bigframes

BigQuery DataFrames (BigFrames)

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BigQuery DataFrames (also known as BigFrames) provides a Pythonic DataFrame and machine learning (ML) API powered by the BigQuery engine. It provides modules for many use cases, including:

• bigframes.pandas <https://dataframes.bigquery.dev/reference/api/bigframes.pandas.html>_ is a pandas API for analytics. Many workloads can be migrated from pandas to bigframes by just changing a few imports.
• bigframes.ml <https://dataframes.bigquery.dev/reference/index.html#ml-apis>_ is a scikit-learn-like API for ML.
• bigframes.bigquery.ai <https://dataframes.bigquery.dev/reference/api/bigframes.bigquery.ai.html>_ are a collection of powerful AI methods, powered by Gemini.

BigQuery DataFrames is an open-source package <https://github.com/googleapis/python-bigquery-dataframes>_.

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Getting started with BigQuery DataFrames

The easiest way to get started is to try the BigFrames quickstart <https://cloud.google.com/bigquery/docs/dataframes-quickstart>_ in a notebook in BigQuery Studio <https://cloud.google.com/bigquery/docs/notebooks-introduction>_.

To use BigFrames in your local development environment,

• Run pip install --upgrade bigframes to install the latest version.

• Setup Application default credentials <https://cloud.google.com/docs/authentication/set-up-adc-local-dev-environment>_ for your local development environment enviroment.

• Create a GCP project with the BigQuery API enabled <https://cloud.google.com/bigquery/docs/sandbox>_.

• Use the bigframes package to query data.

.. code-block:: python

import bigframes.pandas as bpd

bpd.options.bigquery.project = your_gcp_project_id  # Optional in BQ Studio.
bpd.options.bigquery.ordering_mode = "partial"  # Recommended for performance.
df = bpd.read_gbq("bigquery-public-data.usa_names.usa_1910_2013")
print(
    df.groupby("name")
    .agg({"number": "sum"})
    .sort_values("number", ascending=False)
    .head(10)
    .to_pandas()
)

Documentation

To learn more about BigQuery DataFrames, visit these pages

• Introduction to BigQuery DataFrames (BigFrames) <https://cloud.google.com/bigquery/docs/bigquery-dataframes-introduction>_
• Sample notebooks <https://github.com/googleapis/python-bigquery-dataframes/tree/main/notebooks>_
• API reference <https://dataframes.bigquery.dev/>_
• Source code (GitHub) <https://github.com/googleapis/python-bigquery-dataframes>_

License

BigQuery DataFrames is distributed with the Apache-2.0 license <https://github.com/googleapis/python-bigquery-dataframes/blob/main/LICENSE>_.

It also contains code derived from the following third-party packages:

• Ibis <https://ibis-project.org/>_
• pandas <https://pandas.pydata.org/>_
• Python <https://www.python.org/>_
• scikit-learn <https://scikit-learn.org/>_
• XGBoost <https://xgboost.readthedocs.io/en/stable/>_
• SQLGlot <https://sqlglot.com/sqlglot.html>_

For details, see the third_party <https://github.com/googleapis/python-bigquery-dataframes/tree/main/third_party/bigframes_vendored>_ directory.

Contact Us

For further help and provide feedback, you can email us at [e-mail] <https://mail.google.com/mail/?view=cm&fs=1&tf=1&to=[e-mail]>_.