Assertion (A) : The output of addition of two series will be NaN, if one of the elements or both the elements have no value(s). Reason (R) : While performing mathematical operations on a series, by default all missing values are filled in with 0. (A) Both (A) and (R) are true and

Question

Assertion (A) : The output of addition of two series will be NaN, if one of the elements or both the elements have no value(s). Reason (R) : While performing mathematical operations on a series, by default all missing values are filled in with 0. (A) Both (A) and (R) are true and (R) is the correct explanation for (A). (B) Both (A) and (R) are true and (R) is not the correct explanation for (A). (C) (A) is true and (R) is false. (D) (A) is false but (R) is true.

Accepted Answer

> [!TLDR]
> In pandas, adding two Series propagates `NaN` values (missing data remains missing), so Assertion (A) is true. Reason (R) claims missing values are filled with 0 by default, which is false — pandas preserves `NaN` in arithmetic. The answer is **(C)**.

## Understanding Missing Data in Pandas Series Operations

When you work with pandas Series, missing values are represented as `NaN` (Not a Number). The question asks what happens when you add two Series that contain missing values, and whether pandas automatically fills those gaps with zeros.

The core concept here is **how pandas handles missing data during arithmetic operations**. This behavior is fundamental to data analysis because real-world datasets almost always have gaps, and you need predictable rules for what happens when you compute with incomplete data.

Let's examine each statement:

### Evaluating Assertion (A)

Assertion (A) claims that adding two Series produces `NaN` if one or both elements are missing.

This is **true**. Pandas follows the IEEE floating-point standard for `NaN` propagation: any arithmetic operation involving `NaN` returns `NaN`. If you add a number to `NaN`, you get `NaN`. If you add `NaN` to `NaN`, you still get `NaN`.

```python
import pandas as pd
import numpy as np

s1 = pd.Series([1, 2, np.nan, 4])
s2 = pd.Series([10, np.nan, 30, 40])
result = s1 + s2
# Output: [11, NaN, NaN, 44]
```

Position 1: 2 + NaN = NaN  
Position 2: NaN + 30 = NaN

The missing values propagate through the calculation, preserving the information that data was absent.

### Evaluating Reason (R)

Reason (R) claims that pandas fills missing values with 0 by default during mathematical operations.

This is **false**. Pandas does *not* automatically fill missing values with any default value during arithmetic. The default behavior is to propagate `NaN`, as we just saw. If you wanted to treat missing values as zero, you would need to explicitly use methods like `fillna(0)` or pass `fill_value=0` to

Concept understanding — Missing Value Handling

Missing Value Handling

The everyday intuition

What "missing" actually means

Why it matters in commerce and humanities

The two broad approaches

1. Deletion — remove the incomplete records

2. Imputation — fill in the missing values

Understanding Missing Data in Pandas Series Operations

Evaluating Assertion (A)

Evaluating Reason (R)

Understanding Missing Data in Pandas Series Operations

Evaluating Assertion (A)

Evaluating Reason (R)

🎓Unlock everything free for 14 days

Unlock everything free for 14 days