Can I create multi-panel figures with this skill?

Yes, it covers figure-level functions and grid objects like FacetGrid and PairGrid for creating complex, multi-plot layouts easily.

What is the advantage of using the Seaborn skill over Matplotlib?

Seaborn provides a higher-level interface that produces more aesthetically pleasing plots with less code, specifically optimized for statistical data and DataFrames.

Does this skill support the modern seaborn.objects interface?

Yes, it includes comprehensive guidance for the modern, declarative, and composable API similar to ggplot2.

How does the skill handle different data formats?

The skill provides patterns for working with both 'long-form' (tidy) and 'wide-form' data, including instructions on when to use each for specific plot types.

Seaborn Statistical Visualization

Name: Seaborn Statistical Visualization
Author: K-Dense-AI

byK-Dense-AI

•

2,188

Ciencia de Datos y ML

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

Acerca de

This skill empowers Claude to create sophisticated statistical visualizations by leveraging the Seaborn library's dataset-oriented API. It provides structured guidance for generating relational, distribution, categorical, and regression plots with aesthetic defaults and automatic statistical estimation. Whether you need quick exploratory analysis or complex multi-panel figures for publication, this skill helps Claude select the correct plotting interfaces and ensures data is structured correctly for optimal visualization results.

Características Principales

Automatic semantic mapping of data variables to visual properties like hue and size
Support for both traditional function-based and modern declarative object-oriented interfaces
Advanced multi-panel plotting via FacetGrid, PairGrid, and JointGrid
2,188 GitHub stars
Integrated handling of both long-form (tidy) and wide-form data structures
Statistical awareness with built-in aggregation and error estimation

Casos de Uso

Creating publication-ready correlation matrices and heatmaps for exploratory data analysis
Comparing statistical distributions across categories with violin, box, and swarm plots
Visualizing multivariate relationships across datasets using faceted scatter and line plots

Acerca de

Características Principales

Automatic semantic mapping of data variables to visual properties like hue and size
Support for both traditional function-based and modern declarative object-oriented interfaces
Advanced multi-panel plotting via FacetGrid, PairGrid, and JointGrid
2,188 GitHub stars
Integrated handling of both long-form (tidy) and wide-form data structures
Statistical awareness with built-in aggregation and error estimation

Casos de Uso

Creating publication-ready correlation matrices and heatmaps for exploratory data analysis
Comparing statistical distributions across categories with violin, box, and swarm plots
Visualizing multivariate relationships across datasets using faceted scatter and line plots