pandas-gbq
Google BigQuery connector for pandas
Decision gist · record as of 2026-08-14
Yes. pandas-gbq is actively maintained, has low install friction, carries no known vulnerabilities, and is widely used (top 1000 on PyPI). Install it if you work with BigQuery and want to read or write data from pandas without managing raw SQL clients or authentication separately. The permissive BSD license poses no barrier.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a Google Cloud project and valid authentication credentials (typically via gcloud CLI or OAuth flow).
- Low friction installation with a pure-Python wheel.
- Active maintenance—last release 8 days ago—and 5371 repository stars suggest stable, well-used code.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive, allowing use in commercial and proprietary projects with minimal restrictions beyond attribution and liability disclaimers.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 5,371 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 34,473,624 downloads/mo, #758 on PyPI
Alternatives
Verify before relying
pip install pandas-gbq
import pandas_gbq
result_dataframe = pandas_gbq.read_gbq(
"SELECT column FROM dataset.table WHERE value = 'something'"
)
pandas_gbq.to_gbq(result_dataframe, "dataset.table")- Whether authentication to Google Cloud is required upfront or handled transparently by the library.
- Performance characteristics for large result sets or high-frequency queries.
- Whether pandas-gbq handles schema inference or requires explicit type specification.
What it is and what it does
pandas-gbq is a connector library that integrates Google BigQuery with pandas, allowing you to query BigQuery tables directly into DataFrames and persist DataFrames back to BigQuery. It wraps the google-cloud-bigquery client and handles the conversion between SQL result sets and pandas objects, so you can work with BigQuery data using familiar pandas operations.
The library is built on top of google-cloud-bigquery, pydata-google-auth, and related Google Cloud libraries, meaning it inherits their authentication and API patterns. It's actively maintained and supports modern Python versions (3.10–3.14), making it suitable for data science and analytics workflows that need to read from or write to BigQuery without leaving the pandas ecosystem.
Use it for
- Query BigQuery public datasets or your own tables and load results directly into a pandas DataFrame for exploratory analysis.
- Export a pandas DataFrame to a BigQuery table for persistence, sharing, or further processing in BigQuery.
- Build ETL pipelines that read from BigQuery, transform data in pandas, and write results back to BigQuery.
- Integrate BigQuery into Jupyter notebooks or data science scripts without switching between SQL clients and Python.
- Prototype analytics on large datasets by querying a sample into pandas, then scaling the query in BigQuery.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
pandas-gbq is actively maintained, has low install friction, carries no known vulnerabilities, and is widely used (top 1000 on PyPI). Install it if you work with BigQuery and want to read or write data from pandas without managing raw SQL clients or authentication separately. The permissive BSD license poses no barrier.
Install
pandas-gbq on PyPI
Before you install
Low friction installation with a pure-Python wheel. Active maintenance—last release 8 days ago—and 5371 repository stars suggest stable, well-used code. Requires Python 3.10 or later and depends on 12 runtime packages including google-cloud-bigquery and pydata-google-auth.
Requires a Google Cloud project and valid authentication credentials (typically via gcloud CLI or OAuth flow).
License in practice
BSD-3-Clause is permissive, allowing use in commercial and proprietary projects with minimal restrictions beyond attribution and liability disclaimers.
Quickstart
pip install pandas-gbq
import pandas_gbq
result_dataframe = pandas_gbq.read_gbq(
"SELECT column FROM dataset.table WHERE value = 'something'"
)
pandas_gbq.to_gbq(result_dataframe, "dataset.table")Verify before relying
- Whether authentication to Google Cloud is required upfront or handled transparently by the library.
- Performance characteristics for large result sets or high-frequency queries.
- Whether pandas-gbq handles schema inference or requires explicit type specification.
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 packagessetuptoolsdb-dtypesnumpypandaspyarrowpydata-google-authpsutilgoogle-api-coregoogle-authgoogle-auth-oauthlibgoogle-cloud-bigquerypackaging |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 34,473,624 / month, #758 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | 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 |
Evidence: pandas_gbq-0.35.1-py3-none-any.whl
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See also bigframes · bigquery · google-cloud-bigquery · google-cloud-bigquery-storage · pandasql · bigquery-magics · cloudsearch · db-dtypes · gspread-dataframe · salesforce-cdp-connector