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Classe « DataFrame »

Méthode pandas.DataFrame.to_gbq

Signature de la méthode to_gbq

def to_gbq(self, 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' 

Description

help(DataFrame.to_gbq)

Write a DataFrame to a Google BigQuery table.

.. deprecated:: 2.2.0

   Please use ``pandas_gbq.to_gbq`` instead.

This function requires the `pandas-gbq package
<https://pandas-gbq.readthedocs.io>`__.

See the `How to authenticate with Google BigQuery
<https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
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.

    .. _local webserver flow:
        https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
    .. _console flow:
        https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console

    *New in version 0.2.0 of pandas-gbq*.

    .. versionchanged:: 1.5.0
       Default value is changed to ``True``. Google has deprecated the
       ``auth_local_webserver = False`` `"out of band" (copy-paste)
       flow
       <https://developers.googleblog.com/2022/02/making-oauth-flows-safer.html?m=1#disallowed-oob>`_.
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
    <https://cloud.google.com/bigquery/docs/dataset-locations>`__ 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
    :class:`google.auth.compute_engine.Credentials` or Service
    Account :class:`google.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
<https://cloud.google.com/bigquery/docs/samples/bigquery-pandas-gbq-to-gbq-simple>`_

>>> 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)  # doctest: +SKIP

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