When should I use Matplotlib instead of Seaborn?

Use Matplotlib when you need fine-grained control over every plot element, need to create novel plot types, or require specific scientific workflows. Seaborn is better for quick, high-level statistical plots.

Does this skill support the Object-Oriented interface?

Yes, it strongly recommends the Object-Oriented interface (fig, ax) for production code as it provides more explicit control and is easier to debug than the state-based pyplot interface.

How does the skill handle overlapping labels and subplots?

It provides guidance on using 'constrained_layout=True' or 'plt.tight_layout()' to automatically adjust subplot parameters and prevent overlapping axis labels or titles.

Can I export plots for research publications?

Absolutely. The skill includes best practices for exporting high-resolution (300 DPI) PNGs and vector formats like PDF and SVG, which are standard for academic journals.

Matplotlib Visualization

Name: Matplotlib Visualization
Author: Sologa

bySologa

Data Science & ML

Generates and customizes professional-grade static, animated, and interactive visualizations using Python's foundational plotting library.

About

Matplotlib Visualization is an expert-level Claude Code Skill designed to help developers and data scientists master Python's most powerful plotting library. This skill provides comprehensive guidance on navigating the Matplotlib hierarchy, from basic line charts to complex 3D visualizations and multi-panel figures. It emphasizes best practices such as the object-oriented API for granular control, accessibility-focused colormap selection, and publication-ready export settings. Whether you are conducting exploratory data analysis or preparing figures for scientific journals, this skill ensures your visualizations are precise, performant, and aesthetically professional.

Key Features

Granular styling control via rcParams and custom style sheets
Advanced layout management using GridSpec and subplot mosaics
Comprehensive support for 3D plotting and complex data annotations
Publication-quality export configurations for PNG, PDF, and SVG formats
Expert implementation of both pyplot and Object-Oriented (OO) interfaces
0 GitHub stars

Use Cases

Generating high-resolution, journal-ready scientific visualizations
Creating exploratory data analysis (EDA) dashboards and reports
Developing custom visualization templates for consistent corporate branding

About

Key Features

Granular styling control via rcParams and custom style sheets
Advanced layout management using GridSpec and subplot mosaics
Comprehensive support for 3D plotting and complex data annotations
Publication-quality export configurations for PNG, PDF, and SVG formats
Expert implementation of both pyplot and Object-Oriented (OO) interfaces
0 GitHub stars

Use Cases

Generating high-resolution, journal-ready scientific visualizations
Creating exploratory data analysis (EDA) dashboards and reports
Developing custom visualization templates for consistent corporate branding