Pandas3 Submitted

[PranathiDeepak]

No description provided.

[super30admin]

Let me evaluate each of the three solutions separately:

Problem1.py (calculate_special_bonus):

1. Correctness: The logic appears correct for calculating bonuses based on odd employee_id and names not starting with 'M'.
2. Time Complexity: O(n) where n is number of employees, which is optimal.
3. Space Complexity: O(n) for storing results, which is necessary.
4. Code Quality: Could be improved by using vectorized pandas operations instead of iterating through rows. Also, the salary variable is defined but not used.
5. Efficiency: Using pandas' built-in operations would be more efficient than Python loops.

Problem2.py (fix_names):

1. Correctness: Correctly capitalizes first letter and makes rest lowercase.
2. Time Complexity: O(n) due to string operations on each name.
3. Space Complexity: O(n) for storing modified names.
4. Code Quality: Clean and concise, good use of pandas string methods.
5. Efficiency: This is already quite efficient using vectorized operations.

Problem3.py (find_patients):

1. Correctness: Logic for finding diabetes patients seems correct but has potential issues:
   • Only checks if any condition starts with 'DIAB1' (should it be exactly 'DIAB1'?)
   • Doesn't handle cases where 'DIAB1' might appear in the middle of a condition
2. Time Complexity: O(n*m) where n is patients and m is conditions per patient - could be optimized.
3. Space Complexity: O(n) for results, which is necessary.
4. Code Quality: Could use pandas string methods instead of manual splitting and checking.
5. Efficiency: Using str.contains() with regex would be more efficient.

General improvements needed:

1. Avoid iterating through DataFrames when possible - use vectorized pandas operations
2. Some unused variables (like salary in Problem1)
3. Problem3 needs more robust condition checking
4. Consistent style (some functions have spaces around operators, others don't)