fastf1
Python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry.
Decision gist · record as of 2026-08-14
Yes. FastF1 is production-stable (active since 2021-03-10, 5303 stars), has low install friction, no known vulnerabilities, and a permissive MIT license. It fills a clear niche for F1 data analysis in Python. Install it if you work with Formula 1 data; skip it if you have no need for F1-specific APIs or telemetry.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Depends on external F1 data APIs; network access needed for data retrieval.
- Low friction install via pip with a pure-Python wheel.
License · maintenance · safety
permissive license (permissive) — MIT License permits unrestricted use, modification, and redistribution in both open-source and commercial projects, with no warranty or liability.
last release 2026-04-29 (107 days) · last repo commit 2026-08-13 · 5,303 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 14,954,097 downloads/mo, #1,208 on PyPI
Alternatives
Verify before relying
pip install fastf1
import fastf1
session = fastf1.get_session(2021, 1, 'R')
session.load()
results = session.results- Whether WASM compatibility (Pyodide/JupyterLite) is production-ready or still experimental.
- Specific API rate limits or throttling behavior when making repeated requests.
- Whether caching is automatic or requires explicit configuration.
- Concrete examples of F1-specific custom functions added to Pandas objects.
What it is and what it does
FastF1 is a Python library that retrieves and structures Formula 1 data—timing, telemetry, results, and schedules—from the Ergast-compatible API into extended Pandas DataFrames. It adds F1-specific analysis methods to those DataFrames and integrates with Matplotlib for visualization, while implementing request caching to reduce API calls.
The package is designed for data analysts and developers working with F1 statistics and performance metrics. It abstracts away API details and data wrangling, letting you load a session or race weekend and immediately work with lap times, driver telemetry, and results as familiar tabular data. Its main dependencies are pandas, numpy, matplotlib, requests, and supporting libraries for data handling and web communication.
Use it for
- Analyze lap times and tire strategies across a race weekend to compare driver performance.
- Extract and visualize telemetry (speed, throttle, brake) for a specific driver or lap.
- Build historical F1 statistics dashboards combining results, schedules, and season-long trends.
- Compare qualifying and race pace across multiple seasons or drivers.
- Prototype machine-learning models using F1 telemetry and performance data as features.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
FastF1 is production-stable (active since 2021-03-10, 5303 stars), has low install friction, no known vulnerabilities, and a permissive MIT license. It fills a clear niche for F1 data analysis in Python. Install it if you work with Formula 1 data; skip it if you have no need for F1-specific APIs or telemetry.
Install
fastf1 on PyPI
Before you install
Low friction install via pip with a pure-Python wheel. Active maintenance with recent commits and a large community (5303 stars). Supports current Python versions (3.10–3.14) and depends on well-established libraries (pandas, numpy, matplotlib, requests).
Requires Python 3.10 or later. Depends on external F1 data APIs; network access needed for data retrieval.
License in practice
MIT License permits unrestricted use, modification, and redistribution in both open-source and commercial projects, with no warranty or liability.
Quickstart
pip install fastf1
import fastf1
session = fastf1.get_session(2021, 1, 'R')
session.load()
results = session.resultsVerify before relying
- Whether WASM compatibility (Pyodide/JupyterLite) is production-ready or still experimental.
- Specific API rate limits or throttling behavior when making repeated requests.
- Whether caching is automatic or requires explicit configuration.
- Concrete examples of F1-specific custom functions added to Pandas objects.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 15 packagescryptographymatplotlibnumpypandasplatformdirspydanticpyjwtpython-dateutilrapidfuzzrequests-cacherequestsscipysignalrcoretimplewebsockets |
| Maintenance | Actively maintained 107 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 14,954,097 / month, #1,208 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: fastf1-3.8.3-py3-none-any.whl
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