BigQuery DataFrames -- scalable analytics and machine learning with BigQuery
Project description
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.
Getting started with BigQuery DataFrames
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,
Run pip install --upgrade bigframes to install the latest version.
Setup Application default credentials for your local development environment enviroment.
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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