solution to both the problems

[avishwak]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of pandas operations and SQL-like transformations. Here's a detailed evaluation:

1. Correctness:

• For Problem 1 (Department Highest Salary), both solutions appear correct. Solution 1 uses groupby and merge operations effectively, while Solution 2 demonstrates understanding of transform() which is more efficient for this use case.
• For Problem 2 (Rank Scores), the solution correctly implements dense ranking as required.

2. Time Complexity:

• Problem 1 solutions: O(n log n) for groupby operations and O(n) for merges
• Problem 2 solution: O(n log n) for sorting and ranking
  These are optimal for pandas operations.

3. Space Complexity:

• Both problems create new DataFrames during operations, resulting in O(n) space complexity, which is expected for these transformations.

4. Code Quality:

• Good use of pandas operations and chaining
• Clear variable naming
• Helpful comments explaining key concepts
• Could improve by:
  • Adding docstrings to functions
  • Using more descriptive variable names (e.g., 'result_df' instead of just 'df')
  • Including edge case handling comments (e.g., empty DataFrames)

5. Efficiency:

• Solution 2 for Problem 1 is more efficient as it avoids creating intermediate DataFrames
• Both solutions could potentially benefit from using inplace=True where possible to reduce memory usage

Additional suggestions:

• Consider adding input validation
• Include example test cases in comments
• For Problem 1, the column renaming could be done earlier in the pipeline for clarity