Is it suitable for publication-quality graphics?

Yes, the skill includes instructions for setting professional themes and contexts (like 'paper' or 'talk') specifically for scientific publications and presentations.

Can I create subplots with this skill?

Absolutely. It leverages Seaborn's figure-level functions like FacetGrid and PairGrid to create complex multi-plot layouts automatically based on data categories.

What is the benefit of using Seaborn over Matplotlib?

Seaborn provides a higher-level interface that automates complex tasks like statistical aggregation and legend creation while producing more aesthetically pleasing defaults than standard Matplotlib.

Can Claude help me debug my Seaborn plots?

Yes, the skill provides a library of best practices and anti-patterns to help fix common issues such as overlapping labels, incorrect legends, or performance bottlenecks with large data.

Does this skill support Pandas integration?

Yes, it is specifically designed to work with 'tidy' (long-form) Pandas DataFrames to map variables to plot aesthetics seamlessly.

Seaborn Statistical Visualization

Name: Seaborn Statistical Visualization
Author: tondevrel

bytondevrel

•

Data Science & ML

Generates sophisticated, publication-quality statistical graphics and exploratory data visualizations using the Python Seaborn library.

This skill equips Claude with the expertise to perform advanced exploratory data analysis (EDA) and create informative statistical plots. It provides specific guidance on leveraging Seaborn's high-level interface to visualize complex relationships, distributions, and categorical comparisons directly from Pandas DataFrames. By following best practices for 'tidy' data and choosing between figure-level and axes-level functions, this skill ensures that generated visualization code is clean, efficient, and aesthetically professional for scientific research or business intelligence.

Key Features

01Seamless integration with Pandas DataFrames for 'tidy' long-form data visualization.

02Advanced statistical mapping including automatic confidence interval calculation and data aggregation.

03Automated creation of faceted grids and multi-plot layouts using relplot, displot, and catplot.

04High-level aesthetic control through built-in themes, color palettes, and publication-ready contexts.

051 GitHub stars

06Support for complex matrix plots including annotated heatmaps and hierarchical cluster maps.

Use Cases

01Conducting Exploratory Data Analysis (EDA) to identify correlations and distributions in raw datasets.

02Visualizing linear regression models and their uncertainty for scientific reporting and data modeling.

03Comparing categorical data using specialized plots like violin, box, and swarm plots for statistical summaries.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add tondevrel/scientific-agent-skills seaborn

For use in Claude.ai and ChatGPT

Download Skill