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def bayes(prior, hit_rate, false_alarm):
    true_pos = hit_rate * prior
    false_pos = false_alarm * (1 - prior)
    return true_pos / (true_pos + false_pos)

p = bayes(prior=0.01, hit_rate=0.99, false_alarm=0.05)
print(round(p, 3))

1. Line 1: We define a function with three inputs. Nothing runs yet: Python just remembers the recipe.
2. Line 6: Now we call it: 1% of people are sick, the test catches 99%, and 5% of healthy people get a false alarm.
3. Line 2: Share of everyone who is sick and tests positive: 0.99 × 0.01.
4. Line 3: Share who are healthy but test positive: 0.05 × 0.99. Five times more than the real cases!
5. Line 4: Of all positives, how many are real? 0.0099 ÷ 0.0594 ≈ 0.167.
6. Line 6: The answer comes back and is stored in p.
7. Line 7: Only 16.7%: a positive test for a rare disease is usually a false alarm.