Lap

Lap class for accessing single lap timing data and telemetry

Overview

The Lap class is a pandas Series representing a single lap with timing data and telemetry access. It extends pandas Series to provide convenient access to lap-specific data.

Properties

session

lap.session -> Session

Reference to the parent Session object.

driver

lap.driver -> str

Driver code for this lap (e.g., “VER”, “HAM”).

lap_number

lap.lap_number -> int

Lap number.

telemetry

lap.telemetry -> Telemetry

Get telemetry data for this lap.

Returns: Telemetry DataFrame with telemetry data:

• Time: Time offset from lap start (timedelta)
• SessionTime: Time offset from session start (timedelta)
• Speed: Speed in km/h (float)
• RPM: Engine RPM (int)
• nGear: Gear number (int)
• Throttle: Throttle position 0-100 (float)
• Brake: Brake status (bool or 0-100)
• DRS: DRS status (int)
• X, Y, Z: Position coordinates (float)
• Distance: Distance along track in meters (float)

Returns empty DataFrame if telemetry not available.

Methods

get_telemetry

lap.get_telemetry() -> Telemetry

Get telemetry data with driver-ahead channels added.

Returns: Telemetry DataFrame with additional columns:

• DriverAhead: Driver code of the driver ahead
• DistanceToDriverAhead: Distance to driver ahead in meters

get_car_data

lap.get_car_data() -> Telemetry

Get car data (alias for telemetry property).

Returns: Telemetry DataFrame

get_pos_data

lap.get_pos_data() -> Telemetry

Get position data (alias for telemetry property).

Returns: Telemetry DataFrame

get_weather_data

lap.get_weather_data() -> pd.Series

Get weather data for this lap.

Returns: Empty pandas Series (weather data is session-level, not lap-specific)

Lap Data Attributes

The Lap object contains the following timing data as pandas Series values:

• LapNumber (int): Lap number
• LapTime (timedelta): Total lap time
• LapTimeSeconds (float): Lap time in seconds
• Driver (str): Driver code
• Team (str): Team name
• Position (int): Position at end of lap
• Sector1Time (timedelta): Sector 1 time
• Sector2Time (timedelta): Sector 2 time
• Sector3Time (timedelta): Sector 3 time
• Compound (str): Tyre compound (e.g., “SOFT”, “MEDIUM”, “HARD”)
• TyreLife (int): Tyre age in laps
• Stint (int): Stint number
• TrackStatus (str): Track status code
• IsPersonalBest (bool): Whether this is the driver’s personal best
• IsAccurate (bool): Whether timing is accurate
• PitInTime (timedelta): Pit entry time (if applicable)
• PitOutTime (timedelta): Pit exit time (if applicable)
• SpeedI1, SpeedI2, SpeedFL, SpeedST (float): Speed trap readings
• AirTemp, TrackTemp, Humidity, Pressure (float): Weather conditions
• FreshTyre (bool): Whether fresh tyres were fitted
• Deleted (bool): Whether lap was deleted
• DeletedReason (str): Reason for deletion

Usage Examples

Accessing Lap Data

import tif1

session = tif1.get_session(2025, "Monaco Grand Prix", "Qualifying")
driver = session.get_driver("VER")
lap = driver.get_lap(15)

# Access timing data
print(f"Lap {lap.lap_number}")
print(f"Driver: {lap.driver}")
print(f"Lap time: {lap['LapTime']}")
print(f"Sector 1: {lap['Sector1Time']}")
print(f"Sector 2: {lap['Sector2Time']}")
print(f"Sector 3: {lap['Sector3Time']}")
print(f"Position: {lap['Position']}")
print(f"Compound: {lap['Compound']}")
print(f"Tyre life: {lap['TyreLife']} laps")

Getting Telemetry

lap = driver.get_lap(15)
tel = lap.telemetry

if not tel.empty:
    print(f"Telemetry samples: {len(tel)}")
    print(f"Max speed: {tel['Speed'].max():.1f} km/h")
    print(f"Min speed: {tel['Speed'].min():.1f} km/h")
    print(f"Average speed: {tel['Speed'].mean():.1f} km/h")

Analyzing Sector Performance

lap = driver.get_lap(15)

# Get sector times
s1 = lap['Sector1Time'].total_seconds()
s2 = lap['Sector2Time'].total_seconds()
s3 = lap['Sector3Time'].total_seconds()
total = lap['LapTime'].total_seconds()

print(f"Sector 1: {s1:.3f}s ({s1/total*100:.1f}%)")
print(f"Sector 2: {s2:.3f}s ({s2/total*100:.1f}%)")
print(f"Sector 3: {s3:.3f}s ({s3/total*100:.1f}%)")

Telemetry Analysis

lap = driver.get_lap(15)
tel = lap.telemetry

if not tel.empty:
    # Speed analysis
    max_speed = tel['Speed'].max()
    speed_at_finish = tel['Speed'].iloc[-1]
    print(f"Max speed: {max_speed:.1f} km/h")
    print(f"Speed at finish line: {speed_at_finish:.1f} km/h")
    
    # Throttle analysis
    full_throttle = (tel['Throttle'] >= 99).sum()
    full_throttle_pct = full_throttle / len(tel) * 100
    print(f"Full throttle: {full_throttle_pct:.1f}% of lap")
    
    # Gear usage
    gear_counts = tel['nGear'].value_counts().sort_index()
    print("Gear usage:")
    for gear, count in gear_counts.items():
        pct = count / len(tel) * 100
        print(f"  Gear {gear}: {pct:.1f}%")
    
    # DRS usage
    if 'DRS' in tel.columns:
        drs_active = (tel['DRS'] > 0).sum()
        drs_pct = drs_active / len(tel) * 100
        print(f"DRS active: {drs_pct:.1f}% of lap")

Comparing Laps

driver = session.get_driver("VER")
lap1 = driver.get_lap(5)
lap2 = driver.get_lap(15)

# Compare lap times
time_diff = (lap2['LapTime'] - lap1['LapTime']).total_seconds()
print(f"Time difference: {time_diff:+.3f}s")

# Compare sector times
for sector in [1, 2, 3]:
    col = f"Sector{sector}Time"
    diff = (lap2[col] - lap1[col]).total_seconds()
    print(f"Sector {sector} diff: {diff:+.3f}s")

# Compare compounds
print(f"Lap 5 compound: {lap1['Compound']}")
print(f"Lap 15 compound: {lap2['Compound']}")
print(f"Tyre age difference: {lap2['TyreLife'] - lap1['TyreLife']} laps")

Speed Trap Analysis

lap = driver.get_lap(15)

# Check speed traps
speed_traps = {
    'Intermediate 1': lap['SpeedI1'],
    'Intermediate 2': lap['SpeedI2'],
    'Finish Line': lap['SpeedFL'],
    'Speed Trap': lap['SpeedST']
}

for trap_name, speed in speed_traps.items():
    if not pd.isna(speed):
        print(f"{trap_name}: {speed:.1f} km/h")

Checking Lap Validity

lap = driver.get_lap(15)

print(f"Is accurate: {lap['IsAccurate']}")
print(f"Is personal best: {lap['IsPersonalBest']}")
print(f"Is deleted: {lap['Deleted']}")

if lap['Deleted']:
    print(f"Deletion reason: {lap['DeletedReason']}")

# Check for pit stop
if not pd.isna(lap['PitInTime']) or not pd.isna(lap['PitOutTime']):
    print("This lap includes a pit stop")
    if not pd.isna(lap['PitInTime']):
        print(f"Pit entry at: {lap['PitInTime']}")
    if not pd.isna(lap['PitOutTime']):
        print(f"Pit exit at: {lap['PitOutTime']}")

Weather Conditions

lap = driver.get_lap(15)

print(f"Air temp: {lap['AirTemp']:.1f}°C")
print(f"Track temp: {lap['TrackTemp']:.1f}°C")
print(f"Humidity: {lap['Humidity']:.1f}%")
print(f"Pressure: {lap['Pressure']:.1f} mbar")

See Also

• Session - Main session object
• Driver - Driver-specific data
• Laps - Collection of laps
• Telemetry - Telemetry data operations