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230 lines
8.1 KiB
Python
230 lines
8.1 KiB
Python
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""" Google BigQuery support """
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
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from pandas.compat._optional import import_optional_dependency
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if TYPE_CHECKING:
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from pandas import DataFrame
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def _try_import():
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# since pandas is a dependency of pandas-gbq
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# we need to import on first use
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msg = (
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"pandas-gbq is required to load data from Google BigQuery. "
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"See the docs: https://pandas-gbq.readthedocs.io."
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)
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pandas_gbq = import_optional_dependency("pandas_gbq", extra=msg)
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return pandas_gbq
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def read_gbq(
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query: str,
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project_id: Optional[str] = None,
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index_col: Optional[str] = None,
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col_order: Optional[List[str]] = None,
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reauth: bool = False,
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auth_local_webserver: bool = False,
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dialect: Optional[str] = None,
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location: Optional[str] = None,
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configuration: Optional[Dict[str, Any]] = None,
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credentials=None,
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use_bqstorage_api: Optional[bool] = None,
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max_results: Optional[int] = None,
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private_key=None,
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verbose=None,
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progress_bar_type: Optional[str] = None,
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) -> "DataFrame":
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"""
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Load data from Google BigQuery.
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This function requires the `pandas-gbq package
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<https://pandas-gbq.readthedocs.io>`__.
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See the `How to authenticate with Google BigQuery
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<https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
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guide for authentication instructions.
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Parameters
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----------
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query : str
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SQL-Like Query to return data values.
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project_id : str, optional
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Google BigQuery Account project ID. Optional when available from
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the environment.
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index_col : str, optional
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Name of result column to use for index in results DataFrame.
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col_order : list(str), optional
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List of BigQuery column names in the desired order for results
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DataFrame.
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reauth : bool, default False
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Force Google BigQuery to re-authenticate the user. This is useful
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if multiple accounts are used.
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auth_local_webserver : bool, default False
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Use the `local webserver flow`_ instead of the `console flow`_
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when getting user credentials.
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.. _local webserver flow:
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https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
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.. _console flow:
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https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
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*New in version 0.2.0 of pandas-gbq*.
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dialect : str, default 'legacy'
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Note: The default value is changing to 'standard' in a future version.
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SQL syntax dialect to use. Value can be one of:
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``'legacy'``
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Use BigQuery's legacy SQL dialect. For more information see
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`BigQuery Legacy SQL Reference
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<https://cloud.google.com/bigquery/docs/reference/legacy-sql>`__.
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``'standard'``
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Use BigQuery's standard SQL, which is
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compliant with the SQL 2011 standard. For more information
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see `BigQuery Standard SQL Reference
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<https://cloud.google.com/bigquery/docs/reference/standard-sql/>`__.
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.. versionchanged:: 0.24.0
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location : str, optional
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Location where the query job should run. See the `BigQuery locations
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documentation
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<https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
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list of available locations. The location must match that of any
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datasets used in the query.
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*New in version 0.5.0 of pandas-gbq*.
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configuration : dict, optional
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Query config parameters for job processing.
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For example:
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configuration = {'query': {'useQueryCache': False}}
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For more information see `BigQuery REST API Reference
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<https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query>`__.
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credentials : google.auth.credentials.Credentials, optional
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Credentials for accessing Google APIs. Use this parameter to override
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default credentials, such as to use Compute Engine
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:class:`google.auth.compute_engine.Credentials` or Service Account
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:class:`google.oauth2.service_account.Credentials` directly.
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*New in version 0.8.0 of pandas-gbq*.
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.. versionadded:: 0.24.0
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use_bqstorage_api : bool, default False
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Use the `BigQuery Storage API
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<https://cloud.google.com/bigquery/docs/reference/storage/>`__ to
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download query results quickly, but at an increased cost. To use this
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API, first `enable it in the Cloud Console
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<https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com>`__.
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You must also have the `bigquery.readsessions.create
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<https://cloud.google.com/bigquery/docs/access-control#roles>`__
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permission on the project you are billing queries to.
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This feature requires version 0.10.0 or later of the ``pandas-gbq``
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package. It also requires the ``google-cloud-bigquery-storage`` and
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``fastavro`` packages.
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.. versionadded:: 0.25.0
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max_results : int, optional
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If set, limit the maximum number of rows to fetch from the query
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results.
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*New in version 0.12.0 of pandas-gbq*.
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.. versionadded:: 1.1.0
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progress_bar_type : Optional, str
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If set, use the `tqdm <https://tqdm.github.io/>`__ library to
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display a progress bar while the data downloads. Install the
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``tqdm`` package to use this feature.
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Possible values of ``progress_bar_type`` include:
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``None``
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No progress bar.
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``'tqdm'``
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Use the :func:`tqdm.tqdm` function to print a progress bar
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to :data:`sys.stderr`.
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``'tqdm_notebook'``
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Use the :func:`tqdm.tqdm_notebook` function to display a
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progress bar as a Jupyter notebook widget.
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``'tqdm_gui'``
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Use the :func:`tqdm.tqdm_gui` function to display a
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progress bar as a graphical dialog box.
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Note that his feature requires version 0.12.0 or later of the
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``pandas-gbq`` package. And it requires the ``tqdm`` package. Slightly
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different than ``pandas-gbq``, here the default is ``None``.
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.. versionadded:: 1.0.0
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Returns
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-------
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df: DataFrame
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DataFrame representing results of query.
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See Also
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--------
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pandas_gbq.read_gbq : This function in the pandas-gbq library.
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DataFrame.to_gbq : Write a DataFrame to Google BigQuery.
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"""
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pandas_gbq = _try_import()
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kwargs: Dict[str, Union[str, bool, int, None]] = {}
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# START: new kwargs. Don't populate unless explicitly set.
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if use_bqstorage_api is not None:
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kwargs["use_bqstorage_api"] = use_bqstorage_api
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if max_results is not None:
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kwargs["max_results"] = max_results
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kwargs["progress_bar_type"] = progress_bar_type
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# END: new kwargs
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return pandas_gbq.read_gbq(
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query,
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project_id=project_id,
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index_col=index_col,
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col_order=col_order,
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reauth=reauth,
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auth_local_webserver=auth_local_webserver,
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dialect=dialect,
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location=location,
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configuration=configuration,
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credentials=credentials,
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**kwargs,
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)
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def to_gbq(
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dataframe: "DataFrame",
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destination_table: str,
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project_id: Optional[str] = None,
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chunksize: Optional[int] = None,
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reauth: bool = False,
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if_exists: str = "fail",
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auth_local_webserver: bool = False,
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table_schema: Optional[List[Dict[str, str]]] = None,
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location: Optional[str] = None,
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progress_bar: bool = True,
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credentials=None,
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verbose=None,
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private_key=None,
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) -> None:
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pandas_gbq = _try_import()
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pandas_gbq.to_gbq(
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dataframe,
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destination_table,
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project_id=project_id,
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chunksize=chunksize,
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reauth=reauth,
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if_exists=if_exists,
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auth_local_webserver=auth_local_webserver,
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table_schema=table_schema,
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location=location,
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progress_bar=progress_bar,
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credentials=credentials,
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verbose=verbose,
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private_key=private_key,
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)
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