Laps API

The Laps and Lap classes provide access to lap timing data with filtering and telemetry access.

Laps

DataFrame-like object for accessing lap timing data of multiple laps. Constructor:

Session | None

default:"None"

Instance of Session class (required for full functionality)

Available Columns:

Time (timedelta64[ns]): Time when the lap was completed
Driver (str): Driver three-letter abbreviation
DriverNumber (str): Driver number
LapTime (timedelta64[ns]): Lap time duration
LapNumber (float64): Lap number
Stint (float64): Stint number
PitOutTime (timedelta64[ns]): Time when car exited pit lane
PitInTime (timedelta64[ns]): Time when car entered pit lane
Sector1Time (timedelta64[ns]): Sector 1 time
Sector2Time (timedelta64[ns]): Sector 2 time
Sector3Time (timedelta64[ns]): Sector 3 time
Sector1SessionTime (timedelta64[ns]): Session time at sector 1
Sector2SessionTime (timedelta64[ns]): Session time at sector 2
Sector3SessionTime (timedelta64[ns]): Session time at sector 3
SpeedI1 (float64): Speed trap at intermediate 1 (km/h)
SpeedI2 (float64): Speed trap at intermediate 2 (km/h)
SpeedFL (float64): Speed trap at finish line (km/h)
SpeedST (float64): Speed trap at speed trap (km/h)
IsPersonalBest (bool): Whether this lap is the driver’s personal best
Compound (str): Tyre compound (‘SOFT’, ‘MEDIUM’, ‘HARD’, ‘INTERMEDIATE’, ‘WET’)
TyreLife (float64): Laps completed on this set of tyres
FreshTyre (bool): Whether tyres were new when fitted
Team (str): Team name
LapStartTime (timedelta64[ns]): Time when the lap started
LapStartDate (datetime64[ns]): Date when the lap started
TrackStatus (str): Track status flags during the lap
Position (float64): Position at the end of this lap
Deleted (bool): Whether the lap time was deleted
DeletedReason (str): Reason for deletion
FastF1Generated (bool): Whether this lap was generated by FastF1
IsAccurate (bool): Whether the lap passed accuracy validation

Filtering Methods

pick_drivers()

Return all laps of the specified driver(s).

int | str | Iterable[int | str]

required

Driver abbreviation(s) or driver number(s) - can be mixed

Laps

Filtered Laps object

Example:

# Single driver
ver_laps = laps.pick_drivers('VER')
ham_laps = laps.pick_drivers(44)

# Multiple drivers
top_3_laps = laps.pick_drivers(['VER', 'HAM', 'LEC'])
mixed = laps.pick_drivers([1, 'HAM', 44])

pick_teams()

Return all laps of the specified team(s).

str | Iterable[str]

required

Team name(s)

Laps

Filtered Laps object

Example:

# Single team
rbr_laps = laps.pick_teams('Red Bull Racing')

# Multiple teams
top_teams = laps.pick_teams(['Red Bull Racing', 'Ferrari', 'Mercedes'])

pick_laps()

Return all laps matching specific lap number(s).

int | Iterable[int]

required

Lap number or iterable of lap numbers

Laps

Filtered Laps object

Example:

# Single lap
lap_1 = laps.pick_laps(1)

# Range of laps
laps_10_to_20 = laps.pick_laps(range(10, 21))

# Specific laps
specific = laps.pick_laps([1, 5, 10, 20])

pick_fastest()

Return the lap with the fastest lap time.

bool

default:"False"

If False, only return laps marked as personal best. If True, ignore personal best flag and return the absolute fastest lap.

Lap | None

The fastest Lap object, or None if no valid lap exists

Example:

# Fastest personal best lap
fastest = laps.pick_fastest()

# Absolute fastest lap (even if deleted)
fastest_any = laps.pick_fastest(only_by_time=True)

pick_quicklaps()

Return all laps faster than a threshold (default: 107% of fastest lap).

float | None

default:"None"

Custom threshold coefficient (e.g., 1.05 for 105%). Defaults to 1.07 (107%).

Laps

Filtered Laps object

Example:

# Use default 107% rule
quick_laps = laps.pick_quicklaps()

# Custom 105% threshold
quick_laps = laps.pick_quicklaps(threshold=1.05)

pick_compounds()

Return all laps done on specific tyre compound(s).

str | Iterable[str]

required

Compound name(s): ‘SOFT’, ‘MEDIUM’, ‘HARD’, ‘INTERMEDIATE’, ‘WET’, ‘UNKNOWN’, ‘TEST_UNKNOWN’

Laps

Filtered Laps object

Example:

# Single compound
soft_laps = laps.pick_compounds('SOFT')

# Multiple compounds (all slicks)
slick_laps = laps.pick_compounds(['SOFT', 'MEDIUM', 'HARD'])

pick_track_status()

Return all laps set under a specific track status.

str

required

The track status as a string (e.g., ‘1’ for green flag, ‘2’ for yellow flag, ‘4’ for safety car)

str

default:"'equals'"

Matching method: ‘equals’, ‘contains’, ‘excludes’, ‘any’, ‘none’

Laps

Filtered Laps object

Example:

# Green flag laps only
green_laps = laps.pick_track_status('1', how='equals')

# Any lap with yellow flag
yellow_laps = laps.pick_track_status('2', how='contains')

pick_wo_box()

Return all laps which are NOT in-laps or out-laps.

Laps

Filtered Laps object

pick_box_laps()

Return in-laps, out-laps, or both.

str

default:"'both'"

One of ‘in’, ‘out’, or ‘both’

Laps

Filtered Laps object

pick_accurate()

Return all laps which pass accuracy validation.

Laps

Filtered Laps object

pick_not_deleted()

Return all laps whose lap times are NOT deleted.

Laps

Filtered Laps object

Telemetry Methods

get_telemetry()

Get telemetry data for all laps in this Laps object.

int | Literal['original'] | None

default:"None"

Optional frequency to override the default. Either ‘original’ or an integer for frequency in Hz.

Telemetry

Telemetry object with merged car and position data

Laps must contain data from only one driver.

get_car_data()

Get car data (Speed, RPM, Throttle, etc.) for all laps.

Telemetry

Telemetry object with car data only

get_pos_data()

Get position data (X, Y, Z coordinates) for all laps.

Telemetry

Telemetry object with position data only

get_weather_data()

Return weather data for each lap.

pd.DataFrame

DataFrame with weather data for each lap

Other Methods

split_qualifying_sessions()

Split laps into Q1, Q2, and Q3 sessions.

list[Optional[Laps]]

List containing three Laps objects for Q1, Q2, and Q3. Returns None for cancelled sessions.

Example:

quali = session.laps
q1, q2, q3 = quali.split_qualifying_sessions()

if q3 is not None:
    print(f"Q3 fastest: {q3.pick_fastest()['LapTime']}")

iterlaps()

Iterator for iterating over all laps.

Iterable | None

default:"None"

List of required column names. Only yields laps where all required values are non-null.

Example:

for index, lap in laps.iterlaps(require=['LapTime', 'Sector1Time']):
    print(f"Lap {lap['LapNumber']}: {lap['LapTime']}")

Lap

Single lap object (returned when slicing Laps to a single row). Properties: All columns from Laps are accessible as properties on a Lap object. Methods:

get_telemetry()

Get telemetry data for this lap.

int | Literal['original'] | None

default:"None"

Optional frequency override

Telemetry

Telemetry object for this lap

get_car_data()

Get car data for this lap.

Telemetry

Telemetry object with car data

get_pos_data()

Get position data for this lap.

Telemetry

Telemetry object with position data

get_weather_data()

Get weather data for this lap.

pd.Series

Weather data for this lap

Complete Usage Example

import fastf1
import matplotlib.pyplot as plt

# Load session
session = fastf1.get_session(2023, 'Monaco', 'Race')
session.load()

# Get all laps
laps = session.laps

# Filter laps
ver_laps = laps.pick_drivers('VER').pick_quicklaps()
print(f"Verstappen quick laps: {len(ver_laps)}")

# Get fastest lap
fastest = ver_laps.pick_fastest()
print(f"Fastest lap: {fastest['LapTime']}")
print(f"Compound: {fastest['Compound']}")
print(f"Tyre life: {fastest['TyreLife']} laps")

# Get telemetry for fastest lap
tel = fastest.get_telemetry()

# Plot speed trace
fig, ax = plt.subplots()
ax.plot(tel['Distance'], tel['Speed'])
ax.set_xlabel('Distance (m)')
ax.set_ylabel('Speed (km/h)')
ax.set_title('VER Fastest Lap - Speed Trace')
plt.show()

# Compare two drivers
ver_fastest = laps.pick_drivers('VER').pick_fastest()
ham_fastest = laps.pick_drivers('HAM').pick_fastest()

ver_tel = ver_fastest.get_telemetry()
ham_tel = ham_fastest.get_telemetry()

fig, ax = plt.subplots()
ax.plot(ver_tel['Distance'], ver_tel['Speed'], label='VER')
ax.plot(ham_tel['Distance'], ham_tel['Speed'], label='HAM')
ax.set_xlabel('Distance (m)')
ax.set_ylabel('Speed (km/h)')
ax.legend()
plt.show()

Core API

Data Access

Visualization

Utilities

Laps

Filtering Methods

pick_drivers()

pick_teams()

pick_laps()

pick_fastest()

pick_quicklaps()

pick_compounds()

pick_track_status()

pick_wo_box()

pick_box_laps()

pick_accurate()

pick_not_deleted()

Telemetry Methods

get_telemetry()

get_car_data()

get_pos_data()

get_weather_data()

Other Methods

split_qualifying_sessions()

iterlaps()

Lap

get_telemetry()

get_car_data()

get_pos_data()

get_weather_data()

Complete Usage Example

​Laps

​Filtering Methods

​pick_drivers()

​pick_teams()

​pick_laps()

​pick_fastest()

​pick_quicklaps()

​pick_compounds()

​pick_track_status()

​pick_wo_box()

​pick_box_laps()

​pick_accurate()

​pick_not_deleted()

​Telemetry Methods

​get_telemetry()

​get_car_data()

​get_pos_data()

​get_weather_data()

​Other Methods

​split_qualifying_sessions()

​iterlaps()

​Lap

​get_telemetry()

​get_car_data()

​get_pos_data()

​get_weather_data()

​Complete Usage Example

Laps

Filtering Methods

pick_drivers()

pick_teams()

pick_laps()

pick_fastest()

pick_quicklaps()

pick_compounds()

pick_track_status()

pick_wo_box()

pick_box_laps()

pick_accurate()

pick_not_deleted()

Telemetry Methods

get_telemetry()

get_car_data()

get_pos_data()

get_weather_data()

Other Methods

split_qualifying_sessions()

iterlaps()

Lap

get_telemetry()

get_car_data()

get_pos_data()

get_weather_data()

Complete Usage Example