LiveF1
Historical Data¶
The LiveF1 package provides access to historical Formula 1 data through Session objects, which contain comprehensive race data including timing, telemetry, and other metrics.
Note
Topic-level telemetry and timing archives (e.g. get_data, generate) generally require a session that is backed by Livetiming static data. Calendar and metadata may still come from Jolpica when meetings are merged or enriched. See Data sources: Livetiming and Jolpica for how availability flags behave.
Accessing Historical Data¶
Direct Session Access¶
The most common way to access historical data is through sessions:
import livef1
# Get specific session directly
race = livef1.get_session(
season=2024,
meeting_identifier="Spa", # Circuit/Location name
session_identifier="Race" # Session type
)
Note
You can also access data through Season and Meeting objects for broader data exploration:
# Alternative: Browse through season/meeting structure
season = livef1.get_season(2024)
belgian_gp = livef1.get_meeting(season=2024, meeting_identifier="Spa")
# View available sessions
print(belgian_gp.sessions_table)
Working with Session Data¶
Once you have a session object, you can access different types of data:
1. Raw Data Access¶
# Access specific data
weather_data = race.get_data("WeatherData")
car_data = race.get_data("CarData.z")
display(weather_data.head())
display(car_data.head())
| | SessionKey | timestamp | AirTemp | Humidity | Pressure | Rainfall | TrackTemp | WindDirection | WindSpeed |
|---:|-------------:|:-------------|----------:|-----------:|-----------:|-----------:|------------:|----------------:|------------:|
| 0 | 9574 | 00:00:14.052 | 21 | 52 | 978.1 | 0 | 42.1 | 217 | 0.5 |
| 1 | 9574 | 00:01:14.050 | 20.6 | 53 | 978 | 0 | 42.1 | 0 | 1.1 |
| 2 | 9574 | 00:02:14.047 | 20.6 | 52 | 978 | 0 | 42.2 | 284 | 0.7 |
| 3 | 9574 | 00:03:14.053 | 20.7 | 51 | 978.1 | 0 | 42 | 244 | 1.4 |
| 4 | 9574 | 00:04:14.057 | 20.7 | 51 | 978.1 | 0 | 41.1 | 13 | 1.2 |
| | SessionKey | timestamp | Utc | DriverNo | rpm | speed | n_gear | throttle | brake | drs |
|---:|-------------:|:-------------|:-----------------------------|-----------:|------:|--------:|---------:|-----------:|--------:|------:|
| 0 | 9574 | 00:02:28.746 | 2024-07-28T12:11:06.7233441Z | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 9574 | 00:02:28.746 | 2024-07-28T12:11:06.7233441Z | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 9574 | 00:02:28.746 | 2024-07-28T12:11:06.7233441Z | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 9574 | 00:02:28.746 | 2024-07-28T12:11:06.7233441Z | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 9574 | 00:02:28.746 | 2024-07-28T12:11:06.7233441Z | 10 | 0 | 0 | 0 | 0 | 0 | 0 |
See also
For a complete list of available topics and their descriptions, see LiveTiming Data Topics
2. Processed Data¶
Generate processed data tables using the medallion architecture:
# Generate silver and gold tables
race.generate()
# Access processed data
laps = race.laps
telemetry = race.carTelemetry
display(laps.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 |
Example: Complete Historical Data Analysis¶
Here’s a complete example showing how to access and analyze historical race data:
import livef1
# Get a specific race session
race = livef1.get_session(
season=2023,
meeting_identifier="Monaco",
session_identifier="Race"
)
# Generate processed data
race.generate()
# Get lap times and telemetry
laps_data = race.laps
telemetry = race.carTelemetry
# Analyze fastest laps
fastest_laps = laps_data.sort_values('LapTime').groupby('DriverNumber').first()
print("Fastest laps by driver:\n****************")
print(fastest_laps[['LapTime', 'LapNumber']])
Fastest laps by driver:
****************
| DriverNo | lap_time | lap_number |
|-----------:|:-----------------------|-------------:|
| 1 | 0 days 00:01:46.128000 | 32 |
| 10 | 0 days 00:01:47.418000 | 30 |
| 11 | 0 days 00:01:44.701000 | 44 |
| 14 | 0 days 00:01:48.051000 | 42 |
| 16 | 0 days 00:01:47.013000 | 33 |
| 18 | 0 days 00:01:48.105000 | 44 |
| 2 | 0 days 00:01:47.490000 | 43 |
| 20 | 0 days 00:01:47.848000 | 44 |
| 22 | 0 days 00:01:47.969000 | 44 |
| 23 | 0 days 00:01:47.996000 | 44 |
| 24 | 0 days 00:01:52.099000 | 2 |
| 27 | 0 days 00:01:48.954000 | 44 |
| 3 | 0 days 00:01:47.435000 | 37 |
| 31 | 0 days 00:01:46.957000 | 43 |
| 4 | 0 days 00:01:45.563000 | 31 |
| 44 | 0 days 00:01:46.653000 | 33 |
| 55 | 0 days 00:01:46.364000 | 44 |
| 63 | 0 days 00:01:47.113000 | 44 |
| 77 | 0 days 00:01:47.019000 | 37 |
| 81 | 0 days 00:01:45.840000 | 32 |