Pandas4: Problem Solutions_RN32MAY2025DS

[Feminto]

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[super30admin]

The student has provided two solutions for the nth_highest_salary problem and one for the second_highest_salary problem. Here's my evaluation:

Strengths:

1. Correctness: Both implementations correctly handle the problem requirements, including edge cases where N is invalid or there aren't enough unique salaries.
2. Readability: The code is generally readable with clear variable names and comments explaining the logic.
3. Edge Cases: Proper handling of edge cases like N being 0, negative, or larger than the number of unique salaries.

Areas for Improvement:

1. Redundancy: The two methods in NthHighestSalary.py are essentially doing the same thing, with Method 2 being less efficient (using a set and manual iteration). Method 1 is clearly better and Method 2 should be removed.
2. Efficiency: While both solutions are correct, they could be optimized. The current approach sorts all unique salaries, which is O(n log n) time complexity where n is the number of unique salaries. For large datasets, this might be inefficient if we only need the Nth highest salary.
3. Code Duplication: The second_highest_salary function could be written as a special case of nth_highest_salary with N=2, reducing code duplication.
4. Best Practices: The function could use pandas' built-in methods more effectively. For example, nlargest() could be used to potentially improve performance.

Time Complexity:

• Current solution: O(n log n) due to sorting all unique salaries
• Space Complexity: O(n) for storing unique salaries

Optimization Suggestions:

1. Consider using pandas' nlargest() method which might be more efficient for large N.
2. For very large datasets where you only need one specific rank, a more efficient algorithm could be used that doesn't require sorting all elements.