data-comparison/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

This is a Python data comparison tool built with Python 3.13+. The project is currently in early development with a minimal structure containing:

• A basic Python application entry point (main.py)
• Sample data in Excel format (data/sample-data.xlsx)
• Standard Python packaging configuration (pyproject.toml)

Development Commands

Running the Application

uv run main.py

This launches a web-based GUI at http://localhost:8080

Running Analysis Only (Command Line)

uv run data_comparator.py

Project Setup

The project uses Python 3.13+ with uv for dependency management. Dependencies include:

• pandas (Excel file processing)
• openpyxl (Excel file reading)
• flask (Web GUI)

Project Structure

• main.py - Main application entry point that launches the web GUI
• data_comparator.py - Core comparison logic for KST vs Coordi data analysis
• web_gui.py - Flask-based web GUI application
• analyze_excel.py - Basic Excel file structure analysis utility
• data/ - Directory containing sample data files
  • sample-data.xlsx - Sample Excel data file for comparison operations
• templates/ - HTML templates for web GUI (auto-generated)
• pyproject.toml - Python project configuration and metadata

Key Features

• KST vs Coordi Comparison: Compares data between KST columns (Title KR, Epi.) and Coordi columns (KR title, Chap)
• Mismatch Categorization: Identifies KST-only, Coordi-only, and duplicate items
• Data Reconciliation: Ensures matching counts after excluding mismatches
• Web-based GUI: Interactive interface with tabs for different data views
• File Upload: Upload Excel files directly through the web interface
• Sheet Filtering: Filter results by specific Excel sheets
• Real-time Analysis: Live comparison with detailed mismatch reasons

Comparison Logic

The tool compares Excel data by:

1. Finding columns by header names (not positions)
2. Extracting title+episode combinations from both datasets
3. Categorizing mismatches and calculating reconciliation
4. Displaying results with reasons for each discrepancy