data-visualization

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# Data Visualization Skill

Create publication-quality analytical charts using matplotlib and seaborn in a headless GPU sandbox. Charts are saved as PNG files to `/workspace/` for retrieval.

## When to Use This Skill

Use this skill when:
- Visualizing results from cuDF analysis or cuML models
- Creating charts (bar, line, scatter, heatmap, histogram, box plot)
- Building multi-panel analysis summaries
- The user asks for visual output, plots, graphs, or charts
- Presenting statistical findings with figures

## Initialization (REQUIRED)

MUST call `matplotlib.use('Agg')` BEFORE importing pyplot. This enables headless rendering.

```python
import matplotlib
matplotlib.use('Agg')  # Headless backend — MUST be before pyplot import
import matplotlib.pyplot as plt
import numpy as np

# Publication-quality defaults
plt.rcParams.update({
    'figure.dpi': 100,
    'savefig.dpi': 300,
    'font.size': 11,
    'axes.labelsize': 12,
    'axes.titlesize': 14,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'legend.fontsize': 10,
    'figure.constrained_layout.use': True,
})

# Colorblind-safe palette (Okabe-Ito)
COLORS = ['#0173B2', '#DE8F05', '#029E73', '#D55E00', '#CC78BC',
          '#CA9161', '#FBAFE4', '#949494', '#ECE133', '#56B4E9']
```

## Saving Charts

Always save to `/workspace/` with these settings:

```python
plt.savefig('/workspace/chart_name.png', dpi=300, bbox_inches='tight',
            facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inline
```

- `dpi=300` for print quality
- `bbox_inches='tight'` removes excess whitespace
- `facecolor='white'` ensures white background
- Always call `plt.close()` after saving to free memory

## Displaying Charts (REQUIRED)

After saving any chart, you MUST call `read_file` on it to display it inline in the conversation:

```
read_file("/workspace/chart_name.png")
```

Users cannot see charts unless you do this. Every chart you save MUST be followed by a `read_file` call.

## Quick Reference

### Bar Chart (from groupby results)

```python
# After: result = to_pd(df.groupby("category")["value"].mean())
fig, ax = plt.subplots(figsize=(8, 5))

bars = ax.bar(result.index, result.values, color=COLORS[:len(result)],
              edgecolor='black', linewidth=0.8)

for bar in bars:
    height = bar.get_height()
    ax.text(bar.get_x() + bar.get_width()/2., height,
            f'{height:.1f}', ha='center', va='bottom', fontsize=9)

ax.set_ylabel('Mean Value', fontweight='bold')
ax.set_xlabel('Category', fontweight='bold')
ax.set_title('Average Value by Category', fontweight='bold')
ax.grid(axis='y', alpha=0.3, linestyle='--')
ax.set_axisbelow(True)

plt.savefig('/workspace/bar_chart.png', dpi=300, bbox_inches='tight',
            facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inline
```

### Line Chart (trends over time)

```python
fig, ax = plt.subplots(figsize=(10, 5))

for i, col in enumerate(columns_to_plot):
    ax.plot(df["date"], df[col], label=col, color=COLORS[i], linewidth=2,
            marker='o', markersize=3, markevery=max(1, len(df)//20))

ax.set_ylabel('Values', fontweight='bold')
ax.set_xlabel('Date', fontweight='bold')
ax.set_title('Trends Over Time', fontweight='bold')
ax.legend(frameon=True, shadow=False)
ax.grid(True, alpha=0.3, linestyle='--')
ax.set_axisbelow(True)
plt.xticks(rotation=45, ha='right')

plt.savefig('/workspace/line_chart.png', dpi=300, bbox_inches='tight',
            facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inline
```

### Scatter Plot — Continuous Color (correlations)

From langchain-ai/deepagents, MIT.

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