---
name: data-viz
description: Create data visualizations, charts, and graphs using Python matplotlib, seaborn, and plotly
---

# Data Visualization Skill

## Capabilities

Create publication-quality charts and visualizations using Python libraries.

## Chart Types

### Bar Charts
```python
import matplotlib.pyplot as plt
import seaborn as sns

# Data
categories = ['A', 'B', 'C']
values = [10, 20, 15]

# Create
plt.figure(figsize=(10, 6))
sns.barplot(x=categories, y=values, palette='viridis')
plt.title('Bar Chart Title', fontsize=14, fontweight='bold')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.tight_layout()
plt.savefig('/tmp/bar_chart.png', dpi=300)
```

### Line Charts
```python
import pandas as pd

# Time series data
dates = pd.date_range('2024-01', periods=12, freq='M')
values = [100, 105, 110, 108, 115, 120, 118, 125, 130, 128, 135, 140]

plt.figure(figsize=(12, 6))
plt.plot(dates, values, marker='o', linewidth=2, markersize=8)
plt.title('Monthly Trend')
plt.xlabel('Month')
plt.ylabel('Value')
plt.grid(True, alpha=0.3)
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('/tmp/line_chart.png', dpi=300)
```

### Pie Charts
```python
labels = ['Category A', 'Category B', 'Category C']
sizes = [30, 45, 25]
colors = ['#ff9999', '#66b3ff', '#99ff99']

plt.figure(figsize=(8, 8))
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', 
        startangle=90, textprops={'fontsize': 12})
plt.title('Distribution', fontsize=14, fontweight='bold')
plt.axis('equal')
plt.savefig('/tmp/pie_chart.png', dpi=300)
```

### Heatmaps
```python
import numpy as np

# Correlation matrix
data = np.random.rand(5, 5)
labels = ['A', 'B', 'C', 'D', 'E']

plt.figure(figsize=(10, 8))
sns.heatmap(data, annot=True, fmt='.2f', xticklabels=labels, 
            yticklabels=labels, cmap='coolwarm', center=0)
plt.title('Correlation Heatmap')
plt.tight_layout()
plt.savefig('/tmp/heatmap.png', dpi=300)
```

## Styling Best Practices

- Use consistent color schemes
- Add titles and labels
- Include legends when needed
- Use appropriate chart types for data
- 300 DPI for high quality
- Transparent backgrounds for flexibility

## Output

Always save to `/tmp/` with descriptive filenames.