Can I create complex layouts with multiple subplots?

Absolutely. The skill covers various layout methods including standard subplots, subplot_mosaic for flexible grids, and GridSpec for maximum control.

Does the skill help with visualization accessibility?

Yes, it provides guidance on using colorblind-friendly colormaps like viridis and cividis, as well as adding patterns and high-contrast labels.

Is it compatible with data science libraries like Pandas?

Yes, it is designed to integrate seamlessly with NumPy and Pandas for direct plotting from arrays and DataFrames.

How do I ensure my plots are publication-ready?

The skill includes best practices for high-DPI exports, vector formats like PDF/SVG, and specific parameter settings like bbox_inches='tight'.

Does this skill support the Object-Oriented interface?

Yes, it specifically recommends and provides guidance for the Object-Oriented API (Figure/Axes) for better control and maintainability.

Matplotlib Visualization

Name: Matplotlib Visualization
Author: ovachiever

byovachiever

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Data Science & ML

Generates professional-grade scientific plots and data visualizations using Python's foundational plotting library.

About

The Matplotlib skill empowers developers and data scientists to create high-quality, publication-ready visualizations within the Claude Code environment. It provides expert guidance on navigating the complex Matplotlib hierarchy, choosing between the pyplot and object-oriented interfaces, and implementing best practices for layout, styling, and accessibility. Whether you are performing exploratory data analysis or building complex multi-panel figures for scientific papers, this skill streamlines the process from initial data mapping to final export in high-resolution formats like PNG, PDF, and SVG.

Key Features

Expert guidance for both Pyplot and Object-Oriented interfaces
Customizable styling with rcParams, colormaps, and colorblind-friendly palettes
Optimized export settings for high-resolution and vector-based publication figures
Automated creation of diverse plot types including line, scatter, bar, and heatmaps
Advanced layout management using Subplots, Mosaic, and GridSpec
5 GitHub stars

Use Cases

Generating publication-quality figures for scientific research and technical reports
Automating the generation of data-driven dashboards and performance metrics
Exploratory data analysis using rapid-prototype charts and histograms

About

Key Features

Expert guidance for both Pyplot and Object-Oriented interfaces
Customizable styling with rcParams, colormaps, and colorblind-friendly palettes
Optimized export settings for high-resolution and vector-based publication figures
Automated creation of diverse plot types including line, scatter, bar, and heatmaps
Advanced layout management using Subplots, Mosaic, and GridSpec
5 GitHub stars

Use Cases

Generating publication-quality figures for scientific research and technical reports
Automating the generation of data-driven dashboards and performance metrics
Exploratory data analysis using rapid-prototype charts and histograms