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matplotlib

Create publication-ready plots from Python with Figure/Axes control and PNG, PDF, or SVG export.

1|Updated Mar 5, 2026
Search Tags:#python#visualization#data-analysis#matplotlib#3d#publication-quality#plots
One-click install
npx skills add https://github.com/Lin-Hi/DeepRead --skill matplotlib-lin-hi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matplotlib
Source: https://github.com/Lin-Hi/DeepRead/tree/main/.claude/skills/matplotlib
Command: npx skills add https://github.com/Lin-Hi/DeepRead --skill matplotlib-lin-hi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Matplotlib guidance and templates reduce the friction of producing, styling, and exporting high-quality static and 3D visualizations from Python, making it easier to turn analytical results into publication-ready figures.

Core Features & Use Cases

  • Object-oriented Figure/Axes patterns and pyplot shortcuts for both quick exploration and production code.
  • Comprehensive examples for line, scatter, bar, histogram, heatmap, contour, violin/box, and 3D surface/mesh plots, plus multi-panel layouts and GridSpec/mosaic arrangements.
  • Styling, rcParams configuration, performance tips (rasterization, DPI), and export workflows for PNG/PDF/SVG to support papers, presentations, and notebooks.
  • Use Case: prepare a multi-panel figure for a research paper that combines time series, statistical distributions, and a heatmap with consistent styling and export it as a high-resolution PDF.

Quick Start

Create a publication-quality PNG from your DataFrame using the object-oriented API, apply a clean style, and save the figure 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 scientific plots in Python?▼

To create publication-quality scientific plots in Python, use the object-oriented Figure and Axes API to generate line, scatter, heatmap, and 3D visualizations, then configure style and export to high-resolution PNG, PDF, or SVG.

What is the best way to build a multi-panel figure with GridSpec for a research paper?▼

The best way to build a multi-panel figure for a research paper is using GridSpec or mosaic arrangements to combine time series and statistical distributions with consistent styling, then exporting the layout as a high-resolution PDF.

Can I generate 3D surface and mesh plots using matplotlib in my analysis workflow?▼

Yes, you can generate 3D surface and mesh plots in your analysis workflow. The Skill provides comprehensive templates for 3D visualizations alongside contour and violin plots for both quick exploration and production code.

Do I need numpy and scipy to create contour and heatmap visualizations?▼

You need numpy and scipy installed as dependencies to support data processing for contour and heatmap visualizations. These libraries provide the numerical foundation required for generating publication-ready plots.

How does rasterization and DPI configuration improve matplotlib export performance?▼

Rasterization and DPI configuration improve export performance by optimizing how vector and raster elements are rendered. Configuring rcParams allows you to balance file size and image quality when exporting to PDF or SVG.

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