Pandas4 Completed

[alokusw2]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of pandas operations to solve the problems of finding the nth highest and second highest salaries. Here are the detailed evaluations:

1. Correctness: Both solutions correctly handle the requirements. They properly drop duplicate salaries, sort them in descending order, and handle edge cases where the requested rank (N or 2nd) doesn't exist by returning None. This matches the expected behavior from the problem descriptions.

2. Time Complexity: The time complexity is O(n log n) due to the sorting operation (sort_values()), where n is the number of unique salaries. This is optimal for this type of problem since we need sorted data to find the nth highest value.

3. Space Complexity: The space complexity is O(n) as we're creating a new Series with unique salaries and then sorting it. This is reasonable for the problem constraints.

4. Code Quality: The code is clean, readable, and well-structured. The use of pandas operations is appropriate. However, the second problem could have been implemented as a special case of the first problem (with N=2), which would demonstrate better code reuse.

5. Efficiency: The solutions are efficient for their purpose. One minor optimization could be to use nlargest() instead of sorting the entire list when we only need the top N values, but the current approach is perfectly acceptable.

Areas for improvement:

• The second problem could have been implemented by calling the first function with N=2 to avoid code duplication.
• Adding docstrings to explain the functions' purposes and parameters would improve maintainability.
• The reset_index() call isn't strictly necessary since we're using iloc which ignores the index.

[super30admin]

The student's solution demonstrates a good understanding of pandas operations and correctly solves both problems. Here are the detailed evaluations:

1. Correctness:

• Both solutions correctly handle the requirement to find the nth highest salary and second highest salary respectively.
• They properly handle edge cases where there might not be enough distinct salaries (returning None/Null as required).
• The use of drop_duplicates() ensures we're working with distinct salary values.

2. Time Complexity:

• The operations used (drop_duplicates, sort_values) have a time complexity of O(n log n) where n is the number of rows in the DataFrame.
• This is optimal for these problems as sorting is necessary to determine the nth highest value.

3. Space Complexity:

• The space complexity is O(n) due to creating new Series objects during operations.
• This is reasonable given the problem requirements.

4. Code Quality:

• The code is clean, well-structured, and easy to understand.
• Variable names are descriptive (unique_salaries clearly indicates its purpose).
• Consistent formatting and style.
• Could benefit from docstrings explaining the function purpose and parameters.

5. Efficiency:

• The solution is already efficient for the given problems.
• One potential optimization could be to use nlargest() instead of full sorting when N is small compared to the dataset size, but the current approach is fine for most cases.

Areas for improvement:

• Adding docstrings to explain the functions' purposes and parameters.
• The solutions are very similar - the second could potentially be implemented by calling the first with N=2 to avoid code duplication.
• Consider adding type hints for the return value (though this might be overkill for these simple functions).