Pandas4 Submitted

[PranathiDeepak]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of the problem requirements and provides working solutions for both Problem1 and Problem2. Here are some detailed observations:

Strengths:

1. Correctness: Both functions correctly handle the task of finding the nth highest salary and second highest salary respectively. They properly handle edge cases where N is invalid or there are fewer than 2 unique salaries.
2. Set Usage: Using a set to collect unique salaries is a good approach to eliminate duplicates.
3. Edge Case Handling: The solutions correctly return None when the requested rank is invalid or when there aren't enough unique salaries.

Areas for Improvement:

1. Time Complexity: The current implementation has O(n) time complexity for iterating through the DataFrame and O(n log n) for sorting, which could be optimized. Using pandas' built-in methods like drop_duplicates() and nlargest() would be more efficient.
2. Space Complexity: Creating a new DataFrame from a set and then sorting it uses extra space. This could be optimized by using pandas operations directly on the original DataFrame.
3. Code Duplication: The two solutions are very similar, suggesting an opportunity to create a helper function that could be reused.
4. Pandas Best Practices: Direct iteration over DataFrame rows using for i in range(len(employee)) is not the most pandas-idiomatic approach. Using vectorized operations would be more efficient and cleaner.
5. Column Naming: The column naming in the return statements could be simplified. The f-string in Problem1 is unnecessary since N is already passed as an argument.

Suggested Optimizations:

1. For Problem1, consider: employee['salary'].drop_duplicates().nlargest(N).iloc[-1] if N <= len(employee['salary'].drop_duplicates()) else None
2. For Problem2, a similar approach could be used with nlargest(2).