Is it better than using standard Matplotlib?

Seaborn provides a higher-level, more concise API for statistical graphics and better default aesthetics, though it remains fully compatible with Matplotlib for fine-grained customization.

Does this skill work with Pandas DataFrames?

Yes, Seaborn is natively designed to work with long-form and wide-form Pandas DataFrames, and this skill provides specific guidance on mapping DataFrame columns to visual properties.

What is the Seaborn Claude Code Skill?

It is a specialized skill that enhances Claude's ability to write, debug, and optimize Python code using the Seaborn library for statistical data visualization.

Can I use this for complex multi-plot figures?

Absolutely. The skill includes patterns for figure-level functions like FacetGrid, PairGrid, and JointGrid to create sophisticated multi-panel layouts.

How does this help with statistical analysis?

This skill provides the syntax for built-in statistical features such as linear regression fits, kernel density estimation (KDE), and confidence interval calculation.

Seaborn Statistical Visualization

Name: Seaborn Statistical Visualization
Author: BbgnsurfTech

byBbgnsurfTech

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データサイエンスとML

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

概要

This skill provides specialized guidance for using Seaborn, a powerful Python library built on Matplotlib for high-level statistical data visualization. It enables users to generate sophisticated plots directly from Pandas DataFrames, covering everything from simple scatter plots to complex multi-panel faceted grids and hierarchical clustering. By leveraging this skill, developers can implement best practices for dataset-oriented plotting, semantic mapping, and automated statistical estimation to produce professional, exploratory, or publication-ready figures with minimal code complexity.

主な機能

3 GitHub stars
Declarative objects interface for modern, composable visualization design
High-level interfaces for relational, categorical, and distribution plots
Dataset-oriented API for seamless integration with Pandas DataFrames
Automated statistical estimation including confidence intervals and aggregation
Built-in aesthetic themes and color-blind friendly palettes for publication

ユースケース

Generating complex statistical figures like violin plots and heatmaps for reports
Performing exploratory data analysis (EDA) to uncover multivariate relationships
Creating faceted multi-panel visualizations for comparing data across categories

概要

主な機能

3 GitHub stars
Declarative objects interface for modern, composable visualization design
High-level interfaces for relational, categorical, and distribution plots
Dataset-oriented API for seamless integration with Pandas DataFrames
Automated statistical estimation including confidence intervals and aggregation
Built-in aesthetic themes and color-blind friendly palettes for publication

ユースケース

Generating complex statistical figures like violin plots and heatmaps for reports
Performing exploratory data analysis (EDA) to uncover multivariate relationships
Creating faceted multi-panel visualizations for comparing data across categories