Main Features Copy Chapter

• Access to F1 timing data, telemetry, sessions results and more
• Full support for the Ergast compatible jolpica-f1 API to access current and
  historical F1 data
• All data is provided in the form of extended Pandas DataFrames to make
  working with the data easy while having powerful tools available
• Adds custom functions to the Pandas objects specifically to make working
  with F1 data quick and simple
• Integration with Matplotlib to facilitate data visualization
• Implements caching for all API requests to speed up your scripts

Installation Copy Chapter

It is recommended to install FastF1 using pip:

pip install fastf1

Alternatively, a wheel or a source distribution can be downloaded from Pypi.

You can also install using conda:

conda install -c conda-forge fastf1

Installation in Pyodide, JupyterLite and other WASM-based environments

FastF1 should be mostly compatible with Pyodide and other WASM-based
environments, although this is not extensively tested. Currently, the
installation and usage require some additional steps. You can find more
information and a guide in
this external repository
and the discussion in this issue.

Third-party packages Copy Section

• R package that wraps FastF1: https://cran.r-project.org/package=f1dataR

Third-party packages are not directly related to the FastF1 project. Questions
and suggestions regarding these packages need to be directed at their
respective maintainers.

Documentation Copy Chapter

The official documentation can be found here:
docs.fastf1.dev

Supporting the Project Copy Chapter

If you want to support the continuous development of FastF1, you can sponsor me
on GitHub or buy me a coffee.

https://github.com/sponsors/theOehrly

Notice Copy Chapter

FastF1 and this website are unofficial and are not associated in any way with
the Formula 1 companies. F1, FORMULA ONE, FORMULA 1, FIA FORMULA ONE WORLD
CHAMPIONSHIP, GRAND PRIX and related marks are trade marks of Formula One
Licensing B.V.