pandas5 submission

[kiran160195]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of pandas operations for solving ranking problems. Here's a detailed evaluation:

1. Correctness:

• For problem1 (department_highest_salary), the solution correctly merges employee and department data, calculates dense ranks by department, and filters top earners. The output format matches expected column names.
• For problem2 (order_scores), the solution correctly sorts scores and assigns dense ranks. The output columns are appropriately selected.

2. Time Complexity:

• Both solutions use pandas operations that are generally efficient. The merge and groupby operations in problem1 are O(n), while sorting in problem2 is O(n log n). The ranking operations are also efficient.

3. Space Complexity:

• The solutions create intermediate DataFrames but don't appear to use excessive memory. The space complexity is reasonable for the operations performed.

4. Code Quality:

• Strengths:
  • Clear use of pandas operations
  • Proper column renaming in problem1
  • Good function naming
• Areas for Improvement:
  • Could add docstrings explaining parameters and return values
  • In problem1, the column selection could be more explicit (e.g., using .loc)
  • Could add comments explaining the dense ranking choice

5. Efficiency:

• Both solutions are efficient for typical use cases. No major optimizations needed, though:
  • For problem1, could consider filtering before merging if departments are large
  • For problem2, could potentially use nlargest() if only top N scores are needed

Edge Cases to Consider:

• Empty input DataFrames
• Ties in salaries/scores
• Missing department IDs in problem1
• Null values in any columns