Solved three problems

[hinduja97]

No description provided.

[super30admin]

1. Correctness:

   • Problem1: The solution correctly calculates the bonus based on the given conditions (name starting with 'M' or even employee_id results in 0 bonus, otherwise full salary). The sorting by employee_id is also correctly implemented.
   • Problem2: The solution correctly capitalizes the first letter of each name and sorts by user_id in ascending order.
   • Problem3: The solution correctly identifies patients with conditions starting with 'DIAB1' or containing ' DIAB1', which is the correct approach for finding diabetes type 1 patients.

2. Time Complexity:

   • Problem1: O(n) for the apply operation and O(n log n) for sorting, where n is the number of employees.
   • Problem2: O(n) for the string operation and O(n log n) for sorting, where n is the number of users.
   • Problem3: O(n) for the string operations, where n is the number of patients.

3. Space Complexity:

   • Problem1: O(n) for storing the bonus column and the sorted result.
   • Problem2: O(n) for storing the modified name column and the sorted result.
   • Problem3: O(n) for storing the filtered DataFrame.

4. Code Quality:

   • The code is generally well-structured and readable.
   • Variable names are appropriate.
   • The use of pandas operations is efficient and follows best practices.
   • For Problem3, the condition could be written more clearly by combining the two conditions into a single regex pattern for better readability.

5. Efficiency:

   • The solutions are efficient for the given problems.
   • For Problem3, using a single regex pattern might be slightly more efficient and cleaner: patients['conditions'].str.contains(r'\bDIAB1').