Create statistical visualizations from tabular datasets with pandas and matplotlib.
npx skills add https://github.com/Clay-HHK/claude-config --skill seaborn-clay-hhk
Please help me install this Agent Skill. Skill: seaborn Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/seaborn Command: npx skills add https://github.com/Clay-HHK/claude-config --skill seaborn-clay-hhk
Seaborn makes it fast and easy to turn tabular datasets into informative statistical visualizations so analysts and researchers can quickly explore distributions, relationships, and categorical comparisons without bespoke plotting code.
Create a scatter plot of total_bill vs tip colored by day using your DataFrame and display it for quick inspection.
High-intent search queries and answers about installing and using this skill.
Create statistical visualizations from pandas DataFrames by using dataset-oriented plotting functions that map DataFrame columns directly to visual variables, producing distribution, relationship, and categorical comparison plots with sensible aesthetic defaults.
The seaborn.objects declarative API provides a composable, programmatic interface for constructing statistical plots by chaining plot building blocks, offering an alternative to traditional axes-level and figure-level functions.
Generate multi-panel figures and faceted comparisons across groups by using figure-level faceting functions that integrate with matplotlib workflows to build structured grids of subplots for multivariate inspection.
Create correlation heatmaps and clustermaps for matrix visualization from tabular datasets using built-in matrix plotting functions that support color palette controls and hierarchical clustering displays.
Seaborn works directly with matplotlib workflows for publication-quality figure generation by providing axes-level functions that return matplotlib axes objects, allowing seamless integration and customization within existing matplotlib figures.
Plot time-series with confidence bands and built-in aggregation by using relational plotting functions that automatically estimate and display error bands around central trend estimates from tabular datasets.
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