Beautiful Data Viz
Create polished, publication-ready visualizations in Python/Jupyter with strong typography, clean layout, and accessible color choices.
Instructions
- Clarify the message, audience, and medium (notebook/paper/slides).
- Choose the simplest chart type that answers the question.
- Select an appropriate palette type (categorical/sequential/diverging).
- Apply the shared style helpers, then build the plot.
- Validate readability at target size and export with tight bounds.
Quick Reference
| Task | Action |
|---|---|
| Apply style | Use assets/beautiful_style.py helpers |
| Pick palette | See references/palettes.md |
| QA checklist | See references/checklist.md |
| Plot recipes | See examples/recipes.md |
Input Requirements
- Data in a tabular form (pandas DataFrame or similar)
- Clear statement of the primary message
- Target medium and background preference
Output
- Publication-ready figure(s) (PNG/SVG/PDF)
- Consistent styling and labeling
Quality Gates
- Message is clear in 3 seconds at target size
- Labels and units are readable and accurate
- Color choice is colorblind-safe and grayscale-tolerant
- Layout is tight with minimal whitespace
Examples
Example 1: Apply the shared style helper
from assets.beautiful_style import set_beautiful_style, finalize_axes
set_beautiful_style(medium="notebook", background="light")
# build plot here
finalize_axes(ax, title="Example", subtitle="", tight=True)Troubleshooting
Issue: Labels overlap or are unreadable Solution: Reduce tick count, rotate labels, or increase figure width.
Issue: Colors are hard to distinguish Solution: Use a colorblind-safe categorical palette and limit categories.