Done Pandas4

[sakshiasati17]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of the problem requirements and provides working solutions for both the Nth highest salary and second highest salary problems. Here are some specific observations:

Strengths:

1. Correctness: Both functions correctly handle the core logic of finding the Nth highest and second highest salaries, including edge cases where N is invalid or there aren't enough distinct salaries.
2. Edge Case Handling: The solutions properly handle cases where N is too large or when there aren't enough distinct salaries.
3. Use of Sets: The use of sets to eliminate duplicate salaries is appropriate.

Areas for Improvement:

1. Time Complexity: The current implementation has O(n log n) time complexity due to sorting. While acceptable for small datasets, this could be optimized for larger datasets.
2. Code Duplication: The two solutions share very similar logic. The second highest salary could be implemented as a special case of the Nth highest salary function.
3. Pandas Best Practices: The solutions don't fully leverage pandas' built-in methods. Using drop_duplicates() and nlargest() would be more idiomatic.
4. Variable Naming: Variable names like result_set and result could be more descriptive (e.g., unique_salaries and sorted_salaries).
5. Efficiency: The conversion from set to list is unnecessary - sets can be sorted directly.
6. Column Naming: In the second highest salary solution, the column name is hardcoded, which isn't as flexible as the Nth highest solution's approach.

Suggested Improvements:

1. Use pandas methods like drop_duplicates() and nlargest() for more concise and efficient code.
2. Consider combining both solutions into a single, more general function.
3. Improve variable naming for better readability.
4. Consider using pandas' built-in sorting capabilities rather than converting to Python lists.