bigquery-magics
Google BigQuery magics for Jupyter and IPython
Decision gist · record as of 2026-08-14
Yes, if you work with BigQuery in Jupyter notebooks. Low install friction, active maintenance, no known vulnerabilities, and permissive licensing make it a safe choice. The package solves a real friction point—writing SQL queries in notebooks without boilerplate—and integrates cleanly with the IPython ecosystem. Prerequisite: you must already have a Google Cloud project with BigQuery API enabled and authentication set up.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.10; Google Cloud project with BigQuery API enabled and authentication configured (service account or user credentials).
- Low install friction; pure Python wheel with no compiled dependencies.
- Actively maintained with a recent release and 5373 repository stars.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows use in commercial and open-source projects with minimal restrictions.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 5,373 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 226,221 downloads/mo, #9,204 on PyPI
Alternatives
Verify before relying
pip install bigquery-magics
In a Jupyter notebook:
%load_ext bigquery_magics
%%bigquery
SELECT name, SUM(number) as count
FROM 'bigquery-public-data.usa_names.usa_1910_current'
GROUP BY name ORDER BY count DESC LIMIT 3- Whether the package handles authentication transparently via Application Default Credentials or requires manual setup in all cases.
- Performance characteristics when querying very large result sets or long-running queries.
What it is and what it does
bigquery-magics extends IPython and Jupyter with magic commands that let you write and execute BigQuery SQL queries directly in notebook cells. Instead of writing Python code to instantiate a client and fetch results, you use `%%bigquery` to run SQL and get back a pandas DataFrame automatically. The package wraps google-cloud-bigquery and handles the connection, authentication, and result formatting for you.
It's designed for data analysts and engineers working in Jupyter environments who want to explore BigQuery datasets interactively without leaving the notebook. The magic commands integrate with the notebook's display system to show query results inline, and they depend on ipywidgets, ipython, and pandas to provide a seamless interactive experience. You must have a Google Cloud project with BigQuery enabled and valid credentials configured before use.
Use it for
- Exploratory data analysis on BigQuery tables directly from a Jupyter notebook without writing boilerplate client code.
- Teaching or documenting BigQuery SQL workflows in notebooks where SQL queries are the primary focus.
- Building data science workflows where analysts switch between SQL queries and pandas manipulation in the same notebook.
- Prototyping analytics queries interactively before deploying them to production pipelines.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with BigQuery in Jupyter notebooks.
Low install friction, active maintenance, no known vulnerabilities, and permissive licensing make it a safe choice. The package solves a real friction point—writing SQL queries in notebooks without boilerplate—and integrates cleanly with the IPython ecosystem. Prerequisite: you must already have a Google Cloud project with BigQuery API enabled and authentication set up.
Install
bigquery-magics on PyPI
Before you install
Low install friction; pure Python wheel with no compiled dependencies. Actively maintained with a recent release and 5373 repository stars. Supports current Python versions (3.10–3.14).
Requires Python >= 3.10; Google Cloud project with BigQuery API enabled and authentication configured (service account or user credentials).
License in practice
Apache 2.0 permissive license allows use in commercial and open-source projects with minimal restrictions.
Quickstart
pip install bigquery-magics
In a Jupyter notebook:
%load_ext bigquery_magics
%%bigquery
SELECT name, SUM(number) as count
FROM 'bigquery-public-data.usa_names.usa_1910_current'
GROUP BY name ORDER BY count DESC LIMIT 3Verify before relying
- Whether the package handles authentication transparently via Application Default Credentials or requires manual setup in all cases.
- Performance characteristics when querying very large result sets or long-running queries.
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packagesdb-dtypesgoogle-cloud-bigqueryipywidgetsipythonipykernelpackagingpandaspyarrowpydata-google-authtqdmpyopenssl |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 226,221 / month, #9,204 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 :: DevelopersLicense :: OSI Approved :: Apache Software 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 :: Internet |
Evidence: bigquery_magics-0.15.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “bigquery jupyter magic commands”
- bigquery-magicsProvides IPython magic commands to query Google BigQuery directly…
- jupysqlAdds SQL execution to Jupyter and IPython notebooks via `%sql` and…
- ipythonIPython is an enhanced interactive Python shell and Jupyter kernel…
Give your agent the search over MCP, or paste the wish link into any chat.
More Internet packages
Botocore provides low-level, data-driven access to Amazon Web Services APIs, serving as the foundation for the AWS CLI and boto3 libraries.
Install it if you need programmatic access to AWS services.
Provides an async client for AWS services using botocore and aiohttp, allowing you to call AWS APIs asynchronously within asyncio-based applications.
Install it if you need to call AWS services from async Python code; it is the standard way to do so.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides a platform-independent file locking mechanism to coordinate access to files across processes and threads.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides common Protocol Buffer message definitions used across Google Cloud APIs, enabling Python clients to interact with Google services.
See also emr-notebooks-magics · google-cloud-bigquery · bigquery-schema-generator · jupysql · pandas-gbq · pybigquery · sqlalchemy-bigquery · google-cloud-bigquery-storage · google-cloud-bigquery-biglake · google-cloud-bigquery-connection