data-visualization

Chart selection guidance, Python code patterns, and design principles for effective data visualizations. Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations. Before sharing a visualization: - 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 - Pie charts : Avoid unless <6 categories and exact proportions matter less than rough comparison. Humans are bad at comparing angles. Use bar charts instead. - 3D charts : Never. They distort perception and add no information. - Dual-axis charts : Use cautiously.