Data Visualization

import tif1
from tif1.plotting import plot_speed_comparison

session = tif1.get_session(2025, "Monaco Grand Prix", "Qualifying")

# Get fastest laps for two drivers
ver_fastest = session.get_driver("VER").get_fastest_lap()
lec_fastest = session.get_driver("LEC").get_fastest_lap()

# Plot speed comparison
plot_speed_comparison(
    [ver_fastest, lec_fastest],
    labels=["VER", "LEC"],
    title="Monaco Q3 Speed Comparison"
)
```python

### Lap Time Evolution

Track how lap times evolve throughout a stint.

```python
from tif1.plotting import plot_lap_times

session = tif1.get_session(2025, "Bahrain Grand Prix", "Race")
laps = session.laps

# Filter to specific drivers
drivers = ["VER", "HAM", "LEC"]
filtered = laps[laps["Driver"].isin(drivers)]

plot_lap_times(
    filtered,
    by_driver=True,
    highlight_compounds=True
)
```python

## Matplotlib Integration

For custom visualizations, use matplotlib directly.

### Telemetry Overlay

```python
import matplotlib.pyplot as plt
import tif1

session = tif1.get_session(2025, "Silverstone", "Race")
ver = session.get_driver("VER")
lap = ver.get_lap(15)
tel = lap.telemetry

fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 8), sharex=True)

# Speed
ax1.plot(tel["Distance"], tel["Speed"], color="#E10600", linewidth=2)
ax1.set_ylabel("Speed (km/h)")
ax1.grid(True, alpha=0.3)

# Throttle
ax2.plot(tel["Distance"], tel["Throttle"], color="#00D2BE", linewidth=2)
ax2.set_ylabel("Throttle (%)")
ax2.grid(True, alpha=0.3)

# Brake
ax3.plot(tel["Distance"], tel["Brake"].astype(int), color="#FF1E00", linewidth=2)
ax3.set_ylabel("Brake")
ax3.set_xlabel("Distance (m)")
ax3.grid(True, alpha=0.3)

plt.suptitle(f"VER Lap {lap['LapNumber']} Telemetry", fontsize=14, fontweight="bold")
plt.tight_layout()
plt.show()
```python

### Tire strategy visualization

```python
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

session = tif1.get_session(2025, "Monza", "Race")
laps = session.laps

# Compound colors
compound_colors = {
    "SOFT": "#FF0000",
    "MEDIUM": "#FFF200",
    "HARD": "#FFFFFF",
    "INTERMEDIATE": "#00FF00",
    "WET": "#0000FF"
}

fig, ax = plt.subplots(figsize=(14, 8))

drivers = laps["Driver"].unique()
for i, driver in enumerate(drivers):
    driver_laps = laps[laps["Driver"] == driver].sort_values("LapNumber")

    for _, lap in driver_laps.iterrows():
        color = compound_colors.get(lap["Compound"], "#CCCCCC")
        ax.barh(i, 1, left=lap["LapNumber"], color=color, edgecolor="black", linewidth=0.5)

ax.set_yticks(range(len(drivers)))
ax.set_yticklabels(drivers)
ax.set_xlabel("Lap Number")
ax.set_title("Tire Strategy - 2025 Monza GP")

# Legend
patches = [mpatches.Patch(color=color, label=compound)
           for compound, color in compound_colors.items()]
ax.legend(handles=patches, loc="upper right")

plt.tight_layout()
plt.show()
```python

## Plotly for interactive plots

Plotly enables interactive visualizations perfect for Jupyter notebooks.

### Interactive speed trace

```python
import plotly.graph_objects as go
import tif1

session = tif1.get_session(2025, "Spa", "Qualifying")
ver = session.get_driver("VER")
fastest = ver.get_fastest_lap()
tel = fastest.telemetry

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=tel["Distance"],
    y=tel["Speed"],
    mode="lines",
    name="Speed",
    line=dict(color="#E10600", width=2),
    hovertemplate="Distance: %{x:.0f}m<br>Speed: %{y:.0f} km/h<extra></extra>"
))

fig.update_layout(
    title="VER Fastest Lap - Speed Trace",
    xaxis_title="Distance (m)",
    yaxis_title="Speed (km/h)",
    hovermode="x unified",
    template="plotly_dark"
)

fig.show()
```python

### 3D Track Map

```python
import plotly.graph_objects as go

session = tif1.get_session(2025, "Suzuka", "Race")
ver = session.get_driver("VER")
lap = ver.get_lap(1)
tel = lap.telemetry

fig = go.Figure(data=[go.Scatter3d(
    x=tel["X"],
    y=tel["Y"],
    z=tel["Z"],
    mode="lines",
    line=dict(
        color=tel["Speed"],
        colorscale="Viridis",
        width=4,
        colorbar=dict(title="Speed (km/h)")
    ),
    hovertemplate="X: %{x:.0f}<br>Y: %{y:.0f}<br>Z: %{z:.0f}<br>Speed: %{marker.color:.0f} km/h<extra></extra>"
)])

fig.update_layout(
    title="3D Track Map - Suzuka",
    scene=dict(
        xaxis_title="X (m)",
        yaxis_title="Y (m)",
        zaxis_title="Z (m)",
        aspectmode="data"
    ),
    template="plotly_dark"
)

fig.show()
```python

## Seaborn for statistical plots

Seaborn excels at statistical visualizations.

### Lap Time Distribution

```python
import seaborn as sns
import matplotlib.pyplot as plt

session = tif1.get_session(2025, "Barcelona", "Race")
laps = session.laps

# Clean data
clean = laps[~laps["Deleted"] & (laps["LapNumber"] > 1)]

plt.figure(figsize=(12, 6))
sns.violinplot(data=clean, x="Driver", y="LapTime", hue="Compound", split=False)
plt.xticks(rotation=45)
plt.title("Lap Time Distribution by Driver and Compound")
plt.ylabel("Lap Time (s)")
plt.tight_layout()
plt.show()
```python

### Correlation Heatmap

```python
import seaborn as sns
import matplotlib.pyplot as plt

session = tif1.get_session(2025, "Singapore", "Race")
laps = session.laps

# Select numeric columns
numeric_cols = ["LapTime", "Sector1Time", "Sector2Time", "Sector3Time",
                "SpeedI1", "SpeedI2", "SpeedFL", "SpeedST",
                "AirTemp", "TrackTemp", "TyreLife"]

correlation = laps[numeric_cols].corr()

plt.figure(figsize=(10, 8))
sns.heatmap(correlation, annot=True, fmt=".2f", cmap="coolwarm", center=0)
plt.title("Lap Data Correlation Matrix")
plt.tight_layout()
plt.show()
```python ## Best Practices

### Use Team Colors

Make plots more recognizable by using official team colors.

```python
session = tif1.get_session(2025, "Monaco", "Race")
drivers_df = session.drivers_df

# Create color map
color_map = dict(zip(drivers_df["Driver"], drivers_df["TeamColor"]))

# Use in plots
for driver in ["VER", "HAM", "LEC"]:
    driver_laps = laps[laps["Driver"] == driver]
    plt.plot(driver_laps["LapNumber"], driver_laps["LapTime"],
             color=f"#{color_map[driver]}", label=driver)
```python

### Handle missing data

Always filter out invalid laps before plotting.

```python
def clean_for_plotting(laps):
    """Prepare laps for visualization."""
    clean = laps.copy()

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

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

    # Remove outliers
    fastest = clean["LapTime"].min()
    clean = clean[clean["LapTime"] < fastest * 1.10]

    return clean

laps = session.laps
plot_data = clean_for_plotting(laps)
```bash

### Optimize for Performance

For large datasets, downsample telemetry before plotting.

```python
# Downsample telemetry to every 10th point
tel_downsampled = tel[::10]

plt.plot(tel_downsampled["Distance"], tel_downsampled["Speed"])
```bash

## Export for external tools

Save data for use in other visualization tools.

```python
# Export to CSV
laps.to_csv("race_laps.csv", index=False)

# Export to Parquet (smaller, faster)
laps.to_parquet("race_laps.parquet")

# Export to Excel
with pd.ExcelWriter("race_analysis.xlsx") as writer:
    laps.to_excel(writer, sheet_name="Laps", index=False)
    session.weather.to_excel(writer, sheet_name="Weather", index=False)
```python

---

## Related Pages

<CardGroup cols={2}>
  <Card title="Plotting API" href="/api-reference/plotting">
    Plotting reference
  </Card>
  <Card title="Examples" href="/examples">
    Visualization examples
  </Card>
  <Card title="Telemetry Tutorial" href="/tutorials/advanced-telemetry">
    Telemetry viz
  </Card>
</CardGroup>