Getting Started Guide

import tif1

# Load the 2025 monaco grand prix race
session = tif1.get_session(2025, "Monaco Grand Prix", "Race")

print(f"Loaded session: {session.event['EventName']} {session.name}")

# Get list of drivers
drivers = session.drivers_df

print(drivers[["Driver", "Team", "DriverNumber"]])

  Driver                Team  DriverNumber
0    VER  Red Bull Racing            33
1    HAM         Mercedes            44
2    LEC          Ferrari            16
...

# Get all laps
laps = session.laps

# Show fastest laps
fastest = laps.nsmallest(10, "LapTime")
print(fastest[["Driver", "LapNumber", "LapTime", "Compound"]])

# Get specific drivers
ver = session.get_driver("VER")
lec = session.get_driver("LEC")

# Compare fastest laps
ver_fastest = ver.get_fastest_lap()
lec_fastest = lec.get_fastest_lap()

print(f"VER: {ver_fastest['LapTime'].iloc[0].total_seconds():.3f}s")
print(f"LEC: {lec_fastest['LapTime'].iloc[0].total_seconds():.3f}s")

# Get telemetry for fastest lap
ver_lap = ver.get_fastest_lap()
lap_number = ver_lap["LapNumber"].iloc[0]

lap = ver.get_lap(int(lap_number))
telemetry = lap.telemetry

# Analyze speed
print(f"Top speed: {telemetry['Speed'].max()} km/h")
print(f"Avg speed: {telemetry['Speed'].mean():.1f} km/h")

import tif1

# List all events in 2025
events = tif1.get_events(2025)
print(f"Found {len(events)} events")

# List sessions for an event
sessions = tif1.get_sessions(2025, "Monaco Grand Prix")
print(f"Available sessions: {sessions}")

laps = session.laps

# Remove deleted laps
clean = laps[~laps["Deleted"]]

# Remove pit laps
clean = clean[clean["PitInTime"].isna()]

# Remove lap 1 (standing start)
clean = clean[clean["LapNumber"] > 1]

# Filter by compound
soft_laps = clean[clean["Compound"] == "SOFT"]

# Group by driver and stint
strategy = laps.groupby(["Driver", "Stint"]).agg({
    "Compound": "first",
    "LapNumber": ["min", "max", "count"]
}).reset_index()

print(strategy)

# Get weather data
weather = session.weather

# Check for rain
if weather["Rainfall"].any():
    print("Rain detected during session")
    rain_laps = weather[weather["Rainfall"]]["Time"]
    print(f"Rain from {rain_laps.min():.0f}s to {rain_laps.max():.0f}s")

import asyncio
import tif1

async def load_data():
    session = tif1.get_session(2025, "Monaco", "Race")
    laps = await session.laps_async()
    return laps

laps = asyncio.run(load_data())

# Use polars lib for 2x faster processing
session_pl = tif1.get_session(
    2025,
    "Monaco",
    "Race",
    lib="polars"
)

laps_pl = session_pl.laps  # Returns polars DataFrame

import matplotlib.pyplot as plt

# Plot lap times
laps = session.laps
ver_laps = laps[laps["Driver"] == "VER"]

plt.figure(figsize=(12, 6))
plt.plot(ver_laps["LapNumber"], ver_laps["LapTime"].dt.total_seconds(), marker="o")
plt.xlabel("Lap Number")
plt.ylabel("Lap Time (s)")
plt.title("VER Lap Times - Monaco 2025")
plt.grid(True, alpha=0.3)
plt.show()

import tif1

try:
    session = tif1.get_session(2025, "Monaco", "Race")
    laps = session.laps
except tif1.DataNotFoundError:
    print("Session data not available")
except tif1.NetworkError as e:
    print(f"Network error: {e}")
except tif1.TIF1Error as e:
    print(f"Error: {e}")

import tif1

# Get configuration
config = tif1.get_config()

# Change cache directory
config.cache_dir = "/custom/cache/path"

# Use polars by default
config.lib = "polars"

# Enable debug logging
config.log_level = "DEBUG"

# Check cache directory permissions
# Replace <cache-dir> with the platform-specific tif1 cache directory
ls -la <cache-dir>/

# Fix permissions
chmod -R 755 <cache-dir>/

import tif1

config = tif1.get_config()
config.request_timeout = 30  # Increase timeout to 30s

# Use polars (50% less memory)
session = tif1.get_session(2025, "Monaco", "Race", lib="polars")

# Or process drivers one at a time
for driver_code in session.drivers:
    driver = session.get_driver(driver_code)
    laps = driver.laps
    # Process laps
    del laps  # Free memory