Data Cleanup Tool

This tool cleans up supermarket sales data to make it easier to analyze and use.

What it does

The script processes the raw sales data and:

1. Removes incomplete records - Deletes rows with missing information
2. Removes duplicates - Keeps only unique sales records
3. Standardizes text formatting - Makes all text consistent:
   • Removes extra spaces from text fields
   • Capitalizes city names properly (e.g., "Yangon")
   • Formats customer types ("Member", "Normal")
   • Formats gender ("Male", "Female")
   • Formats product lines ("Sports And Travel", "Fashion Accessories")
   • Formats payment methods ("Credit Card", "Cash", "Ewallet")
   • Makes branch codes uppercase ("A", "B", "C")
4. Fixes date formats - Converts dates to a standard format

How to use

1. Place your raw sales data in supermarket_sales_dirty.csv
2. Run the cleanup script using uv:

   uv run python cleanup.py

3. The cleaned data will be saved as supermarket_sales_cleaned.csv

Requirements

This script uses uv for Python package management. Make sure you have uv installed:

curl -LsSf https://astral.sh/uv/install.sh | sh

or

# Fedora based
sudo dnf install uv

Dataset used

Supermarket sales by alexhuitron and supermarket_sales_dirty by Claude

Input and Output

Input: supermarket_sales_dirty.csv - Raw sales data with possible errors
Output: supermarket_sales_cleaned.csv - Clean, standardized data ready for analysis