jinfanzheng/data-viz

Mental Model

Visualization is choosing the right chart to answer a specific question. Chart type depends on data relationship, not aesthetics.

Chart Selection

| Question | Chart Type | |----------|------------| | Trends over time? | Line chart | | Compare categories? | Bar chart | | Show distribution? | Histogram, box plot | | Relationship between variables? | Scatter plot | | Parts of whole? | Pie, stacked bar | | 2D patterns? | Heatmap | | Financial data? | Candlestick, OHLC |

Anti-Patterns (NEVER)

• Don't use Chinese characters anywhere in charts - use English for labels, titles, legends, data labels
• Don't use Chinese characters in filenames (ASCII only)
• Don't pick wrong chart type for the question
• Don't overload with data → aggregate or sample
• Don't forget labels, title, legend
• Don't use poor colors (colorblind-safe palettes)

Chart language: Always use English (titles, axes, legends, labels) to avoid font rendering issues.

Output Formats

• PNG: Static, high-quality for reports
• HTML: Interactive (zoom, pan, hover)
• SVG: Vector for editing

Filename: {chart_type}_{timestamp}.{ext} (ASCII only)

Workflow

1. Ask: What's the question? What story to tell?
2. Load data from CSV/JSON (or data-analysis output)
3. Choose chart type based on question
4. Create Python script and execute using virtual environment:

   .venv/bin/python script.py
   

5. Return file path to user

Python Environment

Auto-initialize virtual environment if needed, then execute:

# Navigate to skill directory
cd skills/data-viz

# Auto-create venv if not exists
if [ ! -f ".venv/bin/python" ]; then
    echo "Creating Python environment..."
    ./setup.sh
fi

# Execute script
.venv/bin/python your_script.py

The setup script auto-installs: matplotlib, seaborn, plotly, pandas with Chinese font support.

References (load on demand)

For chart APIs and code templates, load: references/REFERENCE.md, references/templates.md