Jupyter Integration

import tif1
session = tif1.get_session(2025, "Monaco Grand Prix", "Race")
session # This will render the rich HTML display
```sql

### Driver Info
The driver display highlights the 3-letter driver code and indicates whether their lap data has been fetched from the cdn.

```python
ver = session.get_driver("VER")
ver
```python

### Lap Info
The lap display shows the lap number, driver, and whether high-frequency telemetry data has been loaded.

```python
lap = ver.get_lap(19)
lap
```python

---

## DataFrame Summaries

`tif1` also includes a helper to display a clean summary of its DataFrames, showing the number of rows, columns, and total memory usage.

```python
from tif1.jupyter import display_dataframe_summary

laps = session.laps
display_dataframe_summary(laps)
```python

<Info>
  This works seamlessly with both **Pandas** and **Polars** backends.
</Info>

---

## How it Works

The integration is automatically enabled if `tif1` detects it is running in a Jupyter environment. It uses the `_repr_html_` protocol supported by IPython.

### Manual Enable/Disable

If you need to manually trigger the display setup (e.g., in some niche notebook environments):

```python
from tif1.jupyter import enable_jupyter_display

enable_jupyter_display()
```sql

---

## Visual Themes

The HTML components are designed to be theme-agnostic. They use semi-transparent borders and backgrounds to ensure they are readable in both **Light Mode** and **Dark Mode**.

<CardGroup cols={2}>
  <Card title="Light Mode" icon="sun">
    Clean, subtle borders with light background.
  </Card>
  <Card title="Dark Mode" icon="moon">
    Transparent overlays that adapt to your notebook's dark theme.
  </Card>
</CardGroup>

<Tip>
  If you are using **Polars**, `tif1` objects will still render rich HTML, and you'll benefit from even faster data processing in your interactive sessions.
</Tip>