data-visualization
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
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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