Matplotlib
Low-level plotting library for full customization.
Install
npx skills add zLanqing/codex-claude-academic-skills --skill matplotlib -g -a claude-code -y- Repository
- zLanqing/codex-claude-academic-skills5.5k
- Category
- Documents
- Updated
- 6mo ago
- License
- MIT
- Context cost
- Mediumabout 2,726 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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:
- Figure - The top-level container for all plot elements
- Axes - The actual plotting area where data is displayed (one Figure can contain multiple Axes)
- Artist - Everything visible on the figure (lines, text, ticks, etc.)
- Axis - The number line objects (x-axis, y-axis) that handle ticks and labels
Two Interfaces
1. pyplot Interface (Implicit, MATLAB-style)
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)
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
