Question32 The Pandas DataFrame employees is displayed as below. [table]
Name | Department | Salary | Location John | HR | 60000 | Sydney Emma | IT | NaN | Melbourne Liam | Finance | 55000 | None Olivia | IT | 70000 | Sydney Noah | Finance | 65000 | None Ava | HR | 62000 | Brisbane
[/table] Which option(s) can replace missing values in the Location column with the most frequently occurring location? Select one or more alternatives: [table]
[/table]Select one or more alternatives: employees['Location'].fillna('None') employees['Location'].fillna(employees['Location'].mode()[0], inplace=True) employees['Location'].replace(np.nan, 'Melbourne') employees['Location'].fillna('Sydney', inplace=True) ResetMaximum marks: 3 Flag question undefined

Question

BlackTom AI · Accepted Answer

The question asks which options can replace missing values in the Location column with the most frequently occurring location. In this dataset, the missing values (NaN) should be filled with the mode of the non-missing Location values. Looking at the options one by one:

Option 1: employees['Location'].fillna('None')
This would replace NaN entries with the string 'None'. While this fills the missing slots, it does not use the most frequent location, so it does not satisfy the requirement to fill with the mode. It also introduces a literal string that was not present as data, which could be misleading for downstream analysis.

Option 2: employees['Location'].fillna(employees['Location'].mode()[0], inplace=True)
Here, we fill NaN values with the first value returned by the mode() of the Location series. Since mode() returns the most frequent value among the non-missing entries, this correctly uses the most frequent location (the mode). The [0] extracts that value, and inplace=True performs the replacement in place on the original column, which is typically desired for in-place data cleaning.

Option 3: employees['Location'].replace(np.nan, 'Melbourne')
This approach replaces NaN with the literal string 'Melbourne' for every NaN, regardless of what the actual mode is. It does not ensure the replacement uses the most frequent location and also hard-codes Melbourne, which may be incorrect if the mode is not Melbourne. Therefore, it does not meet the requirement.

Option 4: employees['Location'].fillna('Sydney', inplace=True)
This fills NaN values with the string 'Sydney' regardless of the actual most frequent location. If Sydney is indeed the mode, this would work; however, the question asks to replace with the most frequent location, and we should derive that value from the data rather than assuming it. If Sydney is not the mode in another scenario, this would be incorrect. In this specific dataset, Sydney is the mode, so this would be correct by coincidence, but the approach is not guaranteed to always reflect the most frequent location.

In summary, the two options that correctly replace NaN with the most frequently occurring location (the mode) are the second option and, in this dataset, the fourth option would also yield the correct result by coincidence if Sydney is the mode. However, since the question asks for the one that uses the most frequent location, the explicit and reliable choice is filling with the mode value via fillna(mode()[0], inplace=True).

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