def to_gbq(self, destination_table, project_id=None, chunksize=None,<EOL>reauth=False, if_exists='<STR_LIT>', auth_local_webserver=False,<EOL>table_schema=None, location=None, progress_bar=True,<EOL>credentials=None, verbose=None, private_key=None):
from pandas.io import gbq<EOL>return gbq.to_gbq(<EOL>self, destination_table, project_id=project_id,<EOL>chunksize=chunksize, reauth=reauth, if_exists=if_exists,<EOL>auth_local_webserver=auth_local_webserver,<EOL>table_schema=table_schema, location=location,<EOL>progress_bar=progress_bar, credentials=credentials,<EOL>verbose=verbose, private_key=private_key)<EOL>
Write a DataFrame to a Google BigQuery table.

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, 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.

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

    .. versionadded:: 0.24.0
verbose : bool, deprecated
    Deprecated in pandas-gbq version 0.4.0. Use the `logging module
    to adjust verbosity instead
    <https://pandas-gbq.readthedocs.io/en/latest/intro.html#logging>`__.
private_key : str, deprecated
    Deprecated in pandas-gbq version 0.8.0. Use the ``credentials``
    parameter and
    :func:`google.oauth2.service_account.Credentials.from_service_account_info`
    or
    :func:`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).

See Also
--------
pandas_gbq.to_gbq : This function in the pandas-gbq library.
read_gbq : Read a DataFrame from Google BigQuery.