Lesson 25: Capstone — Build a CLI Expense Tracker

The Capstone Project

You have learned variables, control flow, data structures, functions, OOP, files, errors, and JSON. Now we combine all of it into a real, usable program: a CLI Expense Tracker. This is the moment you stop being a beginner.

This project mirrors how real software is built: a small set of focused functions, persistence to disk, validation of user input, and a menu loop. If you can build this from memory, you are ready to start building your own projects.

What You'll Learn in This Lesson

• Design a small program's architecture before writing code
• Persist data to JSON so it survives restarts
• Validate user input with try/except
• Structure a menu-driven CLI application

Features We Build

Architecture — Think Before You Code

The whole program is a small set of focused functions:

Each function does one job — the golden rule of function design (Lesson 12).

Mental model: map each menu option to the function it calls. The menu is the front door; the functions are the rooms behind it. main() stays thin — it only routes.

Building It Step by Step

Step 1 — Load: if expenses.json doesn't exist, start empty. Otherwise json.load it.

def load_expenses():
    if not os.path.exists(DATA_FILE):
        return []
    with open(DATA_FILE, "r", encoding="utf-8") as f:
        return json.load(f)

Step 2 — Save: dump the list with indent=2 for readable JSON.

Step 3 — Add with validation: reject non-positive amounts by raising ValueError.

Step 4 — Summary: a dict of totals using .get() — exactly the pattern from Lesson 9:

by_category = {}
for e in expenses:
    by_category[e["category"]] = by_category.get(e["category"], 0) + e["amount"]

Step 5 — The menu loop: while True + break from Lesson 5, with try/except around input:

while True:
    print("\n1. Add expense  2. Summary  3. Exit")
    choice = input("Choose: ")
    if choice == "1":
        try:
            amount = float(input("Amount: "))
            category = input("Category: ").strip()
            add_expense(expenses, amount, category)
            save_expenses(expenses)
        except ValueError as e:
            print("Invalid input:", e)
    elif choice == "2":
        print_summary(expenses)
    elif choice == "3":
        print("Goodbye!")
        break
    else:
        print("Unknown choice")

How This Course Prepared You

Every line of this project uses a lesson you already completed:

• while True + break → Lesson 5
• Dicts + .get() → Lesson 9
• Functions → Lesson 12
• try/except + raise → Lesson 21
• JSON + files → lessons 20 & 23
• The __name__ guard → Lesson 15

That is the point: professional Python is just these building blocks arranged well.

Testing Your Capstone

Apply Lesson 24's habits to the project:

def test_add_expense():
    expenses = []
    add_expense(expenses, 250, "food")
    assert expenses == [{"amount": 250, "category": "food"}]

def test_add_rejects_zero():
    expenses = []
    try:
        add_expense(expenses, 0, "food")
    except ValueError:
        pass
    else:
        raise AssertionError("Expected ValueError for amount 0")

Extension Ideas

Common Mistakes to Avoid

• Mistake: One giant main() with everything inline — Fix: split into focused functions.
• Mistake: Letting bad input crash the menu — Fix: wrap input parsing in try/except.
• Mistake: Forgetting to save after adding — Fix: call save_expenses() after every mutation.
• Mistake: float(input(...)) on empty input crashing — Fix: validate and handle ValueError.
• Mistake: Reading the JSON file before it exists — Fix: check os.path.exists first (or catch FileNotFoundError).

Professional Tips & Tricks

• Keep main() thin: it only routes menu choices to functions — each function does one job.
• The try/except around input makes the program survive bad data — test it with 'abc'.
• Extend the capstone with CSV export and a delete option to practice everything again.
• Write a few pytest tests for add_expense — tests make the project feel professional.
• Use __name__ == "__main__" so the module is also importable.

Key Takeaways

• Design the function list before writing code.
• JSON gives your program memory across runs.
• Validate all user input with try/except.
• One function = one job; main() only routes.
• Test the core functions with pytest.
• You are no longer a beginner. 🎉

Next steps: Review the syllabus, redo any lesson, or explore the Applied Data Science course in the Learning Hub.