Does zero mean the same thing as missing?

1 · Observe

Zero rupees and 'not recorded' are different

A telecom's bill column holds 299, None, 799, 0 and 499. The 0 means the customer had a free month. The None means nobody recorded a bill. If code treats them alike, an average will be wrong and no error will tell you.

2 · Express

if, elif, else and None

3 · Visualize

Two policies, two averages

Dropping the None keeps four real values (average 399.25). Calling it 0 averages over five and understates the bill by ₹80. A truthiness filter such as [b for b in bills if b] drops the genuine 0 as well, keeping only three values.

4 · Discover

Say the policy out loud

There is no single right answer for missing values, only a stated one. Code should make the choice visible: an explicit `is None` check, a comment on what happens to the row, and a count of how many rows were affected.

5 · Apply

Two worked examples

Example 1, classification. describe(0) returns 'free month' and describe(None) returns 'missing', because the None check comes first.

Example 2, a range check. 18 <= age < 60 is True for 34. Python allows chained comparisons.

6 · Where it goes wrong

Three ways it goes wrong

7 · In Python

Missing values in code

Here is how to implement it, step by step. The code below does exactly these steps, and every line is editable.

1. Write the list of bills with None for missing and 0 for a free month.
2. Test missing with `is None`, never with plain truthiness.
3. Keep only the observed values: [b for b in bills if b is not None].
4. Average over the values you kept.
5. State the policy in a comment and count how many rows were skipped.

bills = [299, None, 799, 0, 499]            # None = not recorded, 0 = recorded as zero rupees

def describe(b):
    if b is None:
        return "missing"
    elif b == 0:
        return "free month"
    elif b < 500:
        return "low"
    else:
        return "high"
print([describe(b) for b in bills])

# the truthiness trap: 0 and None are both falsy
print("keep only truthy values:", [b for b in bills if b])
print("keep only observed values:", [b for b in bills if b is not None])
observed = [b for b in bills if b is not None]
print("average bill treating missing as skipped:", sum(observed) / len(observed))
print("average bill treating missing as 0:", sum(b or 0 for b in bills) / len(bills))

# chained conditions and booleans
age = 34
print("18 <= age < 60:", 18 <= age < 60, "| not (age > 40):", not (age > 40))

['low', 'missing', 'high', 'free month', 'low']
keep only truthy values: [299, 799, 499]
keep only observed values: [299, 799, 0, 499]
average bill treating missing as skipped: 399.25
average bill treating missing as 0: 319.4
18 <= age < 60: True | not (age > 40): True