# Seaborn

Statistical visualization with pandas integration.

- Repository: https://github.com/zLanqing/codex-claude-academic-skills (5550 stars)
- Category: Data and ML
- License: BSD-3-Clause license
- Last updated: 2026-05-14
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add zLanqing/codex-claude-academic-skills --skill seaborn -g -a claude-code -y`
Codex: `npx skills add zLanqing/codex-claude-academic-skills --skill seaborn -g -a codex -y`
Cursor: `npx skills add zLanqing/codex-claude-academic-skills --skill seaborn -g -a cursor -y`
Gemini CLI: `npx skills add zLanqing/codex-claude-academic-skills --skill seaborn -g -a gemini-cli -y`

## Description

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:

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

## Quick Start

```python
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()
```

## Core Plotting Interfaces

### Function Interface (Traditional)

The function interface provides specialized plotting functions organized by visualization type. Each category has **axes-level** functions (plot to single axes) and **figure-level** functions (manage entire figure with faceting).

**When to use:**
- Quick exploratory analysis
- Single-purpose visualizations
- When you need a specific plot type

### Objects Interface (Modern)

The `seaborn.objects` interface provides a declarative, composable API similar to ggplot2. Build visualizations by chaining methods to specify data mappings, marks, transformations, and scales.

**When to use:**
- Complex layered visualizations
- When you need fine-grained control over transformations
- Building custom plot types
- Programmatic plot generation

```python
from seaborn import objects as so

The rest: https://github.com/zLanqing/codex-claude-academic-skills/blob/HEAD/scientific-toolkit-skill/references/scientific-skills/seaborn/SKILL.md