fastf1 2.2.3

Wrapper library for F1 data and telemetry API with additional data processing capabilities.

FastF1 version 2.2.0 has been release. A few breaking changes were required. Please read the changelog.

Enjoy the 2022 to season!

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

Installation

It is recommended to install FastF1 using pip:

pip install fastf1

Note that Python 3.8 or higher is required. (The live timing client does not support Python 3.10, therefore full functionality is only available with Python 3.8 and 3.9)

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

Getting Started: Documentation and Examples

• Documentation.

• Examples.

• Changelog.

Furthermore, there are some great articles and examples written by other people. They provide a nice overview about what you can do with FastF1 and might help you to get started.

• Accessing Formula-1 Race’s historical data using Python (medium.com)

• Formula 1 Data Analysis Tutorial - 2021 Russian GP: “To Box, or Not to Box?” (medium.com)

• How I Analyze Formula 1 Data With Python: 2021 Italian GP (medium.com)

General Information

Usage

Creating a simple analysis is not very difficult, especially if you are already familiar with pandas and numpy.

Suppose that we want to analyse the race pace of Leclerc compared to Hamilton for the Turkish GP 2020.

import fastf1
from fastf1 import plotting
from matplotlib import pyplot as plt

plotting.setup_mpl()

fastf1.Cache.enable_cache('path/to/folder/for/cache')  # optional but recommended

race = fastf1.get_session(2020, 'Turkish Grand Prix', 'R')
race.load()

lec = race.laps.pick_driver('LEC')
ham = race.laps.pick_driver('HAM')

Once the session is loaded, and drivers are selected, you can plot the information.

fastf1.plotting provides some special axis formatting and data type conversion. This is required for generating a correct plot.

It is not necessary to enable the usage of a cache but it is recommended. Simply provide the path to some empty folder on your system.

fig, ax = plt.subplots()
ax.plot(lec['LapNumber'], lec['LapTime'], color='red')
ax.plot(ham['LapNumber'], ham['LapTime'], color='cyan')
ax.set_title("LEC vs HAM")
ax.set_xlabel("Lap Number")
ax.set_ylabel("Lap Time")
plt.show()

Compatibility

Timing data, car telemetry and position data is available for the 2018 and later seasons. Schedule information and session results are available for older seasons too. (limited to Ergast web api).

Data Sources

FastF1 uses data from F1’s live timing service.

Data can be downloaded after a session. Alternatively, the actual live timing data can be recorded and the recording can be used as a data source.

Usually it is not necessary to record the live timing data. But there have been server issues in the past which resulted in the data being unavailable for download. Therefore, you only need to record live timing data if you want to benefit from the extra redundancy.

Bugs and Issues

Please report bugs if (when) you find them. Feel free to report complaints about unclear documentation too.

Contributing

Contributions are welcome of course. If you are interested in contributing, open an issue for the proposed feature or issue you would like to work on. This way we can coordinate so that no unnecessary work is done.

Working directly on the core and api code will require some time to understand. Creating nice default plots on the other hand does not required as deep of an understanding of the code and is therefore easier to accomplish. Pick whatever you like to do.

Also, the documentation needs an examples section. You can provide some snippets of your code as examples for others, to help them get started easier.

Notice

FastF1 is unofficial software and in no way associated with the Formula 1 group of companies.