fastf1 2.1.10

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

Note

Update required

An update to the latest version of FastF1 (v2.1.10) is required to adapt to changes in an external API. Without this update you will only be able to load previously cached data! If you use FastF1 version 2.1.9 or older you need to update to the latest version.

Use pip install --upgrade fastf1 to update to the latest version.

Fast F1

A python package for accessing F1 historical timing data and telemetry.

Installation

It is recommended to install FastF1 using pip:

pip install fastf1

Note that Python 3.8 is required.

Alternatively, a wheel or a source distribution can be downloaded from the Github releases page or from Pypi.

Getting Started: Documentation and Examples

The documentation can be found here. It provides in depth information about the functionality that is available in FastF1 as well as some examples.

There are also 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 from the Bahrain GP (weekend number 2) of 2019.

import fastf1 as ff1
from fastf1 import plotting
from matplotlib import pyplot as plt

plotting.setup_mpl()

ff1.Cache.enable_cache('path/to/folder/for/cache')  # optional but recommended

race = ff1.get_session(2020, 'Turkish Grand Prix', 'R')
laps = race.load_laps()

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

For more information, check the documentation here.

Compatibility

Timing data, car telemetry and position data is available for the 2018 to 2021 seasons. Very basic weekend information is available for older seasons (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.

Roadmap

This is a rather loose roadmap with no fixed timeline whatsoever.

• Improvements to the current plotting functionality

• Some default plots to easily allow creating nice visualizations and interesting comparisons

• General improvements and smaller additions to the current core functionality

• Support for F1’s own data api to get information about events, sessions, drivers and venues

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.