Completed Pandas3

[DhruvParashar246]

No description provided.

[super30admin]

1. Calculate Special Bonus:

   • Correctness: The solution correctly implements the bonus calculation logic where employees with odd IDs and names not starting with 'M' get their full salary as bonus, others get 0. The sorting by employee_id is also correctly implemented.
   • Time Complexity: O(n) for applying the lambda function and O(n log n) for sorting, where n is the number of employees.
   • Space Complexity: O(n) for storing the bonus column and the sorted result.
   • Code Quality: The code is concise and readable. Using apply with a lambda function is appropriate here.
   • Efficiency: The solution is efficient, though using vectorized operations might be slightly faster than apply.

2. Fix Names in a Table:

   • Correctness: The solution correctly capitalizes the first letter of each name and sorts by user_id.
   • Time Complexity: O(n) for the string operation and O(n log n) for sorting.
   • Space Complexity: O(n) for storing the modified names and the sorted result.
   • Code Quality: The code is simple and effective. Using str.capitalize() is the right approach.
   • Efficiency: The solution is already efficient. No major optimizations needed.

3. Patients with a Condition:

   • Correctness: The solution correctly filters patients with conditions starting with 'DIAB1' or containing ' DIAB1'. However, it might miss some edge cases like multiple spaces before 'DIAB1' or cases where 'DIAB1' appears as part of another word.
   • Time Complexity: O(n) for the string operations.
   • Space Complexity: O(n) for the filtered DataFrame.
   • Code Quality: The code is clear but could be improved by handling more edge cases.
   • Efficiency: The solution is efficient, but the condition checking could be more robust.

Overall, the solutions are well-written and mostly correct. The main area for improvement is in the Patients with a Condition problem, where the condition checking could be more thorough to handle edge cases.