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matplotlib

Generate static, animated, and interactive plots from Python data with matplotlib.

46|4|Updated Apr 8, 2023
Search Tags:#data visualization#python#matplotlib#scientific plotting#publication-ready figures#plot customization
One-click install
npx skills add https://github.com/CRAG666/dotfiles --skill matplotlib-crag666
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matplotlib
Source: https://github.com/CRAG666/dotfiles/tree/main/skills/matplotlib
Command: npx skills add https://github.com/CRAG666/dotfiles --skill matplotlib-crag666

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

It helps you turn raw data into clear, customizable visualizations for analysis, reporting, and publication—without being limited to default styling or simplistic plotting approaches.

Core Features & Use Cases

  • Choose the right plotting interface: Use the object-oriented Figure/Axes API for reliable control over complex figures.
  • Build the full plot workflow: Create common chart types (line, scatter, bar, histogram, heatmap, contour) and multi-panel layouts with subplots, subplot_mosaic, or GridSpec.
  • Achieve publication quality: Customize fonts, styling, colormaps, layout spacing, and export to PNG/PDF/SVG with appropriate DPI.

Quick Start

Use the matplotlib skill to generate an object-oriented, multi-panel figure (Figure/Axes) from your dataset and export it as a high-resolution PNG for sharing or as a vector PDF/SVG for publication.

Frequently Asked Questions about matplotlib

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

FAQPage Schema
How do I create publication-ready scientific plots with Python data?▼

Generate publication-ready scientific plots by applying the object-oriented Figure/Axes API to your Python data, customizing rcParams and colormaps, and exporting to high-resolution PNG, PDF, or SVG formats with controlled DPI and bounding boxes.

What's the best way to build multi-panel figures in Python?▼

Build multi-panel figures by utilizing subplots, subplot_mosaic, or GridSpec for layout design, and apply constrained_layout or tight_layout to manage spacing and prevent overlapping elements across panels.

Can I customize line, scatter, bar, and heatmap visualizations in matplotlib?▼

Customize line, scatter, bar, histogram, heatmap, and contour visualizations by leveraging the object-oriented Figure/Axes API and modifying fonts, colormaps, and rcParams styling to achieve clear data reporting and analysis.

How do I export high-resolution vector graphics from Python scripts and notebooks?▼

Export high-resolution vector graphics from Python scripts and notebooks by utilizing publication-ready export workflows that save figures to PDF or SVG formats with controlled DPI and bounding boxes.

Does this data visualization approach support interactive and animated plots?▼

This data visualization approach supports generating customized static, animated, and interactive plots from Python data, enabling dynamic visualization of complex datasets across both scripts and notebooks.

When should I use the object-oriented Figure/Axes API over default plotting?▼

Use the object-oriented Figure/Axes API over default plotting when you need reliable control over complex multi-panel figures, advanced plot customization, and precise layout management for publication-ready outputs.

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