---
id: pandas-gbq
version: "0.35.1"
license: BSD-3-Clause
license_treatment: permissive
maintenance: active
---

# pandas-gbq — Google BigQuery connector for pandas

License: permissive · Maintenance: active · Popularity: top 1,000 on PyPI

## Install

pip install pandas-gbq
uv add pandas-gbq
poetry add pandas-gbq

## Description

pandas-gbq
==========

|preview| |pypi| |versions|

**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...

## AI interpretation — verify before relying

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.

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.

[View on SkillFed](https://skillfed.io/packages/pandas-gbq) · [View on PyPI](https://pypi.org/project/pandas-gbq/)