pandas-gbq

Google BigQuery connector for pandas

pandas-gbq Permissive license BSD-3-Clause Active ★ 5,370 v0.35.1 released 2026-08-06

Install

pandas-gbq on PyPI

pip

pip install pandas-gbq

uv

uv add pandas-gbq

poetry

poetry add pandas-gbq

Package facts

License	BSD-3-Clause (permissive)
Python support	supports the current Python release (>=3.10)
Install friction	low — pure-Python wheel
Runtime dependencies	12 — setuptools, db-dtypes, numpy, pandas, pyarrow, pydata-google-auth, psutil, google-api-core, google-auth, google-auth-oauthlib, google-cloud-bigquery, packaging
Maintenance	actively maintained — 7 days since the last release
Last repo commit	2026-08-13
First released	2017-02-22
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: pandas_gbq-0.35.1-py3-none-any.whl

Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: InternetTopic :: Scientific/Engineering

About pandas-gbq

from the package's own PyPI description — quoted content, verbatim

pandas-gbq

pandas-gbq is a package providing an interface to the Google BigQuery API from pandas.

Library Documentation_
Product Documentation_

.. |preview| image:: https://img.shields.io/badge/support-preview-orange.svg :target: https://github.com/googleapis/google-cloud-python/blob/main/README.rst#beta-support .. |pypi| image:: https://img.shields.io/pypi/v/pandas-gbq.svg :target: https://pypi.org/project/pandas-gbq/ .. |versions| image:: https://img.shields.io/pypi/pyversions/pandas-gbq.svg :target: https://pypi.org/project/pandas-gbq/ .. _Library Documentation: https://googleapis.dev/python/pandas-gbq/latest/ .. _Product Documentation: https://cloud.google.com/bigquery/docs/reference/v2/

Installation

Install latest release version via pip ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: shell

$ pip install pandas-gbq

Install latest development version ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: shell

$ pip install git+https://github.com/googleapis/google-cloud-python.git

Usage

Perform a query ~~~~~~~~~~~~~~~

.. code:: python

import pandas_gbq

# If...

Read as markdown · JSON record · Source repository · Homepage

AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

pandas-gbq provides a pandas interface to Google BigQuery, enabling you to read query results directly into DataFrames and write DataFrames back to BigQuery tables using simple function calls.

Installation is low-friction with a pure-Python wheel distribution. The package is actively maintained with a release 7 days ago and supports Python 3.10–3.14. However, it carries 12 runtime dependencies including google-cloud-bigquery, google-auth-oauthlib, and pyarrow, which may introduce transitive dependency complexity in constrained environments.

pandas-gbq is licensed under BSD-3-Clause, a permissive open-source license that allows commercial use, modification, and redistribution with minimal restrictions, provided the license and copyright notice are retained.

Usage

import pandas_gbq

# Read from BigQuery
result_dataframe = pandas_gbq.read_gbq("SELECT * FROM dataset.table LIMIT 10", project_id="your-project-id")

# Write to BigQuery
pandas_gbq.to_gbq(result_dataframe, "dataset.table")

Requires a Google Cloud project ID and valid authentication credentials (typically via gcloud CLI or environment variables); BigQuery API access must be enabled in your GCP project.

Verdict: pandas-gbq is a well-maintained, actively developed connector in the top 1000 PyPI packages with no known vulnerabilities. Its permissive BSD-3-Clause license and low installation friction make it suitable for data workflows integrating pandas with BigQuery. The primary consideration is the substantial dependency footprint (12 runtime dependencies) and the requirement for GCP authentication and project setup.

Needs verification

Whether the 12 runtime dependencies are all strictly necessary or if some are optional/conditional
Performance characteristics and query size limits when reading large datasets into pandas
Whether authentication can be handled transparently in CI/CD or serverless environments

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