Guard clauses before brittle arithmetic

Beginner Defensive APIs: validation, sanitization & exceptions

Created by Pavel · 29.04.2026 at 19:10 UTC

Division, logarithms, and normalisations explode on zero or negative values in the wrong domain. Guard clauses at the top of a function (if denom == 0: raise ValueError) document preconditions and avoid try/except around expected business violations.

In notebooks, silent inf and nan from bad divides propagate into metrics—guarding early keeps plots trustworthy.

This pairs with numpy conventions: np.where masks, divide with where=, or explicit checks before log.

Floating-point exceptions in NumPy: [1].

Sources

[1]https://numpy.org/doc/stable/reference/generated/numpy.seterr.html Return to text

University approvals: 0

Tasks

Question 1

Best practice before ratio = hits / total in a reporting API when total may be zero:

Hint

Explicit policy beats accidental inf.

Rely on ZeroDivisionError in production logs only

Validate total and fail with a clear error or explicit sentinel policy

Cast to float—divides by zero become exact fractions

Use bare except around the division

Question 2

Compared with catching ZeroDivisionError after dividing, a guard clause before dividing primarily improves:

Hint

Expected errors can be explicit branches.

Asymptotic complexity

Readability and predictable control flow for expected violations

The GIL

JSON serialisation speed

Question 3

safe_ratio(top, bot) returns top/bot; if bot == 0, raise ValueError('zero').

Hint

Check denominator first.

def safe_ratio(top: float, bot: float):
    pass

Starter code is prefilled; replace TODO blocks with your solution.

Runtime output (stdout/stderr)

1 test case will be used for grading

Run checks runtime behavior only. Final correctness is evaluated when you submit.

Card Info

Topic: Defensive APIs: validation, sanitization & exceptions
Difficulty: Beginner
Completed: 0 users

Creator

Pavel