = 'my_dataset.my_table' >>> df = pd.DataFrame({ ... "my_string": ["a", "b", "c"], ... "my_int64": [1, 2, 3], ... "my_float64": [4.0, 5.0, 6.0], ... "my_bool1": [True, False, True], ... "my_bool2": [False, True, False], ... "my_dates": pd.date_range("now", periods=3), ... } ... )
>>> df.to_gbq(table_id, project_id=project_id)
Warning
This method has not been implemented yet. Xorbits will try to execute it with pandas.
This docstring was copied from pandas.core.frame.DataFrame.