Question

What does the df.isnull().sum() function do in Pandas?

• A. It finds the number of NaN values corresponding to each attribute.
• B. It fills the number of NaN values with average value in the DataFrame.
• C. It checks if there are any NaN values in the DataFrame.
• D. It fills NaN values with zeros in the DataFrame.

Hint:

To get the total count of missing values in the entire DataFrame instead of column-wise, you can chain sum twice:
df.isnull().sum().sum()

Answer 1

The Correct Option is A

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Step 1: Understanding the Question:
The question asks for the exact function and behavior of the chained Pandas command df.isnull().sum().

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Step 2: Key Formula or Approach:
The method works in two distinct steps:
1. df.isnull() maps every cell in the DataFrame to True if it is missing (NaN) and False otherwise.
2. Chaining .sum() on this boolean DataFrame aggregates the results. In Python, True is treated as 1 and False is treated as 0. By default, it sums along the columns (axis=0), giving the count of missing values per column.

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Step 3: Detailed Explanation:
Let's analyze the options:
- Option (A) states "It finds the number of NaN values corresponding to each attribute." This is correct. Summing the booleans column-wise directly gives the count of nulls in each attribute (column).
- Option (B) describes data imputation with the mean, which is done using df.fillna(df.mean()).
- Option (C) refers to checking if any NaN exists anywhere, which is typically done using df.isnull().any() or df.isnull().values.any().
- Option (D) describes filling NaNs with zero, which is executed using df.fillna(0).
Thus, option (A) is the correct statement.

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Step 4: Final Answer:
The correct option is (A).