Does this skill support the Object-Oriented Matplotlib API?

Yes, it explicitly recommends and provides implementation patterns for the Object-Oriented interface (Figure/Axes) for superior control and code maintainability.

Can I generate high-resolution figures for publications?

Absolutely. The skill includes specific configurations for DPI settings, vector exports (PDF/SVG), and tight layout management suitable for scientific journals.

Does it handle complex multi-chart layouts?

Yes, it covers standard grids, flexible mosaic layouts, and precise GridSpec configurations for creating intricate multi-panel figures.

How does this help with visualization accessibility?

It promotes best practices like using perceptually uniform, colorblind-friendly colormaps (like viridis) and adding hatching patterns to bar charts.

Matplotlib Visualization Expert

Name: Matplotlib Visualization Expert
Author: lifangda

bylifangda

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Data Science & ML

Generates professional, publication-quality Python visualizations including line plots, heatmaps, and 3D charts using the Matplotlib library.

About

This skill empowers Claude to create sophisticated static, animated, and interactive visualizations using Matplotlib's foundational Python library. It provides expert guidance on choosing between the pyplot and object-oriented interfaces, implementing complex multi-panel subplot layouts, and applying best practices for scientific plotting. Whether you need a simple trend line or a high-resolution figure for an academic publication, this skill ensures your charts follow industry standards for accessibility, styling, and data accuracy.

Key Features

Publication-quality export support for PNG, PDF, and SVG formats
Implementation of both Object-Oriented and pyplot Python interfaces
16 GitHub stars
Custom styling via style sheets and fine-grained rcParams configuration
Comprehensive 3D visualization and statistical plotting capabilities
Advanced multi-panel subplot and mosaic layout configurations

Use Cases

Developing automated data visualization workflows for Python-based analytics
Generating complex scientific charts and figures for academic research
Creating custom statistical distributions, heatmaps, and correlation matrices

About

Key Features

Publication-quality export support for PNG, PDF, and SVG formats
Implementation of both Object-Oriented and pyplot Python interfaces
16 GitHub stars
Custom styling via style sheets and fine-grained rcParams configuration
Comprehensive 3D visualization and statistical plotting capabilities
Advanced multi-panel subplot and mosaic layout configurations

Use Cases

Developing automated data visualization workflows for Python-based analytics
Generating complex scientific charts and figures for academic research
Creating custom statistical distributions, heatmaps, and correlation matrices