Does this skill support the Matplotlib Object-Oriented interface?

Yes, it specifically recommends and prioritizes the Object-Oriented API (using fig and ax) for production-grade, maintainable visualization code over the stateful pyplot interface.

Is this skill compatible with NumPy and Pandas?

Yes, Matplotlib is the core engine for many Python data tools; this skill covers seamless integration with NumPy arrays and plotting directly from Pandas DataFrames.

Can I create 3D plots with this skill?

Absolutely. The skill includes guidance and patterns for using mplot3d to generate 3D surface plots, 3D scatter plots, and wireframes.

Does it help with accessibility and colorblindness?

Yes, the skill emphasizes best practices like using perceptually uniform and colorblind-friendly colormaps (e.g., viridis) and adding textures to categorical charts.

How do I ensure my plots are suitable for print publications?

The skill provides specific export configurations for high-resolution PNGs (300 DPI) and scalable vector formats like PDF and SVG, using settings like bbox_inches='tight'.

Matplotlib Data Visualization

Name: Matplotlib Data Visualization
Author: x-cmd

byx-cmd

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Ciencia de Datos y ML

Generates publication-quality static, animated, and interactive visualizations using Python's foundational plotting library.

Acerca de

This skill equips Claude with expert knowledge of the Matplotlib ecosystem, enabling the creation of professional-grade scientific and statistical visualizations. It provides comprehensive guidance on choosing between the simplified pyplot interface and the robust object-oriented API for complex multi-panel layouts. Users can leverage this skill to implement everything from basic line and scatter plots to advanced heatmaps, 3D surfaces, and custom-styled charts, ensuring all outputs follow best practices for accessibility, layout management, and high-resolution export.

Características Principales

Expert implementation of both Pyplot and Object-Oriented interfaces
High-resolution export workflows for PNG, PDF, and SVG formats
Advanced layout management using subplots, Mosaic, and GridSpec
Publication-quality styling with custom themes and colorblind-friendly colormaps
Support for diverse plot types including line, scatter, bar, and heatmaps
8 GitHub stars

Casos de Uso

Performing exploratory data analysis with statistical visualizations
Building automated reporting dashboards with custom branding and styling
Generating scientific plots for research papers and technical documentation

Acerca de

Características Principales

Expert implementation of both Pyplot and Object-Oriented interfaces
High-resolution export workflows for PNG, PDF, and SVG formats
Advanced layout management using subplots, Mosaic, and GridSpec
Publication-quality styling with custom themes and colorblind-friendly colormaps
Support for diverse plot types including line, scatter, bar, and heatmaps
8 GitHub stars

Casos de Uso

Performing exploratory data analysis with statistical visualizations
Building automated reporting dashboards with custom branding and styling
Generating scientific plots for research papers and technical documentation