problem 1 and 2 added

[Tejas274]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of pandas operations to solve the problem of finding the nth highest salary. Here are the detailed observations:

1. Correctness:

   • Both solutions correctly handle the edge cases where N is invalid (either <=0 or > number of unique salaries) or when there aren't enough unique salaries.
   • The approach of dropping duplicates, sorting, and then using head/tail to get the nth element is correct.
   • However, there's a potential issue in the nth_highest_salary function where res is set to None for invalid N, but the code still tries to access values[0] when N is valid. This could cause an error if the filtered DataFrame is empty.

2. Time Complexity:

   • The time complexity is O(n log n) due to the sorting operation, which is optimal for this type of problem.
   • The drop_duplicates operation is O(n), but it's dominated by the sorting.

3. Space Complexity:

   • The space complexity is O(n) due to creating a new DataFrame with unique salaries.
   • This is reasonable for the problem size.

4. Code Quality:

   • The code is generally clean and readable.
   • Variable names are appropriate (df, res).
   • The functions follow a consistent structure.
   • The docstrings are missing, which would help document the purpose and parameters of each function.

5. Efficiency:

   • The solution could be slightly optimized by using nlargest() instead of sort_values + head.
   • The second_highest_salary function could be written as a special case of nth_highest_salary for better code reuse.

Areas for improvement:

• Add docstrings to explain the functions.
• Consider using nlargest() for better readability and potential performance.
• Handle the case where the filtered DataFrame might be empty even when N is valid.
• The second_highest_salary could be implemented by calling nth_highest_salary(employee, 2) to avoid code duplication.