seaborn

Seaborn statistical data visualization. Use for statistical plots.

7 stars

byG1Joshi

View on GitHub Installation ↓

Best use case

seaborn is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Seaborn statistical data visualization. Use for statistical plots.

Teams using seaborn should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/seaborn/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/ai-ml/seaborn/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/seaborn/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How seaborn Compares

Feature / Agent	seaborn	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Seaborn statistical data visualization. Use for statistical plots.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Seaborn

Seaborn is a high-level wrapper around Matplotlib. It makes **statistical plots** (violins, heatmaps, pairs) easy.

## When to Use

- **Exploratory Data Analysis (EDA)**: Quickly understanding distributions.
- **Statistical Relationships**: "Show me the regression line with confidence intervals".
- **Pandas Models**: Works natively with DataFrames (long-form).

## Core Concepts

### Objects Interface (`so`)

New in v0.12+. A grammar-of-graphics style API (like ggplot2) : `so.Plot(df, x="time", y="val").add(so.Line())`.

### Themes

`sns.set_theme()`.

## Best Practices (2025)

**Do**:

- **Use the Objects Interface**: For composable, complex plots.
- **Use `relplot`, `displot`, `catplot`**: The figure-level functions are more flexible than `scatterplot`.

**Don't**:

- **Don't iterate**: Seaborn handles "hue" and "col" (faceting) automatically.

## References

- [Seaborn Documentation](https://seaborn.pydata.org/)