beautiful-data-viz

name	beautiful-data-viz
description	Create publication-quality matplotlib/seaborn charts with readable axes, tight layout, and curated palettes.
argument-hint	[medium=notebook|paper|slides] [background=light|dark]

Beautiful Data Viz

Create polished, publication-ready visualizations in Python/Jupyter with strong typography, clean layout, and accessible color choices.

Instructions

1. Clarify the message, audience, and medium (notebook/paper/slides).
2. Choose the simplest chart type that answers the question.
3. Select an appropriate palette type (categorical/sequential/diverging).
4. Apply the shared style helpers, then build the plot.
5. 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.