LiveF1 - An Open-Source Formula 1 Data Processing Toolkit

LiveF1 is a powerful Python toolkit for accessing and analyzing Formula 1 data in real-time or from historical archives. It's designed for developers, analysts, and F1 fans building applications around Formula 1 insights.

Features

• Real-Time Race Data: Provides live telemetry, timing, and position updates, ideal for powering dashboards and live analytics.
• Historical Data Access: Includes comprehensive race data from past seasons, perfect for performance analysis and comparisons.
• Data Processing Modules: Built-in ETL tools make raw data immediately usable, supporting analysis and seamless data storage.
• Easy Integration: Simple API for both real-time and historical data

In a nutshell:

Using LiveF1, you can access real-time and historical racing data, making it easy to feed analytics and visualizations.

Data sources

LiveF1 combines two backends:

• Formula 1 Livetiming — Official timing static JSON and feeds for session discovery and archived high-frequency topics (telemetry, timing, position, weather, etc.).
• Jolpica F1 API — Community Ergast-style JSON REST API for seasons, calendars, drivers, constructors, results, standings, and related structured data.

Livetiming alone does not cover every object shape users need; Jolpica fills and enriches the season calendar when Livetiming is incomplete and supplies championship-oriented data. The HTTP client for Jolpica is BaseF1 (source), a separate library that LiveF1 depends on.

Installation

Install using pip:

pip install livef1

To build tables with Polars instead of pandas, install the extra:

pip install "livef1[polars]"

Authentication

Live timing data requires an F1 account with an active F1TV subscription. Put your credentials in a .env file (or export them as environment variables):

F1_USERNAME='you@example.com'
F1_PASSWORD='your-password'

LiveF1 uses the first .env found in the working directory or one of its parents, so notebooks in subfolders use your project's .env. If the credentials are missing, LiveF1 asks for them and saves them to .env. The token is saved to ~/.livef1/f1tv_auth_token.json and reused until it expires.

import livef1

livef1.login()                      # reuse the saved token, or sign in
livef1.login(force=True)            # always sign in again
livef1.login(save_token=False)      # keep the token in memory only
livef1.login(token_path="/path/to/token.json")  # also remembered in .env as LIVEF1_TOKEN_PATH
livef1.logout()                     # delete the saved token

Calling login() is optional: LiveF1 signs in automatically when live timing rejects a request.

Quick Start

Timing, telemetry, and the live stream need an F1 account with an active F1TV subscription. Set that up in Authentication above. Calendar and championship data does not need one.

Historical Data

Access data from past races:

import livef1

# Get a specific race session
session = livef1.get_session(
    season=2024,
    meeting_identifier="Spa",
    session_identifier="Race"
)

# Load position data
position_data = session.get_data(
    dataNames="Position.z"
)

print(position_data.head())

  |    |   SessionKey | timestamp    | Utc                          |   DriverNo | Status   |   X |   Y |   Z |
  |---:|-------------:|:-------------|:-----------------------------|-----------:|:---------|----:|----:|----:|
  |  0 |         9574 | 00:01:45.570 | 2024-07-28T12:10:22.7877313Z |          1 | OnTrack  |   0 |   0 |   0 |
  |  1 |         9574 | 00:01:45.570 | 2024-07-28T12:10:22.7877313Z |          2 | OnTrack  |   0 |   0 |   0 |
  |  2 |         9574 | 00:01:45.570 | 2024-07-28T12:10:22.7877313Z |          3 | OnTrack  |   0 |   0 |   0 |
  |  3 |         9574 | 00:01:45.570 | 2024-07-28T12:10:22.7877313Z |          4 | OnTrack  |   0 |   0 |   0 |
  |  4 |         9574 | 00:01:45.570 | 2024-07-28T12:10:22.7877313Z |         10 | OnTrack  |   0 |   0 |   0 |

Data Processing

LiveF1 uses a medallion architecture to process F1 data into analysis-ready formats:

# Generate processed data tables
session.generate(silver=True)

# Access refined data
laps_data = session.get_laps()
telemetry_data = session.get_car_telemetry()

print(laps_data.head())

    |    |   lap_number | lap_time               | in_pit                 | pit_out   | sector1_time           | sector2_time           | sector3_time           | None   |   speed_I1 |   speed_I2 |   speed_FL |   speed_ST |   no_pits | lap_start_time         |   DriverNo | lap_start_date             |
    |---:|-------------:|:-----------------------|:-----------------------|:----------|:-----------------------|:-----------------------|:-----------------------|:-------|-----------:|-----------:|-----------:|-----------:|----------:|:-----------------------|-----------:|:---------------------------|
    |  0 |            1 | NaT                    | 0 days 00:17:07.661000 | NaT       | NaT                    | 0 days 00:00:48.663000 | 0 days 00:00:29.571000 |        |        314 |        204 |            |        303 |         0 | NaT                    |         16 | 2024-07-28 13:03:52.742000 |
    |  1 |            2 | 0 days 00:01:50.240000 | NaT                    | NaT       | 0 days 00:00:31.831000 | 0 days 00:00:48.675000 | 0 days 00:00:29.734000 |        |        303 |        203 |        219 |            |         0 | 0 days 00:57:07.067000 |         16 | 2024-07-28 13:05:45.045000 |
    |  2 |            3 | 0 days 00:01:50.519000 | NaT                    | NaT       | 0 days 00:00:31.833000 | 0 days 00:00:49.132000 | 0 days 00:00:29.554000 |        |        311 |        202 |        215 |        304 |         0 | 0 days 00:58:57.307000 |         16 | 2024-07-28 13:07:35.285000 |
    |  3 |            4 | 0 days 00:01:49.796000 | NaT                    | NaT       | 0 days 00:00:31.592000 | 0 days 00:00:48.778000 | 0 days 00:00:29.426000 |        |        312 |        201 |        217 |        309 |         0 | 0 days 01:00:47.870000 |         16 | 2024-07-28 13:09:25.848000 |
    |  4 |            5 | 0 days 00:01:49.494000 | NaT                    | NaT       | 0 days 00:00:31.394000 | 0 days 00:00:48.729000 | 0 days 00:00:29.371000 |        |        313 |        197 |        217 |        311 |         0 | 0 days 01:02:37.721000 |         16 | 2024-07-28 13:11:15.699000 |

Tables are pandas DataFrames by default. To build every table (Bronze, Silver and Gold) with Polars, configure it before loading data:

livef1.configure(dataframe_backend="polars")

session = livef1.get_session(2024, "Belgian", "Race")
session.generate(silver=True)
session.laps  # polars.DataFrame

Real-Time Data

Stream live race data:

from livef1.adapters import RealF1Client

# Initialize client with topics to subscribe
client = RealF1Client(
    topics=["CarData.z", "Position.z"],
    log_file_name="race_data.json"  # Optional: log data to file
)

# Define callback for incoming data
@client.callback("telemetry_handler")
async def handle_data(records):
    for record in records:
        print(record)  # Process incoming data

# Start receiving data
client.run()

Documentation

For detailed documentation, examples, and API reference, visit our documentation page.

Testing

The project uses pytest for unit tests. Install dev dependencies and run the test suite:

pip install -r requirements-dev.txt
pytest tests/ -v

With coverage:

pytest tests/ -v --cov=livef1 --cov-report=term-missing

Example scripts that use the library (without assertions) live in the examples/ directory.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• Report bugs and request features in GitHub Issues
• For questions and discussions, use GitHub Discussions

FEEL FREE TO CONTACT ME