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

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

Workflow

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

.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