solution to problem 1

[avishwak]

No description provided.

[super30admin]

The student has provided three different solutions to the problem of finding the Nth highest salary from a DataFrame. Here's an evaluation of each solution:

1. Solution 1 (using drop_duplicates and iloc):

   • Correctness: This solution correctly handles the problem by sorting the salaries in descending order, removing duplicates, and then selecting the Nth highest salary. It also handles edge cases where N is out of bounds by returning None.
   • Time Complexity: O(n log n) due to sorting, where n is the number of unique salaries.
   • Space Complexity: O(n) for storing the sorted and deduplicated DataFrame.
   • Code Quality: The code is clean and readable. The use of iloc is appropriate here.
   • Efficiency: The solution is efficient for typical cases, but sorting could be avoided if a more optimized approach is used (e.g., using a heap).

2. Solution 2 (using set):

   • Correctness: This solution also correctly handles the problem by using a set to remove duplicates, sorting the unique salaries, and then selecting the Nth highest salary. It handles edge cases similarly.
   • Time Complexity: O(n log n) due to sorting, where n is the number of unique salaries.
   • Space Complexity: O(n) for storing the set and the sorted list.
   • Code Quality: The code is readable, but converting to a set and then back to a list is slightly less idiomatic for pandas operations.
   • Efficiency: Similar to Solution 1, sorting is the bottleneck here.

3. Solution 3 (using drop_duplicates, sort_values, head, and tail):

   • Correctness: This solution is also correct and handles edge cases. It uses pandas operations more idiomatically.
   • Time Complexity: O(n log n) due to sorting, where n is the number of unique salaries.
   • Space Complexity: O(n) for storing the deduplicated and sorted DataFrame.
   • Code Quality: The code is clean and uses pandas operations effectively. The chaining of methods is clear.
   • Efficiency: Similar to the other solutions, sorting is the main operation affecting performance.

General Feedback:

• Strengths:

  • All solutions correctly handle the problem and edge cases (e.g., N out of bounds).
  • The code is well-structured and readable.
  • The student demonstrates knowledge of different approaches to solve the problem.

• Areas for Improvement:

  • The use of sorting in all solutions leads to O(n log n) time complexity. A more efficient approach could involve using a heap (O(n log k) where k is N), but this might be overkill for typical pandas operations.
  • Solution 2 is less idiomatic for pandas and could be simplified by using pandas' built-in methods (e.g., drop_duplicates).
  • The student could consider adding comments to explain the logic, especially for Solution 3, where method chaining might be less clear to some readers.