---
id: fastf1
version: "3.8.3"
license: MIT License Copyright (c) 2026 Philipp Schäfer Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# fastf1 — Python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry.

License: permissive · Maintenance: active · Downloads: 15.0M/mo

## 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 above — verify before relying.

FastF1 provides Python access to Formula 1 timing data, telemetry, results, and schedules through an Ergast-compatible API, returning data as extended Pandas DataFrames with F1-specific analysis functions and Matplotlib visualization integration.

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

pip install fastf1
uv add fastf1
poetry add fastf1

## Installing fastf1

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).

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.results

Requires Python 3.10 or later. Depends on external F1 data APIs; network access needed for data retrieval.

Verify 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: MIT License Copyright (c) 2026 Philipp Schäfer Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 15.0M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

formula 1 data analysis python, f1 telemetry timing data, formula one results api, f1 schedule and results, motorsport data analysis, f1 lap timing analysis, formula 1 historical data, motorsport-data, telemetry-analysis, sports-analytics

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