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Tianyi-Billy-Ma
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matplotlib

Create publication-quality line, scatter, bar, histogram, heatmap, contour, and 3D plots with Matplotlib.

Updated Dec 8, 2025
Search Tags:#visualization#plotting#matplotlib#publication
One-click install
npx skills add https://github.com/Tianyi-Billy-Ma/PyTemplate --skill matplotlib
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matplotlib
Source: https://github.com/Tianyi-Billy-Ma/PyTemplate/tree/main/.dev/ai/skills/skills/matplotlib
Command: npx skills add https://github.com/Tianyi-Billy-Ma/PyTemplate --skill matplotlib

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

It guides creating publication-quality visualizations using Matplotlib, covering both pyplot and OO interfaces, layout/ styling best practices, and exporting to common formats.

Core Features & Use Cases

  • 2D/3D plotting and multi-panel figures: Line, scatter, bar, heatmaps, contours, 3D plots, and subplots.
  • Publication-ready style: Prebuilt styles and best practices for figures intended for journals.
  • Export & reproducibility: Save figures as PNG/PDF/SVG with proper DPI and bounding.

Quick Start

Install Matplotlib, then run the included plot_template.py or style presets to create and export a publication figure.

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 with Matplotlib?▼

Publication-quality plots with Matplotlib use the pyplot or object-oriented Figure/Axes interface combined with rcParams styling, tight layouts, and high-DPI export. Apply best practices like choosing appropriate colormaps, adding clear annotations, and exporting to PNG, PDF, or SVG formats with proper bounding to meet journal standards.

Can I create multi-panel figures and 3D plots in Matplotlib?▼

Yes. Matplotlib supports multi-panel figures through subplots, and includes 3D plotting capabilities alongside 2D plots like line, scatter, bar, histograms, heatmaps, and contours. Combine these plot types in a single figure for complex scientific visualizations.

What file formats can I export Matplotlib figures to?▼

Matplotlib exports figures to PNG, PDF, SVG, and interactive formats. Each format supports high-DPI settings and tight bounding to ensure crisp, reproducible output suitable for publications, presentations, and web use.

How do I style Matplotlib plots for scientific papers?▼

Style Matplotlib plots using prebuilt style sheets, rcParams configuration, and color mapping options. Combine consistent fonts, appropriate color schemes, and proper axis labels with tight layouts and high-DPI export for figures that meet publication requirements.

Do I need to know both pyplot and object-oriented Matplotlib interfaces?▼

No, but both are supported. The pyplot interface offers simpler syntax for quick plots, while the Figure/Axes object-oriented interface provides finer control over complex multi-panel figures and styling—choose based on your workflow complexity.

Can I use Matplotlib with NumPy and SciPy for scientific visualization?▼

Yes. Matplotlib integrates with NumPy for array handling and SciPy for scientific computation, enabling streamlined workflows for data exploration, notebook-based analysis, and figure generation from computed results.

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