Functions and Data Flow

Defining a function creates a callable object; calling it binds arguments to parameters and executes its body. Return sends one object to the caller. A function without an explicit return produces None, which is different from printing a value.

The central idea in this lesson is function contracts. 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.

Use positional arguments for obvious core inputs and keyword arguments when a call becomes clearer. Defaults are evaluated once at function definition, so never use a mutable list or dictionary as a default; use None and create the object inside.

def calculate_total(prices, tax_rate=0.08):
  subtotal = sum(prices)
  return subtotal, subtotal * (1 + tax_rate)


def format_receipt(subtotal, total):
  return f"Subtotal: USD {subtotal:.2f}\nTotal: USD {total:.2f}"

subtotal, total = calculate_total([12.5, 7.25])
print(format_receipt(subtotal, total))

Subtotal: USD 19.75
Total: USD 21.33

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.

Avoid hidden dependence on globals. Pass collaborators and data explicitly. A long parameter list may reveal that related values deserve a dataclass or that the function has more than one responsibility.

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.