Data Visualization

---
name: data-visualization
description: Create charts, graphs, and visualizations from data. Use when the user needs to visualize data, create charts, or generate reports with graphics.
---

# Data Visualization Skill

This skill provides capabilities for creating data visualizations.

## Quick Start

Using matplotlib for basic charts:

```python
import matplotlib.pyplot as plt

# Simple line chart
plt.plot([1, 2, 3, 4], [1, 4, 2, 3])
plt.title("Sample Chart")
plt.savefig("chart.png")
```

## Capabilities

### Chart Types
- Line charts
- Bar charts
- Pie charts
- Scatter plots
- Histograms
- Box plots
- Heatmaps

### Libraries Supported
- Matplotlib (static charts)
- Seaborn (statistical visualizations)
- Plotly (interactive charts)
- Altair (declarative visualization)

### Advanced Features
- Multi-axis plots
- Subplots and grids
- Custom themes and styling
- Annotations and labels
- Export to various formats (PNG, SVG, PDF)

## Best Practices

1. Choose the right chart type for your data
2. Use clear labels and titles
3. Consider color accessibility
4. Keep visualizations simple and focused
5. Export at appropriate resolution for intended use