seaborn
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.
npx skills add K-Dense-AI/scientific-agent-skills --skill seaborn --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 Current upstream documentation is for seaborn 0.13.2. 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. ```bash # 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" ``` Recommended imports: ```python import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import seaborn.objects as so ``` `sns.load_dataset()` downloads public example data when it is not cached. For private, regulated, or offline work, load local files explicitly with pandas and pass the resulting DataFrame to seaborn. ## Design Philosophy Seaborn follows these core principles: 1. **Datas
- Overview
- Environment and Installation
- Design Philosophy
- Quick Start
- Core Plotting Interfaces
- Function Interface (Traditional)
- Objects Interface (Modern)
- Current API Notes
- Data Structure Requirements
- Long-Form Data (Preferred)
- Wide-Form Data
- Plotting Functions, Grids, Palettes, and Patterns
- Best Practices
- 1. Data Preparation
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"
What does the seaborn skill do?
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.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill seaborn --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.