solution to problems 1 and 2

[avishwak]

No description provided.

[super30admin]

The student has provided multiple solutions for each problem, demonstrating a good understanding of different approaches to solve the same problem. Here's a detailed evaluation:

1. Correctness:

• Problem 1 (Calculate Special Bonus): All four solutions correctly implement the logic to calculate bonuses based on the given conditions. The vectorized approaches (solutions 3 and 4) are particularly well-implemented.
• Problem 2 (Fix Names in a Table): Both solutions correctly capitalize the first letter of each name and lowercase the rest. The use of capitalize() is more elegant than manual string manipulation.

2. Time Complexity:

• Problem 1: Solutions 1 and 2 use loops with O(n) time complexity. Solutions 3 and 4 use vectorized operations which are also O(n) but more efficient in practice due to pandas optimizations.
• Problem 2: Both solutions use vectorized string operations with O(n) time complexity.

3. Space Complexity:

• All solutions have O(n) space complexity as they create new columns or DataFrames proportional to the input size. The vectorized solutions are more memory efficient than the loop-based ones.

4. Code Quality:

• The code is well-structured and readable with clear comments explaining each approach.
• The student demonstrates good pandas knowledge by showing both basic and advanced approaches.
• The use of docstrings and comments is excellent, explaining the thought process and differences between solutions.

5. Efficiency:

• The vectorized solutions (especially solution 4 in Problem 1) are the most efficient and should be preferred.
• One minor improvement could be to use inplace=True where possible to avoid creating intermediate DataFrames.

Areas for Improvement:

• While the loop-based solutions work, they should generally be avoided in pandas in favor of vectorized operations for better performance.
• The solutions could include more edge case testing (e.g., empty DataFrames, null values).