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matplotlib

Generate customizable static and animated plots from Python data workflows.

2|Updated Apr 12, 2026
Search Tags:#python#style#visualization#plotting#matplotlib#3d#publication
One-click install
npx skills add https://github.com/Clay-HHK/claude-config --skill matplotlib-clay-hhk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matplotlib
Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/matplotlib
Command: npx skills add https://github.com/Clay-HHK/claude-config --skill matplotlib-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, matplotlib, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Matplotlib eliminates the friction of producing highly customized, publication-ready visualizations from Python data workflows by providing precise control over every plot element, layout, and export setting. It helps users move beyond quick exploratory charts to create figures that meet journal, presentation, or product requirements while integrating with notebooks, scripts, and GUI applications.

Core Features & Use Cases

  • Flexible Interfaces: Covers both pyplot (stateful) and the recommended object-oriented Figure/Axes API for robust, maintainable code.
  • Comprehensive Plot Types: Line, scatter, bar, histogram, heatmap, contour, box/violin, polar, quiver, and 3D surface/scatter with layout tools (subplots, mosaics, GridSpec).
  • Styling & Export: Full rcParams and style-sheet control, colorblind-friendly colormap guidance, and export to PNG/PDF/SVG at publication DPI.
  • Utilities & Templates: Includes script templates and a style configurator to standardize publication or presentation-ready figures.
  • Use Case: Prepare multi-panel figures for a manuscript by programmatically styling axes, annotating results, and saving vector PDF files for submission.

Quick Start

Create a multi-panel, publication-quality figure from your DataFrame using the object-oriented API, set figsize and dpi, apply a consistent style, and save the result as figure.png.

Frequently Asked Questions about matplotlib

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

FAQPage Schema
How do I create publication-quality plots from a Python DataFrame?▼

To create publication-quality plots from a Python DataFrame, use the object-oriented Figure/Axes API to set figsize and dpi, apply consistent style sheets, and export the result to vector PDF or high-resolution PNG files.

Can I build multi-panel figures for a manuscript using Python?▼

Yes, you can build multi-panel figures for a manuscript using Python by utilizing layout tools like subplots, mosaics, and GridSpec to programmatically style axes, annotate results, and arrange multiple charts in a single figure.

Does matplotlib work with Jupyter notebooks and command-line scripts?▼

Matplotlib works with Jupyter notebooks, command-line scripts, and GUI embedding, allowing you to generate static and animated plots while providing fine-grained control over Figure and Axes properties across these Python environments.

What is the best way to style 3D surface and contour maps in Python?▼

The best way to style 3D surface and contour maps in Python is by applying rcParams-based styling and colorblind-friendly colormaps, giving you full control over plot aesthetics for scientific and presentation workflows.

How do I export Python charts to SVG or PDF at a specific DPI?▼

You can export Python charts to SVG or PDF at a specific DPI by using the built-in export functions, ensuring your visualizations meet journal or product requirements for publication-ready vector and raster formats.

Why use the object-oriented API instead of pyplot for Python data visualization?▼

You should use the object-oriented API instead of pyplot for Python data visualization because it provides robust, maintainable code through fine-grained control over Figure and Axes properties, which is essential for complex multi-panel figures.

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