Build an expenses categorizer

Here is a basic Expenses Categorizer in Python that takes a list of transactions and classifies them into predefined categories based on keywords:

python
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import re

# Sample categories and associated keywords
CATEGORIES = {
    "Groceries": ["walmart", "aldi", "grocery", "supermarket", "whole foods", "costco"],
    "Utilities": ["electric", "water", "gas bill", "internet", "utility"],
    "Rent": ["rent", "landlord", "apartment"],
    "Transportation": ["uber", "lyft", "gas", "fuel", "bus", "metro", "train"],
    "Entertainment": ["netflix", "spotify", "movie", "cinema", "concert"],
    "Dining": ["restaurant", "cafe", "mcdonalds", "starbucks", "food", "dining"],
    "Healthcare": ["pharmacy", "doctor", "hospital", "medicine"],
    "Shopping": ["amazon", "ebay", "mall", "store", "clothing", "fashion"],
    "Other": []
}

def categorize_expense(description):
    """Categorize a single expense based on description."""
    description = description.lower()
    for category, keywords in CATEGORIES.items():
        for keyword in keywords:
            if keyword in description:
                return category
    return "Other"

def categorize_expenses(expenses):
    """
    Categorize a list of expense transactions.
    Each transaction is a tuple: (description, amount)
    """
    categorized = []
    for desc, amount in expenses:
        category = categorize_expense(desc)
        categorized.append((desc, amount, category))
    return categorized

def print_report(categorized_expenses):
    """Prints a summary report of categorized expenses."""
    report = {}
    for _, amount, category in categorized_expenses:
        report[category] = report.get(category, 0) + amount

    print("n--- Expense Report ---")
    for category, total in sorted(report.items(), key=lambda x: x[1], reverse=True):
        print(f"{category}: ${total:.2f}")

# Sample usage
if __name__ == "__main__":
    sample_expenses = [
        ("Walmart Grocery", 120.50),
        ("Netflix Subscription", 15.99),
        ("Uber Ride", 23.75),
        ("Gas Station", 45.00),
        ("McDonalds Lunch", 8.99),
        ("Monthly Rent", 1200.00),
        ("Spotify Premium", 9.99),
        ("Electricity Bill", 60.00),
        ("Amazon Purchase", 130.40),
        ("Doctor Visit", 85.00)
    ]

    categorized = categorize_expenses(sample_expenses)
    print_report(categorized)

Features:

• Matches keywords in descriptions to predefined categories.

• Flexible for custom keyword or category updates.

• Outputs a simple categorized summary report.

Let me know if you want a web-based or Excel-integrated version of this tool.