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This repository was archived by the owner on May 7, 2026. It is now read-only.

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docs: update API reference to new dataframes.bigquery.dev location (#2293)
Also: - include link to `bigframes.bigquery.ai` in README - add partial ordering mode recommendation to starter sample - remove 2.0 warning Thank you for opening a Pull Request! Before submitting your PR, there are a few things you can do to make sure it goes smoothly: - [ ] Make sure to open an issue as a [bug/issue](https://github.com/googleapis/python-bigquery-dataframes/issues/new/choose) before writing your code! That way we can discuss the change, evaluate designs, and agree on the general idea - [ ] Ensure the tests and linter pass - [ ] Code coverage does not decrease (if any source code was changed) - [ ] Appropriate docs were updated (if necessary) Towards b/454350869 🦕
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@@ -6,15 +6,25 @@ BigQuery DataFrames (BigFrames)
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|GA| |pypi| |versions|
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BigQuery DataFrames (also known as BigFrames) provides a Pythonic DataFrame
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and machine learning (ML) API powered by the BigQuery engine.
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and machine learning (ML) API powered by the BigQuery engine. It provides modules
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for many use cases, including:
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* `bigframes.pandas` provides a pandas API for analytics. Many workloads can be
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* `bigframes.pandas <https://dataframes.bigquery.dev/reference/api/bigframes.pandas.html>`_
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is a pandas API for analytics. Many workloads can be
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migrated from pandas to bigframes by just changing a few imports.
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* ``bigframes.ml`` provides a scikit-learn-like API for ML.
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* `bigframes.ml <https://dataframes.bigquery.dev/reference/index.html#ml-apis>`_
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is a scikit-learn-like API for ML.
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* `bigframes.bigquery.ai <https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ai.html>`_
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are a collection of powerful AI methods, powered by Gemini.
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BigQuery DataFrames is an open-source package.
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BigQuery DataFrames is an `open-source package <https://github.com/googleapis/python-bigquery-dataframes>`_.
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**Version 2.0 introduces breaking changes for improved security and performance. See below for details.**
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.. |GA| image:: https://img.shields.io/badge/support-GA-gold.svg
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:target: https://github.com/googleapis/google-cloud-python/blob/main/README.rst#general-availability
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.. |pypi| image:: https://img.shields.io/pypi/v/bigframes.svg
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:target: https://pypi.org/project/bigframes/
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.. |versions| image:: https://img.shields.io/pypi/pyversions/bigframes.svg
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:target: https://pypi.org/project/bigframes/
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Getting started with BigQuery DataFrames
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----------------------------------------
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import bigframes.pandas as bpd
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bpd.options.bigquery.project = your_gcp_project_id
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bpd.options.bigquery.project = your_gcp_project_id # Optional in BQ Studio.
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bpd.options.bigquery.ordering_mode = "partial" # Recommended for performance.
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df = bpd.read_gbq("bigquery-public-data.usa_names.usa_1910_2013")
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print(
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df.groupby("name")
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.to_pandas()
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)
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Documentation
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-------------
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To learn more about BigQuery DataFrames, visit these pages
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* `Introduction to BigQuery DataFrames (BigFrames) <https://cloud.google.com/bigquery/docs/bigquery-dataframes-introduction>`_
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* `Sample notebooks <https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks>`_
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* `API reference <https://cloud.google.com/python/docs/reference/bigframes/latest/summary_overview>`_
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* `API reference <https://dataframes.bigquery.dev/>`_
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* `Source code (GitHub) <https://github.com/googleapis/python-bigquery-dataframes>`_
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⚠️ Warning: Breaking Changes in BigQuery DataFrames v2.0
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--------------------------------------------------------
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Version 2.0 introduces breaking changes for improved security and performance. Key default behaviors have changed, including
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* **Large Results (>10GB):** The default value for ``allow_large_results`` has changed to ``False``.
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Methods like ``to_pandas()`` will now fail if the query result's compressed data size exceeds 10GB,
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unless large results are explicitly permitted.
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* **Remote Function Security:** The library no longer automatically lets the Compute Engine default service
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account become the identity of the Cloud Run functions. If that is desired, it has to be indicated by passing
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``cloud_function_service_account="default"``. And network ingress now defaults to ``"internal-only"``.
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* **@remote_function Argument Passing:** Arguments other than ``input_types``, ``output_type``, and ``dataset``
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to ``remote_function`` must now be passed using keyword syntax, as positional arguments are no longer supported.
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* **@udf Argument Passing:** Arguments ``dataset`` and ``name`` to ``udf`` are now mandatory.
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* **Endpoint Connections:** Automatic fallback to locational endpoints in certain regions is removed.
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* **LLM Updates (Gemini Integration):** Integrations now default to the ``gemini-2.0-flash-001`` model.
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PaLM2 support has been removed; please migrate any existing PaLM2 usage to Gemini. **Note:** The current default
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model will be removed in Version 3.0.
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**Important:** If you are not ready to adapt to these changes, please pin your dependency to a version less than 2.0
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(e.g., ``bigframes==1.42.0``) to avoid disruption.
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To learn about these changes and how to migrate to version 2.0, see the
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`updated introduction guide <https://cloud.google.com/bigquery/docs/bigquery-dataframes-introduction>`_.
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.. |GA| image:: https://img.shields.io/badge/support-GA-gold.svg
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:target: https://github.com/googleapis/google-cloud-python/blob/main/README.rst#general-availability
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.. |pypi| image:: https://img.shields.io/pypi/v/bigframes.svg
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:target: https://pypi.org/project/bigframes/
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.. |versions| image:: https://img.shields.io/pypi/pyversions/bigframes.svg
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:target: https://pypi.org/project/bigframes/
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License
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-------
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