Seaborn

If you're doing exploratory data analysis in Python, this is the library that makes statistical plots actually look good without fighting matplotlib for hours. It shines when you need to visualize distributions, relationships, and categorical comparisons across your dataset. The semantic mapping is the real win here: you pass a DataFrame and specify variables by name, and hue, size, and style encodings just work. The split between the traditional function interface and the newer objects API (inspired by ggplot2) gives you both quick plotting and compositional control when you need it. Pair plots, correlation heatmaps, and violin plots with proper confidence intervals come standard. It's opinionated about aesthetics, which is exactly what you want when stakeholders expect publication quality figures.

skills.sh installs: 565
GitHub stars: 33.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add davila7/claude-code-templates --skill seaborn --agent claude-code

Installs into .claude/skills of the current project.

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.

Design Philosophy

Seaborn follows these core principles:

Dataset-oriented: Work directly with DataFrames and named variables rather than abstract coordinates
Semantic mapping: Automatically translate data values into visual properties (colors, sizes, styles)
Statistical awareness: Built-in aggregation, error estimation, and confidence intervals
Aesthetic defaults: Publication-ready themes and color palettes out of the box
Matplotlib integration: Full compatibility with matplotlib customization when needed

Quick Start

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Load example dataset
df = sns.load_dataset('tips')

# Create a simple visualization
sns.scatterplot(data=df, x='total_bill', y='tip', hue='day')
plt.show()

Seaborn

Install

Files

Seaborn Statistical Visualization

Overview

Design Philosophy

Quick Start

Core Plotting Interfaces

Function Interface (Traditional)

Details

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