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Sir-chawakorn
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data-analysis

Load CSV, JSON, or Excel files for cleaning, aggregation, and chart generation with pandas, matplotlib, seaborn, and Chart.js.

33|5|Updated Jan 14, 2026
Search Tags:#data analysis#visualization#pandas#data cleaning#matplotlib#seaborn#chart.js
One-click install
npx skills add https://github.com/Sir-chawakorn/power-ranger-toolkit --skill data-analysis-sir-chawakorn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/Sir-chawakorn/power-ranger-toolkit/tree/main/src/skills/data-analysis
Command: npx skills add https://github.com/Sir-chawakorn/power-ranger-toolkit --skill data-analysis-sir-chawakorn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill streamlines the process of analyzing and visualizing data, enabling users to quickly gain insights and make data-driven decisions without complex coding.

Core Features & Use Cases

  • Data Loading & Cleaning: Easily load data from CSV, JSON, or Excel files and handle missing values or type conversions.
  • Data Aggregation & Transformation: Perform group-by operations, create pivot tables, and calculate statistics.
  • Visualization: Generate various charts like line plots and heatmaps using Matplotlib and Seaborn, or create interactive charts with Chart.js.
  • Use Case: Analyze monthly sales data to identify top-performing products and visualize trends over time.

Quick Start

Use the data-analysis skill to load 'sales_data.csv', calculate the total sales per category, and generate a bar chart.

Frequently Asked Questions about data-analysis

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

FAQPage Schema
How do I load and clean CSV or Excel data for analysis?▼

To create data visualizations, generate line plots, heatmaps, and bar charts using Python libraries like Matplotlib and Seaborn. You can also build interactive charts with Chart.js, allowing you to easily visualize trends and extract actionable insights from your processed datasets.

Can I use this to aggregate data and calculate statistics from a JSON file?▼

This Skill supports data exploration, cleaning, and visualization for structured and semi-structured datasets. It is best suited for analyzing formats like CSV, JSON, and Excel files to identify trends, such as finding top-performing products in monthly sales data, rather than handling unstructured text.

What's the best way to visualize monthly sales trends from CSV data?▼

The best way to visualize sales trends is to load your CSV file, calculate total sales per category using pandas, and generate a bar chart. This workflow extracts actionable insights by transforming raw transactional data into clear visual representations of product performance over time.

Does this approach require Python libraries for data visualization?▼

Yes, generating charts requires Python libraries such as Pandas, Matplotlib, and Seaborn for static visualizations. If you need interactive charts, the Skill also supports using JavaScript with Chart.js, providing code snippets to integrate these visualization patterns directly into your workflow.

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