Do I need to use Matplotlib alongside this skill?

Seaborn is built on Matplotlib. While this skill focuses on Seaborn's high-level interface, it also includes patterns for Matplotlib integration when fine-grained customization is required.

Can this skill help with complex multi-plot layouts?

Yes, it includes specific patterns for using FacetGrid, PairGrid, and JointGrid to create sophisticated multi-panel figures based on categorical variables.

What is the Seaborn Claude Code Skill?

It is a specialized capability that provides Claude with domain-specific guidance and implementation patterns to generate advanced statistical plots using the Python Seaborn library.

What kind of data structure does this skill work best with?

This skill is optimized for 'long-form' or 'tidy' data structures where each variable is a column and each observation is a row, though it also supports wide-form data for heatmaps.

Seaborn Statistical Visualization

Name: Seaborn Statistical Visualization
Author: lifangda

bylifangda

•

Data Science & ML

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

This skill empowers Claude to create sophisticated, aesthetically pleasing statistical visualizations by leveraging Seaborn's high-level interface. It automates the process of mapping data variables to visual properties, calculating statistical estimations like confidence intervals, and constructing complex multi-panel figures with minimal code. Whether conducting exploratory data analysis or preparing figures for publication, this skill provides the patterns and best practices needed to turn raw DataFrames into insightful, professional-grade charts.

Key Features

01Dataset-oriented plotting specifically designed for pandas DataFrames

02Automatic statistical aggregation and error estimation for line and bar plots

03Declarative objects interface for building complex layered visualizations

04Advanced multivariate plotting including KDE, violin plots, and joint distributions

05Built-in support for multi-panel faceted grids and pairwise relationship matrices

0616 GitHub stars

Use Cases

01Exploratory data analysis to identify trends, correlations, and outliers in complex datasets

02Creating publication-ready graphics with professionally designed color palettes and themes

03Visualizing high-dimensional data through faceted subplots and matrix-based heatmaps

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lifangda/claude-plugins seaborn

For use in Claude.ai and ChatGPT

Download Skill