Alterlab Seaborn
Skill Verified ActivePart of the AlterLab Academic Skills suite. Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
To enable quick and attractive statistical data exploration and visualization using Python, integrating seamlessly with pandas DataFrames.
Features
- Statistical visualization with pandas integration
- Supports box plots, violin plots, pair plots, heatmaps
- Offers both function and declarative object interfaces
- Built on matplotlib with attractive defaults
- Provides extensive documentation and examples
Use Cases
- Quickly exploring data distributions and relationships
- Creating attractive default visualizations for academic research
- Generating publication-quality statistical graphics
- Performing multivariate analysis with minimal code
Non-Goals
- Interactive plots (use plotly instead)
- Publication styling beyond attractive defaults (use scientific-visualization for precise styling)
- Directly manipulating raw data beyond what's needed for plotting
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-SkillsRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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