problem 1 and 2 added

[Tejas274]

No description provided.

[super30admin]

Let me evaluate each solution separately.

For problem1 (calculate_special_bonus.py):

Strengths:

1. The student provides two solutions, showing an understanding of different approaches.
2. The second solution using apply() with lambda is more pandas-idiomatic and efficient.
3. Correctly handles the sorting and column renaming requirements.

Areas for improvement:

1. The first solution uses iterative row access which is inefficient in pandas (O(n) time complexity where n is number of rows).
2. The first solution modifies the original 'salary' column which might not be desired.
3. Could add comments explaining the logic, especially the bonus conditions.

For problem2 (fix_names_table.py):

Strengths:

1. Correctly uses vectorized string operations which is efficient in pandas.
2. Properly handles the case conversion requirements.
3. Returns the sorted result as required.

Areas for improvement:

1. Could add input validation to handle potential null/NA values in the 'name' column.
2. The solution could be made more robust by explicitly handling empty strings.
3. A comment explaining the string manipulation would be helpful.

General observations:

1. Both solutions demonstrate good pandas knowledge.
2. The student shows awareness of more efficient approaches (as seen in the second solution for problem1).
3. Code formatting and structure are good.
4. Could benefit from more defensive programming (handling edge cases).

Time Complexity:

• Both solutions are O(n) where n is number of rows, which is optimal for these problems.

Space Complexity:

• Both solutions are O(n) space, which is expected since we're working with DataFrames.