problem 1 and 2 added

[Tejas274]

No description provided.

[super30admin]

The student has provided two solutions for the first problem (department highest salary) and one solution for the second problem (rank scores). Here's my evaluation:

Strengths:

1. Both solutions for the first problem correctly use pandas operations to find the highest salary in each department.
2. The merge operation is correctly implemented with proper join conditions.
3. The ranking solution (second approach) is more robust as it handles ties correctly using dense ranking.
4. The second problem solution correctly implements dense ranking for scores.
5. Code is generally readable with proper column renaming in the output.

Areas for Improvement:

1. For the first problem, the first solution using transform('max') might not handle ties correctly (multiple employees with same max salary in a department).
2. The solutions could benefit from comments explaining the approach.
3. The output columns could be consistently named (first solution uses 'Department'/'Employee', second uses lowercase).
4. For the second problem, the output could be renamed to match typical SQL-style output ('score' and 'rank').
5. No error handling for empty DataFrames or missing columns.

Time Complexity:

1. Both solutions for the first problem are O(n) for the groupby operations.
2. The second problem solution is O(n log n) due to the ranking operation.

Space Complexity:

1. All solutions create new DataFrames, so space complexity is O(n).

Code Quality:

1. Generally good, but could benefit from:
   • Consistent naming conventions
   • Comments explaining the logic
   • Handling edge cases
   • Consistent output formatting