Done Pandas5

[sakshiasati17]

No description provided.

[super30admin]

1. Department_highest_salary.py:

   • Correctness: The solution correctly identifies employees with the highest salary in each department by merging the tables and using transform to find max salaries. However, it should handle cases where multiple employees share the highest salary in a department.
   • Time Complexity: O(n log n) due to the merge and groupby operations.
   • Space Complexity: O(n) for storing the merged DataFrame.
   • Code Quality: The code is concise but could benefit from more descriptive variable names (e.g., 'df' could be 'merged_df'). The column renaming at the end is clear.
   • Efficiency: The solution is efficient for typical cases. No major optimizations needed.

2. Rank_scores.py:

   • Correctness: The solution correctly ranks scores using dense ranking. However, the sort_values at the end is redundant since rank already processes the scores in order.
   • Time Complexity: O(n log n) for the ranking operation.
   • Space Complexity: O(n) for storing the new rank column.
   • Code Quality: The code is clean and straightforward. The method parameter in rank is appropriately set to 'dense'.
   • Efficiency: The sort_values call could be removed since rank processes the data in order.

Overall, both solutions are correct and efficient. The main areas for improvement are handling edge cases in Department_highest_salary.py and removing redundant operations in Rank_scores.py.

[super30admin]

Let me evaluate each file separately.

Department_highest_salary.py:

1. Correctness: The solution appears correct for finding employees with the highest salary in each department. It merges the tables, calculates max salary per department, and filters accordingly.
2. Time Complexity: O(n) for merge + O(n) for groupby + O(n) for filtering = O(n) overall, which is efficient.
3. Space Complexity: O(n) for the merged dataframe, which is reasonable.
4. Code Quality: The code is concise but could be more readable with better variable names (df -> merged_df) and spacing around operators.
5. Efficiency: Good use of transform() for vectorized operations. Could consider adding .astype(int) to the rank if needed.

Rank_scores.py:

1. Correctness: The solution correctly ranks scores using dense ranking, but the final sort is redundant since rank() already produces the correct order.
2. Time Complexity: O(n log n) for ranking + O(n log n) for sorting = O(n log n), but the sort is unnecessary.
3. Space Complexity: O(n) for the new column, which is standard.
4. Code Quality: Simple and clean, but the redundant sorting could be removed.
5. Efficiency: Could be optimized by removing the final sort since rank() already produces the desired order.

General suggestions:

1. Add docstrings to explain function purposes
2. Consider adding type hints for return values
3. For Department_highest_salary, the output columns could be explicitly selected before merging to save memory
4. Consistent spacing around operators would improve readability