BigQuery DataFrames (BigFrames)
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.bigquery.ai provides powerful AI functions, text generation, vector embeddings, and semantic search powered by Gemini.
bigframes.ml is a scikit-learn-like API for ML.
BigQuery DataFrames is an open-source package.
Why Use BigQuery DataFrames?
BigQuery DataFrames eliminates the “data movement bottleneck.” Instead of downloading large datasets to a local environment, BigFrames translates your Python code into optimized SQL executed across BigQuery’s distributed compute engine.
Petabyte-Scale Scalability: Effortlessly process datasets exceeding local memory constraints without provisioning or managing Spark or Ray clusters.
Familiar Python Ecosystem: Use the pandas operations you already know (read_gbq, groupby, merge, pivot_table) directly on BigQuery tables.
Generative AI & Multimodality: Seamlessly leverage Gemini models, AI functions, multimodal processing (audio, video, PDF), vector embeddings, and vector search with bigframes.bigquery.ai.
Optimized Execution: Take advantage of BigFrames partial ordering mode (bpd.options.bigquery.ordering_mode = "partial") for considerable performance gains on analytical workloads.
Enterprise Security: Keep your data securely governed within the BigQuery security perimeter.
Hybrid Flexibility: Seamlessly transition between distributed BigQuery processing and local pandas analysis with to_pandas() and read_pandas().
Getting started with BigQuery DataFrames
Notebooks
The easiest way to get started is to open a sample notebook in Colab or BigQuery Studio. More details can be found in the BigFrames quickstart and in the introduction to notebooks.
Local environment
To use BigFrames in your local development environment:
Run pip install --upgrade bigframes to install the latest version.
Set up Application Default Credentials for your local development environment.
Create a Google Cloud project with the BigQuery API enabled.
Use the bigframes package to query data.
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
License
BigQuery DataFrames is distributed with the Apache-2.0 license.
It also contains code derived from the following third-party packages:
For details, see the third_party directory.
Contact Us
For further help and provide feedback, you can email us at bigframes-feedback@google.com.
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Release files for bigframes 2.50.0
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| File | Interpreter | ABI | Platform | Reset |
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| bigframes-2.50.0-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details |
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