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MichaelYang-lyx
MichaelYang-lyxCOMMUNITY
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category-statistics

Extract category distributions and generate bar, pie, and horizontal charts from datasets.

110|3|Updated Feb 26, 2026
Search Tags:#charts#visualization#pandas#extract#data-cleaning#category-distribution
One-click install
npx skills add https://github.com/MichaelYang-lyx/AIDABench --skill category-statistics-michaelyang-lyx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: category-statistics
Source: https://github.com/MichaelYang-lyx/AIDABench/tree/main/skills/sn-da-excel-workflow/capability/excel-data-statistics/category-statistics
Command: npx skills add https://github.com/MichaelYang-lyx/AIDABench --skill category-statistics-michaelyang-lyx

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill extracts the distribution of a specified category column from a dataset, cleans invalid labels, and computes counts and proportions to enable quick data-driven decisions.

Core Features & Use Cases

  • Category distribution: compute counts and percentages for a target column and generate visual reports (bar, pie, and horizontal charts) for distribution insights.
  • Visualization-ready outputs: produces high-resolution charts suitable for dashboards and reports.
  • Use Case: Analysts evaluating survey results can quickly summarize category frequencies and present clear visuals to stakeholders.

Quick Start

Provide a category distribution summary and visuals for the target column.

Frequently Asked Questions about category-statistics

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

FAQPage Schema
How do I compute category distribution counts and proportions from a dataset in Python?▼

To compute category distribution, this Skill summarizes classification data by extracting target columns, cleaning invalid labels, and calculating count and percentage metrics for each category.

Can I generate bar and pie charts for category distribution visualization using pandas?▼

Yes, you can generate category distribution visualization by rendering high-resolution bar, pie, and horizontal charts from extracted data using Python to produce dashboard-ready visual reports.

What is the best way to clean invalid labels before extracting category distributions?▼

The best way to clean invalid labels for category distribution extraction is applying automated data cleaning logic that filters invalid entries before computing counts and proportions.

Does this category distribution tool work with survey results data for stakeholder reports?▼

Yes, this category distribution tool processes survey results by summarizing category frequencies and generating clear visual outputs suitable for presenting to stakeholders in report formats.

What chart types are available for visualizing category distribution data?▼

Available category distribution chart types include bar charts, pie charts, and horizontal charts, all rendered as high-resolution visual outputs suitable for dashboards and analytical reports.

How do I extract category frequencies from classification data for quick analysis?▼

You extract category frequencies from classification data by specifying a target category column, triggering data cleaning, and computing count and proportion summaries for distribution insights.

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