from didi-skills52
Practical guidance and Python code patterns (matplotlib, seaborn, plotly) for choosing chart types and producing accessible, publication-quality data visualizat
Provides a compact, practical guide to selecting appropriate chart types and implementing them in Python. Includes code patterns, styling best-practices, accessibility checks, and templates for line, bar, histogram, heatmap, small-multiples, and interactive Plotly charts. Useful for analysts and engineers who need reproducible, publication-ready visualizations.
Use this skill when you need to: pick the correct chart for a dataset (time series, distribution, correlation, geographic), produce clean and accessible figures for reports or presentations, convert exploratory plots into polished visuals, or add interactive Plotly exports for sharing.
Works well with Claude Code, Copilot-like coding assistants, and any agent that can run or suggest Python snippets for visualization and reporting.
This skill has not been reviewed by our automated audit pipeline yet.
