Pandas 3 Completed

[alokusw2]

No description provided.

[super30admin]

1. Problem 1: Calculate Special Bonus

   • Correctness: The solution correctly implements the logic to calculate the bonus based on the given conditions (odd employee_id and name not starting with 'M'). The result is ordered by employee_id as required.
   • Time Complexity: O(n), where n is the number of rows in the DataFrame, due to the apply function.
   • Space Complexity: O(n), as a new column 'bonus' is added to the DataFrame.
   • Code Quality: The code is concise and readable. Using apply with a lambda function is appropriate here.
   • Efficiency: The solution is efficient for the given problem. No further optimizations are needed.

2. Problem 2: Fix Names in a Table

   • Correctness: The solution correctly capitalizes the first letter of each name and makes the rest lowercase using str.capitalize(). The result is ordered by user_id as required.
   • Time Complexity: O(n), where n is the number of rows in the DataFrame, due to the str.capitalize() operation.
   • Space Complexity: O(n), as the 'name' column is modified in place.
   • Code Quality: The code is simple and effective. Using str.capitalize() is the right approach.
   • Efficiency: The solution is efficient. No further optimizations are needed.

3. Problem 3: Patients with a Condition

   • Correctness: The solution correctly filters patients with Type I Diabetes by checking if 'conditions' contains the substring 'DIAB1' at the start of a word using regex. However, the regex pattern r'\bDIAB1' might miss some edge cases where 'DIAB1' is not at the start of a word but still indicates Type I Diabetes.
   • Time Complexity: O(n), where n is the number of rows in the DataFrame, due to the str.contains operation.
   • Space Complexity: O(n), as a new DataFrame is returned.
   • Code Quality: The code is concise and readable. The use of regex is appropriate but could be more precise.
   • Efficiency: The solution is efficient, but the regex pattern could be improved to ensure all cases of Type I Diabetes are captured (e.g., r'\bDIAB1' might not catch 'DIAB1' at the end of a string without a word boundary).

General Feedback:

• The solutions are well-structured and follow best practices for pandas operations.
• For Problem 3, consider refining the regex pattern to ensure all cases of Type I Diabetes are captured. For example, r'(^|\s)DIAB1' might be more inclusive.
• The use of apply in Problem 1 is fine, but for very large DataFrames, vectorized operations might be more efficient.