Pandas1: Problem Solutions_RN32MAY2025DS

[Feminto]

No description provided.

[super30admin]

The student's solutions demonstrate a good understanding of pandas DataFrame operations and filtering. Here's a detailed evaluation:

1. Correctness:

• BigCountries.py: Correctly filters countries based on area or population criteria.
• CustomersWhoNeverOrder.py: Accurately identifies customers who haven't placed orders.
• MakeAPandasDataframeWithTwo-DimensionalLists.py: Shows multiple correct ways to create DataFrames from 2D lists.
• RecyclableAndLowFatProducts.py: Properly filters products meeting both conditions.

2. Time Complexity:
   All solutions use standard pandas operations which are generally O(n) for these filtering operations, which is optimal.

3. Space Complexity:
   The solutions create new DataFrames for results, which is O(m) where m is the size of the result set. This is reasonable for these problems.

4. Code Quality:

• Generally clean and readable code
• Good use of pandas idioms
• Could improve with:
  • Consistent docstring documentation
  • More descriptive variable names (e.g., 'df' could be more specific)
  • In MakeAPandasDataframeWithTwo-DimensionalLists.py, the examples could be better organized as separate functions

5. Efficiency:
   The solutions are already efficient for their problem sizes. One potential improvement would be to use .loc[] for more explicit indexing in some cases.

Edge Cases to Consider:

• Empty input DataFrames
• Missing values in filter columns
• Case sensitivity in string comparisons (though not relevant in these specific problems)