How do I fix overlapping labels in my Matplotlib subplots?

The skill provides guidance on using constrained_layout=True or tight_layout() to automatically manage spacing and prevent elements from overlapping.

Why does this skill recommend the Object-Oriented interface over Pyplot?

The Object-Oriented interface is recommended for production code because it provides explicit control over Figure and Axes objects, making scripts easier to debug and maintain compared to the implicit state-based Pyplot interface.

Does this skill help with accessibility in data visualization?

Absolutely. It promotes best practices such as using colorblind-friendly colormaps like viridis and adding hatching or patterns to ensure charts are readable by everyone.

Can I generate high-resolution images for research papers?

Yes, the skill includes configurations for high-DPI (300+) PNG exports and vector formats like PDF and SVG, which are essential for academic and professional publishing.

Matplotlib Visualization Suite

Name: Matplotlib Visualization Suite
Author: jackspace

byjackspace

•

Data Science & ML

Generate professional, publication-quality Python visualizations ranging from basic line plots to complex 3D figures and multi-panel subplots.

About

This skill empowers Claude to create high-quality, customizable visualizations using Matplotlib, Python's foundational plotting library. It provides expert guidance on the Object-Oriented API for robust control over figures and axes, offering templates for line plots, scatter plots, histograms, and heatmaps. Whether you are conducting exploratory data analysis or preparing high-resolution vector exports for scientific publications, this skill ensures best practices in layout management, colorblind-friendly palette selection, and performance optimization for large datasets.

Key Features

High-resolution exports for PNG, PDF, and SVG with publication-ready DPI
2 GitHub stars
Advanced layout management using subplots, mosaic layouts, and GridSpec
Standardized styling using built-in style sheets and custom rcParams
Support for both Pyplot and professional Object-Oriented interfaces
Comprehensive 3D plotting capabilities and animation support

Use Cases

Visualizing complex multi-dimensional datasets with 3D surfaces and scatter plots
Creating scientific publication figures with specific resolution and font requirements
Building interactive data visualizations and exploratory heatmaps for analysis

About

Key Features

High-resolution exports for PNG, PDF, and SVG with publication-ready DPI
2 GitHub stars
Advanced layout management using subplots, mosaic layouts, and GridSpec
Standardized styling using built-in style sheets and custom rcParams
Support for both Pyplot and professional Object-Oriented interfaces
Comprehensive 3D plotting capabilities and animation support

Use Cases

Visualizing complex multi-dimensional datasets with 3D surfaces and scatter plots
Creating scientific publication figures with specific resolution and font requirements
Building interactive data visualizations and exploratory heatmaps for analysis