Receipt Parser & Spending Analyzer

A Python tool for parsing grocery receipt data and generating spending analytics.

Overview

This project transforms unstructured receipt data into actionable spending insights:

• Parse receipt transactions into structured data
• Categorize items automatically
• Generate multi-dimensional spending analysis
• Export to Excel with formatted tables and dashboards

Features

Data Parsing (parse_receipts.py)

• Extracts transaction metadata (date, time, store, totals)
• Parses line items with prices and tax flags
• Computes derivative metrics:
  • Day of week, time of day patterns
  • Pre-discount totals and savings percentages
  • Effective tax rates
  • Self-checkout detection

Item Categorization

Automatically categorizes grocery items into:

• Dairy, Meat/Protein, Fruit, Vegetables
• Cereal, Snacks, Beverages, Bakery
• Frozen, Candy/Sweets, Eggs
• And more...

Analytics Generated

Analysis	Description
By Store	Spending and visits per location
By Month	Monthly spending trends
By Day of Week	Which days you shop most
By Time of Day	Morning/Afternoon/Evening patterns
By Category	What you spend the most on
Top Items	Most frequently purchased items
Savings Tracking	Discount utilization

Excel Export (create_excel.py)

Creates a formatted workbook with:

• Transactions sheet - All receipt summaries with formatting
• Items sheet - Line-item detail with categories
• Dashboard - Summary metrics and instructions

Data Structure

Receipt transactions follow this structure:

{
    'receipt_id': 'XXXX XXX XXX XXX',
    'date': '01/15/2026',
    'time': '14:30',
    'store_name': 'Store Name',
    'store_address': '123 Main St',
    'city': 'City',
    'state': 'FL',
    'zip': '12345',
    'subtotal': 45.00,
    'sales_tax': 2.50,
    'grand_total': 47.50,
    'savings': 10.00,
    'payment_method': 'Credit Card',
    'items': [
        ('ITEM NAME', 4.99, 'F'),   # F = Food stamp eligible
        ('TAXABLE ITEM', 3.99, 'TF'), # T = Taxable, F = Food
        ('Promotion', -2.00, 'F'),   # Negative = discount
    ]
}

Setup

Requirements

openpyxl

Installation

pip install openpyxl

Configuration

1. Copy config_example.py to config.py
2. Update paths to match your data directory

Usage

# Parse receipts and generate CSVs
python parse_receipts.py

# Create Excel workbook
python create_excel.py

Output Files

File	Description
transactions.csv	Summary of each receipt
items.csv	Line items with categories
summary.csv	Aggregated statistics
spending_database.xlsx	Formatted Excel workbook

Project Structure

receipt-parser/
├── README.md
├── config_example.py      # Template configuration
├── parse_receipts.py      # Receipt parsing and analytics
├── create_excel.py        # Excel workbook generator
├── requirements.txt       # Dependencies
└── .gitignore

Tech Stack

• Python 3.x
• openpyxl - Excel file generation with formatting
• csv - Standard library for CSV handling
• datetime - Date parsing and manipulation
• collections.defaultdict - Aggregation helpers

Sample Output

The analytics reveal patterns like:

• Which stores you frequent most
• What time of day you typically shop
• Your most purchased items
• How much you save with promotions
• Spending by category breakdown

Extending

To add your own receipt data:

1. Structure transactions in the format shown above
2. Add to the transactions list in parse_receipts.py
3. Run both scripts to regenerate analytics

License

Personal project for spending analysis.