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seaborn

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

Updated Aug 12, 2026
Search Tags:#data visualization#python#pandas#matplotlib#statistical plots#seaborn#heatmap
One-click install
npx skills add https://github.com/littlt-momo-c-yfc/skills --skill seaborn-littlt-momo-c-yfc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seaborn
Source: https://github.com/littlt-momo-c-yfc/skills/tree/main/skills/scientific-toolkit-skill/references/scientific-skills/seaborn
Command: npx skills add https://github.com/littlt-momo-c-yfc/skills --skill seaborn-littlt-momo-c-yfc

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Choosing the right statistical plot and writing correct seaborn code for distributions, categorical comparisons, regressions, and heatmaps is time-consuming, especially when deciding between axes-level and figure-level functions or the modern objects interface. ## Core Features & Use Cases - Full plotting coverage: Guidance for relational, distribution, categorical, regression, and matrix plots including scatterplot, histplot, violinplot, regplot, heatmap, and clustermap. - Multi-panel figures: Patterns for FacetGrid, PairGrid, and JointGrid plus matplotlib subplot integration for publication-quality multi-panel figures. - Modern objects interface: Declarative composition with seaborn.objects using marks, stats, moves, and scales for layered, programmatic plot generation. - Use Case: A researcher needs a faceted violin plot comparing treatment groups with significance annotations and a correlation heatmap for a paper; this Skill provides the exact code patterns, theming, and export settings. ## Quick Start Use the seaborn skill to create a violin plot comparing response across treatment groups with individual data points overlaid and save it as a 300 DPI PDF.

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 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 heatmap plot 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 and return grid objects.

Seaborn vs matplotlib: when should I use each?▼

Seaborn is best for statistical plots with DataFrames, automatic aggregation, and confidence intervals with minimal code. Use matplotlib directly for fine-grained layout control, and combine both since seaborn axes integrate into matplotlib figures.

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', cmap='coolwarm', and center=0. Use numpy triu masking to hide the redundant upper triangle and square=True for equal cell proportions.

Why does my seaborn KDE plot look too smooth or too jagged?▼

The KDE bandwidth controls smoothness and defaults may not fit your data scale. Adjust it with the bw_adjust parameter: values below 1 produce more detail, values above 1 produce smoother curves.

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

Use seaborn.objects for complex layered visualizations, programmatic plot generation, and custom compositions of marks, stats, and scales. The traditional function interface remains simpler for quick single-purpose exploratory plots.

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