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seaborn

Create statistical visualizations from pandas DataFrames using seaborn's plotting functions and objects interface.

Updated Oct 7, 2022
Search Tags:#data visualization#python#pandas#matplotlib#statistical plots#seaborn#heatmap
One-click install
npx skills add https://github.com/tamagusko/linux-cfg --skill seaborn-tamagusko
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seaborn
Source: https://github.com/tamagusko/linux-cfg/tree/main/dotfiles/claude/skills/seaborn
Command: npx skills add https://github.com/tamagusko/linux-cfg --skill seaborn-tamagusko

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires seaborn, matplotlib, pandas, and includes references (resource) components.

What problem does it solve? Choosing the right statistical plot and configuring it correctly in Python requires deep knowledge of matplotlib internals and seaborn's large API surface, slowing down exploratory data analysis and publication figure production. ## Core Features & Use Cases - Full plotting taxonomy: Covers relational, distribution, categorical, regression, and matrix plots (scatterplot, histplot, boxplot, violinplot, heatmap, pairplot, and more) with guidance on when to use each. - Two interfaces documented: Both the traditional function interface and the modern declarative seaborn.objects API, including marks, stats, moves, and scales. - Reference materials: Detailed function signatures, parameter explanations, and ready-to-adapt examples for EDA, publication figures, time series, and large datasets. - Use Case: Given a DataFrame of experimental results, generate a faceted violin plot comparing treatment groups with proper color palettes, error bars, and publication-ready styling exported at 300 DPI. ## Quick Start Use the seaborn skill to create a violin plot comparing response values across treatment groups from my experiment DataFrame.

Frequently Asked Questions about seaborn

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a box plot or violin plot in Python with seaborn?▼

Use sns.boxplot or sns.violinplot with a DataFrame, mapping a categorical variable to x and a continuous variable to y. Add hue for subgroup comparison, split=True for paired violins, and overlay sns.stripplot to show individual observations.

What is the difference between seaborn axes-level and figure-level functions?▼

Axes-level functions like scatterplot and boxplot draw onto a single matplotlib Axes and accept an ax parameter. Figure-level functions like relplot, displot, and catplot manage entire figures with built-in faceting via col and row parameters.

Seaborn vs matplotlib: which should I use for statistical plots?▼

Seaborn is built on matplotlib and provides dataset-oriented plotting with automatic statistical aggregation, error bars, and attractive defaults. Use seaborn for statistical graphics and fall back to matplotlib for fine-grained layout control.

How do I make a correlation heatmap in seaborn?▼

Compute the correlation matrix with df.corr(), then call sns.heatmap with annot=True, fmt='.2f', a diverging colormap like coolwarm, and center=0. Use a boolean mask to hide the redundant upper triangle.

Why is my seaborn KDE plot too smooth or too jagged?▼

The KDE bandwidth controls smoothness. Adjust it with the bw_adjust parameter: values below 1 produce more jagged curves, values above 1 produce smoother curves. The default bandwidth follows Scott's rule.

When should I use the seaborn.objects interface instead of plotting functions?▼

Use seaborn.objects for complex layered visualizations, custom plot types, and programmatic plot generation through composable marks, stats, and scales. The function interface remains better for quick single-purpose exploratory plots.

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