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

• bigframes.pandasis a pandas API for analytics. Many workloads can bemigrated from pandas to bigframes by just changing a few imports.
• bigframes.mlis a scikit-learn-like API for ML.
• bigframes.bigquery.aiare a collection of powerful AI methods, powered by Gemini.

BigQuery DataFrames is anopen-source package.

The easiest way to get started is to try theBigFrames quickstartin anotebook in BigQuery Studio.

To use BigFrames in your local development environment,

1. Runpip install --upgrade bigframes to install the latest version.
2. SetupApplication default credentialsfor your local development environment enviroment.
3. Create aGCP project with the BigQuery API enabled.
4. Use thebigframes package to query data.

importbigframes.pandasasbpdbpd.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())

To learn more about BigQuery DataFrames, visit these pages

• Introduction to BigQuery DataFrames (BigFrames)
• Sample notebooks
• API reference
• Source code (GitHub)

BigQuery DataFrames is distributed with theApache-2.0 license.

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

• Ibis
• pandas
• Python
• scikit-learn
• XGBoost

For details, see thethird_partydirectory.

For further help and provide feedback, you can email us atbigframes-feedback@google.com.