visualization▌

Expert guidance on chart selection and data visualization design for clear data communication. \n \n Covers five core chart categories: comparison (bar/column), distribution (histograms/box plots), relationship (scatter/bubble), composition (pie/stacked bars), and trends (line/area) \n Emphasizes four foundational principles: clarity, honesty, simplicity, and accessibility for color-blind audiences \n Provides chart type recommendations with rationale, code examples using matplotlib and plotly,

Clear, effective data visualizations with chart selection rules, design principles, and storytelling techniques. \n \n Covers 10+ chart types with decision rules for when to use each (line for time series, bar for comparison, scatter for correlation, heatmap for patterns) \n Design guidelines for axes, color theory, typography, and annotations including colorblind-safe palettes and a strong stance against pie charts \n Includes ready-to-run Python/matplotlib recipes for line charts, bar charts,