> ## Documentation Index
> Fetch the complete documentation index at: https://tif1.tracinginsights.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Migration from fastf1

> Step-by-step guide to switching to tif1

`tif1` is designed to be a drop-in replacement for `fastf1` for most common use cases. This guide highlights the similarities and the few key differences you need to know.

## Comparison at a Glance

| Feature           | fastf1                  | tif1                    |
| :---------------- | :---------------------- | :---------------------- |
| **Data Source**   | Ergast + F1 Live Timing | TracingInsights (CDN)   |
| **Loading Speed** | Baseline                | **4-5x faster** (Async) |
| **Lib**           | Pandas only             | **Pandas + Polars**     |
| **Caching**       | File-based Pickle       | **SQLite + Memory LRU** |
| **HTTP Version**  | HTTP/1.1                | **HTTP/2**              |

***

## Migration Steps

<Steps>
  <Step title="Update Imports">
    Change `import fastf1` to `import tif1`.

    ```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
    import tif1 as ff1 # Alias to keep existing code working
    ```
  </Step>

  <Step title="Update get_session Calls">
    Use full GP names and remove any `session.load()` calls. `tif1` loads data lazily.

    <CodeGroup>
      ```python tif1 (Lazy) theme={"theme":{"light":"github-light","dark":"github-dark"}}
      session = tif1.get_session(2025, "Monaco Grand Prix", "Race")
      laps = session.laps # Fetches automatically
      ```

      ```python fastf1 (Explicit) theme={"theme":{"light":"github-light","dark":"github-dark"}}
      session = fastf1.get_session(2025, "Monaco", "R")
      session.load()
      laps = session.laps
      ```
    </CodeGroup>
  </Step>

  <Step title="Check Filters">
    If you used `pick_` methods, switch to standard DataFrame filtering or use the new `Session` methods.

    | fastf1                    | tif1                                        |
    | :------------------------ | :------------------------------------------ |
    | `laps.pick_fastest()`     | `session.get_fastest_laps(by_driver=False)` |
    | `laps.pick_driver('VER')` | `session.get_driver('VER').laps`            |
  </Step>
</Steps>

***

## Key API Differences

### 1. Lazy Loading

tif1 uses lazy loading by default. No need to call `session.load()`.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# fastf1
session = fastf1.get_session(2025, "Monaco", "R")
session.load()
laps = session.laps

# tif1
session = tif1.get_session(2025, "Monaco Grand Prix", "Race")
laps = session.laps  # Automatically fetches on first access
```

### 2. Property Access

In `fastf1`, telemetry is often a method. In `tif1`, it is a property for a more modern feel.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# fastf1
telemetry = lap.get_telemetry()

# tif1
telemetry = lap.telemetry
```

### 3. Driver Information

tif1 provides a convenient `drivers_df` DataFrame with comprehensive driver information.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# tif1
drivers_df = session.drivers_df
print(drivers_df[["Driver", "Team", "DriverNumber", "TeamColor"]])
```

### 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:

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# Massive speed gain for initial load
laps = await session.laps_async()
```

***

## Feature Parity

<CardGroup cols={2}>
  <Card title="Archive Only" icon="box-archive">
    `tif1` focuses on historical data (2018-current). It does not support Live Timing.
  </Card>

  <Card title="Track Maps" icon="map">
    Track map generation is currently not supported but is planned for a future release.
  </Card>

  <Card title="Weather Data" icon="cloud">
    We provide raw 1-minute weather samples. `fastf1` style interpolation is not yet implemented.
  </Card>

  <Card title="Categorical Optimization" icon="database">
    `tif1` optimizes memory by default using categoricals, which may behave slightly differently in some Pandas operations.
  </Card>
</CardGroup>

<Info>
  Ready to start? Check out the [Quickstart](/quickstart) guide.
</Info>

***

## Related Pages

<CardGroup cols={2}>
  <Card title="Quickstart" href="/quickstart">
    Get started quickly
  </Card>

  <Card title="API Reference" href="/api-reference/core">
    Core API details
  </Card>

  <Card title="FastF1 Compat" href="/api-reference/fastf1-compat">
    Compatibility layer
  </Card>

  <Card title="Best Practices" href="/guides/best-practices">
    tif1 patterns
  </Card>
</CardGroup>