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

•

데이터 과학 및 ML

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

소개

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.

주요 기능

Dataset-oriented plotting specifically designed for pandas DataFrames
Automatic statistical aggregation and error estimation for line and bar plots
Declarative objects interface for building complex layered visualizations
Advanced multivariate plotting including KDE, violin plots, and joint distributions
Built-in support for multi-panel faceted grids and pairwise relationship matrices
16 GitHub stars

사용 사례

Exploratory data analysis to identify trends, correlations, and outliers in complex datasets
Creating publication-ready graphics with professionally designed color palettes and themes
Visualizing high-dimensional data through faceted subplots and matrix-based heatmaps

소개

주요 기능

Dataset-oriented plotting specifically designed for pandas DataFrames
Automatic statistical aggregation and error estimation for line and bar plots
Declarative objects interface for building complex layered visualizations
Advanced multivariate plotting including KDE, violin plots, and joint distributions
Built-in support for multi-panel faceted grids and pairwise relationship matrices
16 GitHub stars

사용 사례

Exploratory data analysis to identify trends, correlations, and outliers in complex datasets
Creating publication-ready graphics with professionally designed color palettes and themes
Visualizing high-dimensional data through faceted subplots and matrix-based heatmaps