Fast F1
A python package for accessing F1 historical timing data and telemetry.
General Information
FastF1 v2.1 is now available for installation through pip. The old way of installing via pip + git directly from the master branch is no longer recommended.
It is no longer possible to download telemetry and car position data after a session!
See this Twitter post for some information.
This means:
It is still possible to load timing data, tire data, track status data and session status data.
It is not possible to load car telemetry data (includes position data). You need to record live timing data during a session for this!
Live timing data
A live timing client has been added with the v2.1 release. The client can be used to save the live timing telemetry data stream that is available during sessions.
The live timing client does not parse data in real time! Data can only be parsed and used after a session has completed. This is a limitation of FastF1’s api parser. For various reasons there is no intention of changing this.
For usage see the documentation.
Changes
If you have used previous versions of FastF1, please read the changelog in the documentation.
V2.1 introduces some new features and some breaking changes. The documentation is improved in general. Also, there is a new section discussing how to get the most accurate results from the data that is available. It may be worth reading if you want to make more complicated analyses and visualizations.
Other
Please report bugs if (when) you find them. Feel free to report complaints about unclear documentation too.
Interested in contributing? There’s some info at the end of this document…
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.
Usage
Full documentation can be found here.
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()
Compatibility
Timing data is available for the 2018, 2019 and 2020 season. Very basic weekend information is available for older seasons (limited to Ergast web api). Car telemetry data is only available as a live stream during a session. This means that you need to record this data yourself, using the provided client, if you want to have access to it.
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.
1 maintainer
Metadata
1 maintainer
Release files for fastf1 2.1.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| fastf1-2.1.5.tar.gz | 57.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| fastf1-2.1.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 117.4 kB
Release files / fastf1-2.1.5.tar.gz
| Download URL | fastf1-2.1.5.tar.gz |
|---|---|
| Size | 57.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / fastf1-2.1.5-py3-none-any.whl
| Download URL | fastf1-2.1.5-py3-none-any.whl |
|---|---|
| Size | 59.5 kB |
| Tags | Python 3 |
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