data-visualization

Installation
Summary

Chart selection guidance, Python code patterns, and design principles for effective data visualizations.

  • Comprehensive chart selection table covering 13+ chart types with guidance on when to use each and common anti-patterns to avoid (pie charts, 3D, dual-axis)
  • Ready-to-use Python code examples for line charts, bar charts, histograms, heatmaps, small multiples, and interactive Plotly visualizations with professional styling
  • Design principles covering color theory (sequential, diverging, categorical palettes), typography, layout, and accuracy standards like zero-baseline bar charts
  • Accessibility checklist including colorblind-friendly palettes, screen reader considerations, contrast requirements, and black-and-white printability validation
SKILL.md

Data Visualization Skill

Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations.

Chart Selection Guide

Choose by Data Relationship

What You're Showing Best Chart Alternatives
Trend over time Line chart Area chart (if showing cumulative or composition)
Comparison across categories Vertical bar chart Horizontal bar (many categories), lollipop chart
Ranking Horizontal bar chart Dot plot, slope chart (comparing two periods)
Part-to-whole composition Stacked bar chart Treemap (hierarchical), waffle chart
Composition over time Stacked area chart 100% stacked bar (for proportion focus)
Distribution Histogram Box plot (comparing groups), violin plot, strip plot
Correlation (2 variables) Scatter plot Bubble chart (add 3rd variable as size)
Correlation (many variables) Heatmap (correlation matrix) Pair plot
Geographic patterns Choropleth map Bubble map, hex map
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Installs
6.4K
GitHub Stars
12.0K
First Seen
Jan 31, 2026