Python Capstone: Build an Expense Tracker

The expense tracker has four boundaries: user commands, domain rules, storage, and presentation. Keeping them separate prevents input calls and file paths from leaking into every function. The domain layer should work entirely in memory.

The central idea in this lesson is integrated application design. Read the rule, predict a concrete outcome, and then run the smallest example that can confirm or reject the prediction. This separates knowledge from familiarity with syntax.

Represent each expense with an identifier, date, category, description, and positive amount. Store a schema version beside records. Make the repository translate between JSON values and domain objects while reports operate only on validated objects.

from dataclasses import dataclass

@dataclass(frozen=True)
class Expense:
  category: str
  amount: float


def totals_by_category(expenses):
  totals = {}
  for expense in expenses:
      totals[expense.category] = totals.get(expense.category, 0) + expense.amount
  return totals

expenses = [Expense("food", 12.5), Expense("travel", 8), Expense("food", 4.5)]
print(totals_by_category(expenses))

{'food': 17.0, 'travel': 8}

Trace the example before running it. Identify each input, transformation, returned value, and side effect. Then change one boundary value and explain why the new behavior follows from the rule rather than memorizing the output.

Do not begin with a giant menu loop. First prove model creation, totals by category, save and reload, then connect commands. Write a temporary file before replacement and show actionable errors without erasing the last valid data.

Turn the concept into a repeatable workflow: write down the expected behavior, implement one coherent change, run a representative example, and retain a regression check. If the result surprises you, capture the exact input and error before changing anything.

Review the program for names, boundaries, and hidden side effects. A solution is complete when another developer can understand its contract, reproduce its setup, and verify both the successful path and one meaningful failure path.