tif1 is a drop-in replacement for fastf1 in most common use cases. This guide shows the similarities and the key differences.
Comparison at a Glance
Migration Steps
1
Update Imports
Change
import fastf1 to import tif1.2
Update get_session Calls
Use full GP names and remove any
session.load() calls. tif1 loads data lazily.3
Check Filters
Replace
pick_ methods with standard DataFrame filtering or the new Session methods.Key API Differences
1. Lazy Loading
tif1 uses lazy loading by default. No need to callsession.load().
2. Property Access
Infastf1, telemetry is often a method. In tif1, it is a property for a more modern feel.
3. Driver Information
tif1 provides a convenientdrivers_df DataFrame with comprehensive driver information.
4. Enriched Data
tif1 automatically enriches data:- Weather data is included in every lap
- Telemetry includes DriverAhead and DistanceToDriverAhead
- LapTimeSeconds is provided as a numeric helper alongside LapTime
5. Async Optimization
For maximum performance, adopt the async pattern:Feature Parity
Archive Only
tif1 focuses on historical data (2018-current). It does not support Live Timing.Track Maps
Track map generation is not supported. It is planned for a future release.
Weather Data
tif1 provides raw 1-minute weather samples.
fastf1 style interpolation is not yet implemented.Categorical Optimization
tif1 optimizes memory by default using categoricals, which may behave slightly differently in some Pandas operations.Ready to start? Read the Quickstart guide.
Related Pages
Why tif1?
The full fastf1 comparison — rate limits, granularity, and more.
Quickstart
Get started quickly.
API Reference
Core API details.
FastF1 Compat
Compatibility layer.
Best Practices
tif1 patterns.