Data Validation Patterns

Range Checks

Numeric validation keeps values inside the range a program is prepared to handle.

range check A range check compares a value against lower and upper bounds and returns a clear decision.

Range Checks

age
range_check.py
Replay: real traced execution (multi-file project)
def status_for_age(age):
    if age < 18:
        return "too_young"
    if age > 120:
        return "too_high"
    return "ok"


age = 30
status = status_for_age(age)

print(f"age={age}:{status}")

def status_for_age(age):
    if age < 18:
        return "too_young"
    if age > 120:
        return "too_high"
    return "ok"


age = 17
status = status_for_age(age)

print(f"age={age}:{status}")

  1. age ← 30

    9age→ 30 = 30  #@age=1710status = status_for_age(age30)

  2. def status_for_age(age):

    1def status_for_age(age30):2    if age < 18:3        return "too_young"4    if age > 120:5        return "too_high"6    return "ok"

  3. status ← ok

    9age = 30  #@age=1710status→ ok = status_for_age(age30)1112print(f"age={age30}:{statusok}")
    outputage=30:ok

  1. age ← 17

    9age→ 17 = 1710status = status_for_age(age17)

  2. def status_for_age(age):

    1def status_for_age(age17):2    if age < 18:3        return "too_young"

  3. if age < 18:

    1def status_for_age(age):2    if age17 < 18:3        return "too_young"4    if age > 120:

  4. status ← too_young

    9age = 1710status→ too_young = status_for_age(age17)1112print(f"age={age17}:{statustoo_young}")
    outputage=17:too_young