Seaborn

This is your go-to for statistical plots in Python when you're working with pandas DataFrames. It shines at distribution comparisons (violin plots, box plots, pair plots) and regression visualizations with built-in confidence intervals and statistical aggregation. The semantic mapping is the real win here: pass column names and let hue, size, and style parameters handle the visual encoding automatically. Has both a traditional function interface for quick exploration and a newer objects API for declarative plot composition. Built on matplotlib so you get full customization when needed, but the defaults actually look good out of the box. If you need interactivity, reach for plotly instead.

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last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill seaborn --agent claude-code

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Files

Seaborn Statistical Visualization

Overview

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Environment and Installation

Reviewed 2026-10-01 against the current stable Seaborn 0.13.2 documentation and released source. Native synthetic checks used Python 3.13, Seaborn 0.13.2, Matplotlib 3.11.2, pandas 3.0.6, NumPy 2.5.3, SciPy 1.18.1, and statsmodels 0.15.0. Official docs support Python 3.8+ with mandatory NumPy, pandas, and matplotlib dependencies; scipy, statsmodels, and fastcluster are optional for some advanced statistics and clustering workflows. The tested stack emits upstream pandas Copy-on-Write and Matplotlib deprecation warnings; successful current plots do not guarantee compatibility with future pandas 4 or Matplotlib 3.13.

# Reproducible install for examples in this skill
uv pip install "seaborn==0.13.2"

# Include optional statistical dependencies when needed
uv pip install "seaborn[stats]==0.13.2"

SKILL.md on GitHub