Completed Pandas1

[kaaviyavarrshini]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of pandas operations and DataFrame manipulations. Here's a detailed evaluation:

1. Problem1_2DList.py:

   • Correctness: The solution correctly converts a 2D list to a DataFrame, though using from_dict() with zip is unconventional. The standard approach would be pd.DataFrame(data, columns=columns).
   • Code Quality: The code is readable but could be simplified using the standard DataFrame constructor.

2. Problem2_BigCountry.py:

   • Correctness: The solution correctly filters countries based on area and population criteria.
   • Efficiency: The filtering operation is efficient with O(n) time complexity where n is the number of rows.
   • Code Quality: Well-structured and concise.

3. Problem3_Recyclable.py:

   • Correctness: Correctly filters products that are both low-fat and recyclable.
   • Efficiency: The filtering operation is efficient with O(n) time complexity.
   • Code Quality: Clean and straightforward.

4. Problem4_CustomerNoOrder.py:

   • Correctness: Both solutions correctly identify customers who haven't placed orders.
   • Efficiency:
     • Solution 1 (merge): O(n + m) time complexity where n and m are the sizes of customers and orders.
     • Solution 2 (isin): O(n*m) in worst case, making Solution 1 more efficient for larger datasets.
   • Code Quality:
     • Good to see multiple approaches.
     • The print statement in Solution 1 should be removed as it's not needed for the function's purpose.
     • The column renaming could be done more consistently between solutions.

General improvements:

• Remove debug print statements in production code.
• Consider using more descriptive variable names (e.g., 'df' could be more specific).
• For Problem1, the standard DataFrame constructor would be more straightforward.
• For Problem4, Solution 1 is more efficient for larger datasets.