pandas3_kiran_Submission

[kiran160195]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of pandas operations and string manipulations. Here's a detailed evaluation:

1. Correctness:

• Problem1 (calculate_special_bonus): The solution correctly applies the bonus conditions (name doesn't start with 'M' and odd employee_id) using pandas' where() method. The output format is also correct.
• Problem2 (fix_names): The solution properly capitalizes names and returns them in the required format.
• Problem3 (find_patients): The solution correctly identifies patients with diabetes conditions using both startswith() and contains() methods to catch different pattern cases.

2. Time Complexity:

• All solutions use pandas vectorized operations which are generally efficient. The operations are O(n) where n is the number of rows in the DataFrame.

3. Space Complexity:

• The solutions create new columns or modify existing ones in place, maintaining O(n) space complexity which is optimal.

4. Code Quality:

• The code is well-structured and readable.
• Variable names are appropriate.
• Proper use of pandas methods and chaining.
• Could benefit from comments explaining the logic, especially in problem3 where the condition checking is more complex.

5. Efficiency:

• Problem1 could potentially use np.where() for slightly better performance, but the current implementation is fine.
• Problem3's condition checking is optimal as it handles both cases (DIAB1 at start and DIAB1 after space) efficiently.

Areas for improvement:

• Adding docstrings to explain function purposes and parameters.
• In problem3, consider adding a comment explaining why both startswith() and contains() are needed.
• The solutions could include error handling for edge cases (empty DataFrames, null values).