> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/theOehrly/Fast-F1/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> FastF1 is a Python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry

FastF1 is a Python package that provides comprehensive access to Formula 1 timing data, telemetry, session results, and historical race information. Built on top of Pandas, it offers powerful data analysis capabilities with an intuitive API designed specifically for F1 data.

## Key Features

<CardGroup cols={2}>
  <Card title="Timing & Telemetry Data" icon="clock">
    Access detailed lap times, sector times, and high-frequency telemetry data including speed, throttle, brake, and GPS position
  </Card>

  <Card title="Session Results" icon="flag-checkered">
    Retrieve complete session data for practice, qualifying, sprint, and race sessions from 2018 onwards
  </Card>

  <Card title="Historical F1 Data" icon="database">
    Query historical F1 data through the Ergast-compatible jolpica-f1 API for seasons dating back to 1950
  </Card>

  <Card title="Pandas Integration" icon="table">
    All data returns as extended Pandas DataFrames with custom methods for F1-specific operations
  </Card>
</CardGroup>

## Data Sources

FastF1 aggregates data from multiple sources:

* **F1 Live Timing**: Real-time and historical timing data from Formula 1's official timing system (2018-present)
* **Ergast API**: Comprehensive historical F1 data via the jolpica-f1 API
* **MultiViewer API**: Additional telemetry and session metadata

## Use Cases

FastF1 is ideal for:

* **Race Analysis**: Compare driver performance, analyze strategies, and identify performance trends
* **Data Visualization**: Create compelling charts and graphics with built-in Matplotlib integration
* **Statistical Research**: Conduct quantitative analysis of F1 performance data
* **Machine Learning**: Build predictive models using historical telemetry and results

## Performance & Caching

<Note>
  FastF1 implements a two-stage caching system that significantly speeds up data loading and prevents API rate limit issues. Enabling cache is **strongly recommended** for all use cases.
</Note>

The cache system stores:

* **Stage 1**: Raw HTTP requests in a SQLite database
* **Stage 2**: Parsed Python objects as pickle files

Cached requests don't count towards API rate limits, making your scripts faster and more reliable.

## Python Version Support

FastF1 requires **Python 3.10 or higher** and is tested on:

* Python 3.10
* Python 3.11
* Python 3.12
* Python 3.13
* Python 3.14

## Next Steps

<CardGroup cols={2}>
  <Card title="Installation" icon="download" href="/installation">
    Install FastF1 via pip or conda
  </Card>

  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Get started with a complete example
  </Card>
</CardGroup>

## Legal Notice

<Warning>
  FastF1 is an unofficial package and is not associated with Formula 1 companies. F1, FORMULA ONE, FORMULA 1, FIA FORMULA ONE WORLD CHAMPIONSHIP, GRAND PRIX and related marks are trademarks of Formula One Licensing B.V.
</Warning>