# Matplotlib

Low-level plotting library for full customization.

- Repository: https://github.com/zLanqing/codex-claude-academic-skills (5550 stars)
- Category: Documents
- License: MIT
- Last updated: 2026-05-14
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add zLanqing/codex-claude-academic-skills --skill matplotlib -g -a claude-code -y`
Codex: `npx skills add zLanqing/codex-claude-academic-skills --skill matplotlib -g -a codex -y`
Cursor: `npx skills add zLanqing/codex-claude-academic-skills --skill matplotlib -g -a cursor -y`
Gemini CLI: `npx skills add zLanqing/codex-claude-academic-skills --skill matplotlib -g -a gemini-cli -y`

## Description

Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Matplotlib

## Overview

Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. This skill provides guidance on using matplotlib effectively, covering both the pyplot interface (MATLAB-style) and the object-oriented API (Figure/Axes), along with best practices for creating publication-quality visualizations.

## When to Use This Skill

This skill should be used when:
- Creating any type of plot or chart (line, scatter, bar, histogram, heatmap, contour, etc.)
- Generating scientific or statistical visualizations
- Customizing plot appearance (colors, styles, labels, legends)
- Creating multi-panel figures with subplots
- Exporting visualizations to various formats (PNG, PDF, SVG, etc.)
- Building interactive plots or animations
- Working with 3D visualizations
- Integrating plots into Jupyter notebooks or GUI applications

## Core Concepts

### The Matplotlib Hierarchy

Matplotlib uses a hierarchical structure of objects:

1. **Figure** - The top-level container for all plot elements
2. **Axes** - The actual plotting area where data is displayed (one Figure can contain multiple Axes)
3. **Artist** - Everything visible on the figure (lines, text, ticks, etc.)
4. **Axis** - The number line objects (x-axis, y-axis) that handle ticks and labels

### Two Interfaces

**1. pyplot Interface (Implicit, MATLAB-style)**
```python
import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()
```
- Convenient for quick, simple plots
- Maintains state automatically
- Good for interactive work and simple scripts

**2. Object-Oriented Interface (Explicit)**
```python
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4])
ax.set_ylabel('some numbers')
plt.show()
```
- **Recommended for most use cases**
- More explicit control over figure and axes
- Better for complex figures with multiple subplots
- Easier to maintain and debug

## Common Workflows

The rest: https://github.com/zLanqing/codex-claude-academic-skills/blob/HEAD/scientific-toolkit-skill/references/scientific-skills/matplotlib/SKILL.md