Which Python expression selects rows in df_customers where any column contains NULL values (axis=1)?

Which Python expression selects rows in df_customers where any column contains NULL values (axis=1)?

• A. df_customers.notnull().all(axis=1)
• B. df_customers.isnull().any(axis=1)
• C. df_customers.dropna()
• D. df_customers.isnull().sum(axis=1)

Answer: (B)

To find rows that have any missing values, create a boolean mask across cells with isnull(), then reduce along columns in each row with any(axis=1). df_customers.isnull() marks True where a cell is NULL. any(axis=1) looks at all columns in each row and returns True if any of those cells are True (i.e., if the row contains at least one NULL). This boolean Series can be used to index the DataFrame to select exactly the rows that have at least one missing value. The other options don’t fit: isnull().all(axis=1) would require every column in a row to be NULL; dropna() removes rows with any NULLs rather than selecting them; isnull().sum(axis=1) returns a numeric count of NULLs per row, not a boolean mask for selection.

To find rows that have any missing values, create a boolean mask across cells with isnull(), then reduce along columns in each row with any(axis=1). df_customers.isnull() marks True where a cell is NULL. any(axis=1) looks at all columns in each row and returns True if any of those cells are True (i.e., if the row contains at least one NULL). This boolean Series can be used to index the DataFrame to select exactly the rows that have at least one missing value.

The other options don’t fit: isnull().all(axis=1) would require every column in a row to be NULL; dropna() removes rows with any NULLs rather than selecting them; isnull().sum(axis=1) returns a numeric count of NULLs per row, not a boolean mask for selection.