submitting all problems

[avishwak]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of pandas DataFrame operations across different problems. Here's a detailed evaluation:

1. Correctness:

• Problem 1: Correctly demonstrates multiple ways to create a DataFrame from 2D lists.
• Problem 2: Correctly filters countries based on area and population criteria.
• Problem 3: Correctly identifies products that are both low fat and recyclable.
• Problem 4: Correctly finds customers who never ordered using a left merge and filtering.

2. Time Complexity:

• Problems 2-4 all involve DataFrame filtering operations which are O(n) where n is the number of rows.
• The merge operation in Problem 4 is O(n + m) where n and m are the sizes of the DataFrames.

3. Space Complexity:

• All solutions create new DataFrames, resulting in O(n) space complexity where n is the size of the input.

4. Code Quality:

• Good use of comments explaining the code.
• Consistent formatting and style.
• Could improve by adding docstrings to functions explaining their purpose and return values.
• In Problem 4, the comment about 'left_on' and 'right_on' is helpful but could be more detailed.

5. Efficiency:

• All solutions are efficient for their respective problems.
• No major optimizations needed, though in Problem 4, an alternative approach using ~customers['id'].isin(orders['customerId']) might be slightly more readable.

Areas for Improvement:

• Add docstrings to functions.
• Consider alternative approaches for Problem 4 that might be more readable.
• Could add edge case handling (e.g., empty DataFrames) though the current solutions handle them gracefully.