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plotnine

Create data visualizations with the plotnine Python library.

226|35|Updated Feb 7, 2026
Search Tags:#data visualization#charts#graphics#python#ggplot#plotnine
One-click install
npx skills add https://github.com/DAAF-Contribution-Community/daaf --skill plotnine
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: plotnine
Source: https://github.com/DAAF-Contribution-Community/daaf/tree/main/.claude/skills/plotnine
Command: npx skills add https://github.com/DAAF-Contribution-Community/daaf --skill plotnine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive reference for creating publication-quality data visualizations in Python using the plotnine library, which implements the grammar of graphics.

Core Features & Use Cases

  • Grammar of Graphics: Build complex plots declaratively by layering components.
  • Extensive Geoms: Supports scatter plots, line plots, bar charts, histograms, box plots, and more.
  • Customization: Control aesthetics like color, size, shape, and themes for polished output.
  • Use Case: Generate a scatter plot showing the relationship between two variables, colored by a categorical group, with custom axis labels and a minimal theme.

Quick Start

Use the plotnine skill to create a scatter plot of 'sepal_length' vs 'sepal_width' from the iris dataset, coloring points by 'species'.

Frequently Asked Questions about plotnine

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create publication-quality Python data visualizations using the grammar of graphics?▼

Python data visualizations based on the grammar of graphics are created declaratively by layering components like geoms, aesthetics, scales, and themes to build publication-ready plots. This approach allows precise control over plot elements.

What is the best way to build complex charts in Python without writing extensive code?▼

Building complex charts is best handled declaratively by stacking layers such as geoms and coordinate systems. This methodology lets you generate scatter plots, bar charts, and histograms with minimal code while maintaining high customization.

Can I use plotnine to generate a scatter plot colored by a categorical group?▼

Yes, you can generate a scatter plot colored by a categorical group by mapping the category to aesthetics. This allows you to visualize relationships between two variables while differentiating data points by group color.

Does this approach support customizing axes, themes, and facets for reporting tasks?▼

Customizing axes, themes, and facets is fully supported for reporting tasks. You can control aesthetics like color and size, apply minimal themes, and use faceting to generate polished, publication-ready outputs for data analysis.

What types of geoms are available for data visualization in this library?▼

Available geoms for data visualization include scatter plots, line plots, bar charts, histograms, and box plots. These extensive geom options facilitate rapid visualization development for diverse data analysis and reporting requirements.

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