How does it handle Pandas DataFrames?

The skill is natively dataset-oriented, allowing you to map DataFrame columns directly to visual properties like hue, size, style, and facets.

Does this skill support the modern Seaborn Objects interface?

Yes, it includes detailed guidance for both the traditional function-level interface and the newer, declarative seaborn.objects API.

Can I use this for publication-quality figures?

Absolutely. Seaborn is designed specifically for creating publication-ready graphics with built-in aesthetic themes and color-blind friendly palettes.

Does it support complex multi-plot grids?

Yes, it provide patterns for using FacetGrid, PairGrid, and JointGrid to create sophisticated multi-panel faceted visualizations.

Can I customize the plots using Matplotlib?

Yes, because Seaborn is built on top of Matplotlib, this skill provides guidance on using axes-level functions for deep customization.

Seaborn Statistical Visualization

Name: Seaborn Statistical Visualization
Author: Microck

byMicrock

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데이터 과학 및 ML

Simplifies the creation of complex, publication-quality statistical graphics and data visualizations in Python.

소개

This Claude Code skill provides comprehensive guidance for using Seaborn, a powerful Python library built on Matplotlib for dataset-oriented plotting. It enables users to perform multivariate analysis, automatic statistical estimation, and create intricate multi-panel figures with minimal code. By leveraging aesthetic defaults and built-in statistical awareness, it helps data scientists and developers translate complex DataFrames into clear, visual insights through a wide array of plot types, including heatmaps, regressions, and distribution charts.

주요 기능

Dataset-oriented plotting using named variables and DataFrames
Built-in statistical aggregation and error estimation
Publication-ready themes and color palettes out of the box
Declarative 'Objects' interface for composable, layered visualizations
Automated faceting for creating complex multi-panel figures
81 GitHub stars

사용 사례

Visualizing correlations and multivariate relationships in large datasets
Exploratory data analysis for identifying trends and distributions
Generating publication-quality statistical figures for reports

소개

주요 기능

Dataset-oriented plotting using named variables and DataFrames
Built-in statistical aggregation and error estimation
Publication-ready themes and color palettes out of the box
Declarative 'Objects' interface for composable, layered visualizations
Automated faceting for creating complex multi-panel figures
81 GitHub stars

사용 사례

Visualizing correlations and multivariate relationships in large datasets
Exploratory data analysis for identifying trends and distributions
Generating publication-quality statistical figures for reports