Completed Pandas4

[kaaviyavarrshini]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of pandas operations for solving salary ranking problems. Here are my observations:

Strengths:

1. Correctness: Both solutions correctly handle the problem of finding nth highest salary, including edge cases where N is invalid or there aren't enough distinct salaries.
2. Approach: The pandas solution uses efficient operations like drop_duplicates(), sort_values(), and head/tail combinations to get the desired result.
3. Edge Cases: Properly handles cases where N is too large or non-positive by returning None.

Areas for Improvement:

1. Code Duplication: The second problem (SecondHighestSalary) is essentially a special case of the first problem (NthHighestSalary). The student could have reused the first function with N=2.
2. Type Hints: The solution uses type hints for the DataFrame parameter but could benefit from return type hints.
3. Efficiency: The non-pandas solution creates multiple data structures (set, list) which increases space complexity unnecessarily.
4. Readability: The pandas solution chain could be broken into clearer intermediate steps with comments explaining each operation.
5. Error Handling: While edge cases are handled, explicit error messages or logging could be added for invalid N values.

Time Complexity:

• Pandas solution: O(n log n) due to sorting
• Non-pandas solution: O(n log n) due to sorting

Space Complexity:

• Pandas solution: O(n) for storing the unique salaries
• Non-pandas solution: O(n) for storing unique salaries in set and list