How does this relate to Matplotlib?

Seaborn is built on top of Matplotlib. This skill helps you use Seaborn's high-level API while maintaining the ability to use Matplotlib for fine-grained axis and figure customization.

Can I create multi-panel figures with this skill?

Absolutely. The skill includes patterns for using figure-level functions like relplot, displot, and catplot, as well as complex grids like FacetGrid and PairGrid.

What is the Seaborn Claude Code skill?

It is a specialized capability that enables Claude to generate, debug, and optimize statistical visualizations using the Seaborn Python library, providing domain-specific patterns for data analysis.

Does it support custom color palettes?

Yes, it provides guidance on using Seaborn's built-in qualitative, sequential, and diverging palettes, as well as creating custom color mappings for accessibility.

Does this skill work with Pandas DataFrames?

Yes, it is specifically designed for dataset-oriented plotting and works best with 'tidy' or long-form Pandas DataFrames.

Seaborn Statistical Visualization

Name: Seaborn Statistical Visualization
Author: Activer007

byActiver007

데이터 과학 및 ML

Generates publication-quality statistical graphics and complex data visualizations using the Seaborn Python library.

소개

This Claude Code skill provides specialized guidance for creating sophisticated, dataset-oriented visualizations with minimal code. By leveraging Seaborn's high-level interface, it enables Claude to automate statistical estimation, map data variables to visual semantics, and construct complex multi-panel figures. It is an essential tool for exploratory data analysis, multivariate relationship discovery, and producing aesthetically pleasing, publication-ready charts for scientific research and data science projects.

주요 기능

Built-in aesthetic themes and color-blind friendly palettes
Automatic statistical aggregation and confidence interval calculation
0 GitHub stars
Comprehensive support for distribution, categorical, and regression plots
Dataset-oriented plotting integrated with Pandas DataFrames
Advanced multi-plot grid systems including FacetGrid and PairGrid

사용 사례

Visualizing correlations and matrix-based data via heatmaps and cluster maps
Creating professional graphics for research papers and data science presentations
Performing exploratory data analysis on multi-dimensional scientific datasets

소개

주요 기능

Built-in aesthetic themes and color-blind friendly palettes
Automatic statistical aggregation and confidence interval calculation
0 GitHub stars
Comprehensive support for distribution, categorical, and regression plots
Dataset-oriented plotting integrated with Pandas DataFrames
Advanced multi-plot grid systems including FacetGrid and PairGrid

사용 사례

Visualizing correlations and matrix-based data via heatmaps and cluster maps
Creating professional graphics for research papers and data science presentations
Performing exploratory data analysis on multi-dimensional scientific datasets