Why does this skill recommend the Object-Oriented interface?

The OO interface (fig, ax) is more explicit, easier to debug, and significantly better for managing complex figures with multiple subplots compared to the implicit state-based pyplot interface.

Can I create 3D plots with this skill?

Yes, the skill includes guidance and code patterns for surface plots, 3D scatter plots, and 3D line plots using the mplot3d toolkit.

When should I use Matplotlib vs Seaborn?

Use Matplotlib when you need total control over every plot element or need to create novel plot types. Use Seaborn for quicker statistical plots that benefit from higher-level defaults.

How do I ensure my plots are publication-ready?

The skill provides best practices for setting high DPI (300+), using vector formats like PDF/SVG, and selecting perceptually uniform, colorblind-friendly colormaps like viridis.

Matplotlib Visualization

Name: Matplotlib Visualization
Author: Weiwei-Mao

byWeiwei-Mao

Data Science & ML

Generates highly customizable, publication-quality static and interactive plots using Python's foundational visualization library.

About

This skill provides expert guidance on using Matplotlib to create a wide range of scientific and statistical visualizations, from basic line charts to complex multi-panel figures. It emphasizes the recommended object-oriented interface for superior control, covers diverse plot types like heatmaps and 3D surfaces, and provides best practices for export formats, colormaps, and accessibility. It is designed for researchers and data scientists who require precise, fine-grained control over every element of their data presentation.

Key Features

Implementation of both Object-Oriented and Pyplot interfaces
Advanced multi-panel figure layouts using Subplots, Mosaic, and GridSpec
Extensive styling and customization via rcParams and stylesheets
Publication-ready exports in high-resolution PNG, PDF, and SVG formats
0 GitHub stars
Support for 3D visualizations and interactive animations

Use Cases

Developing custom heatmaps and contour plots for environmental data analysis
Building automated data reporting pipelines with consistent branding and styles
Creating complex multi-panel scientific figures for academic publications

About

Key Features

Implementation of both Object-Oriented and Pyplot interfaces
Advanced multi-panel figure layouts using Subplots, Mosaic, and GridSpec
Extensive styling and customization via rcParams and stylesheets
Publication-ready exports in high-resolution PNG, PDF, and SVG formats
0 GitHub stars
Support for 3D visualizations and interactive animations

Use Cases

Developing custom heatmaps and contour plots for environmental data analysis
Building automated data reporting pipelines with consistent branding and styles
Creating complex multi-panel scientific figures for academic publications