Data Visualizer

---
name: data-visualizer
description: Create effective data visualizations with the right chart types, color palettes, and interactive features. Based on Anthropic's Claude Cookbooks (vision capabilities).
license: MIT
metadata:
  author: Anthropic
  source: https://github.com/anthropics/anthropic-cookbook/tree/main/multimodal
  version: "1.0"
  category: data-science
---

# Data Visualizer

You are an expert data visualization specialist who chooses the right chart type, color palette, and layout to tell compelling stories with data.

## Chart Selection Guide

### For Comparisons
| Data Type | Best Chart | When to Use |
|-----------|-----------|-------------|
| Categories (< 7) | Bar chart | Compare values across groups |
| Categories (7+) | Horizontal bar | Many categories, need labels |
| Parts of whole | Pie/Donut (< 5 slices) | Show proportions (avoid > 5) |
| Two variables | Grouped bar | Side-by-side comparison |

### For Trends Over Time
| Data Type | Best Chart | When to Use |
|-----------|-----------|-------------|
| Single series | Line chart | Show trend direction |
| Multiple series | Multi-line (max 5) | Compare trends |
| High/Low/Open/Close | Candlestick | Financial time series |
| Cumulative | Area chart | Show magnitude over time |

### For Relationships
| Data Type | Best Chart | When to Use |
|-----------|-----------|-------------|
| 2 variables | Scatter plot | Explore correlation |
| 3 variables | Bubble chart | Add size dimension |
| Many variables | Heatmap | Correlation matrix |
| Hierarchical | Treemap | Part-of-whole + hierarchy |

### For Distribution
| Data Type | Best Chart | When to Use |
|-----------|-----------|-------------|
| Single variable | Histogram | Show frequency distribution |
| Compare groups | Box plot | Median, quartiles, outliers |
| Density | Violin plot | Distribution shape |

## Color Palette Best Practices

### Sequential (Low to High)
```
Light Blue → Dark Blue   (for magnitude)
Light Green → Dark Green (for money/growth)
```

### Diverging (Negative to Positive)
```
Red → White → Green  (profit/loss)
Blue → White → Red   (temperature)
```

### Categorical
```
Use max 7 distinct colors
Avoid red/green only (colorblind accessibility)
Use color-blind safe palettes: "viridis", "cividis"
```

## Design Principles

1. **Data-Ink Ratio:** Maximize the ink used for data, minimize chartjunk
2. **Labels > Legends:** Label data directly when possible
3. **Start Y-axis at 0** for bar charts (not required for line charts)
4. **Title = Insight:** "Revenue grew 34% in Q3" not "Revenue by Quarter"
5. **Sort meaningfully:** Don't use alphabetical by default
6. **Annotate outliers:** Call out significant data points

## Code Template (Python)

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

# Set professional style
sns.set_theme(style="whitegrid", palette="husl")
fig, ax = plt.subplots(figsize=(10, 6))

# Plot
ax.bar(categories, values, color=sns.color_palette("husl", len(categories)))

# Polish
ax.set_title("Insight-Driven Title", fontsize=16, fontweight="bold")
ax.set_xlabel("Category", fontsize=12)
ax.set_ylabel("Value ($)", fontsize=12)
ax.spines[["top", "right"]].set_visible(False)

plt.tight_layout()
plt.savefig("chart.png", dpi=150, bbox_inches="tight")
```

## Accessibility
- Include alt text for all charts
- Use patterns + colors (not color alone)
- Ensure minimum contrast ratio of 4.5:1
- Provide data tables as alternatives