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 is a pandas API for analytics. Many workloads can be migrated from pandas to bigframes by just changing a few imports.
• bigframes.ml is a scikit-learn-like API for ML.
• bigframes.bigquery.ai are a collection of powerful AI methods, powered by Gemini.

BigQuery DataFrames is an open-source package.

The easiest way to get started is to try the BigFrames quickstart in a notebook in BigQuery Studio.

To use BigFrames in your local development environment,

1. Run pip install --upgrade bigframes to install the latest version.
2. Setup Application default credentials for your local development environment enviroment.
3. Create a GCP project with the BigQuery API enabled.
4. Use the bigframes 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 the Apache-2.0 license.

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

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

For details, see the third_party directory.

For further help and provide feedback, you can email us at [email protected].