Added Pandas1 Q1–Q4 solutions

[arushi2106]

No description provided.

[super30admin]

The student has provided solutions to four different problems using pandas, demonstrating a good understanding of DataFrame operations. Here's a detailed evaluation of each solution:

1. Big_Countries.ipynb:

   • Correctness: The solution correctly filters countries based on area or population criteria.
   • Time Complexity: O(n) where n is the number of rows in the DataFrame (due to the filtering operation).
   • Space Complexity: O(m) where m is the number of rows that meet the criteria (new DataFrame is created).
   • Code Quality: The code is clean and straightforward. The use of chained operations is appropriate here.
   • Efficiency: No major optimizations needed for this simple filtering operation.

2. Customers_Who_Never_Order.ipynb:

   • Correctness: The solution correctly identifies customers who never placed orders using a left merge and NA check.
   • Time Complexity: O(n + m) for the merge operation where n and m are sizes of the DataFrames.
   • Space Complexity: O(n + m) for the merged DataFrame.
   • Code Quality: Good use of merge operation and column renaming. The logic is clear.
   • Efficiency: Could potentially use ~customers['id'].isin(orders['customerId']) for better performance with large datasets.

3. Recylable_products.ipynb:

   • Correctness: Correctly filters products that are both low fat and recyclable.
   • Time Complexity: O(n) for the filtering operation.
   • Space Complexity: O(m) where m is the number of matching products.
   • Code Quality: Simple and effective. The boolean conditions are clearly expressed.
   • Efficiency: No optimizations needed for this straightforward filtering.

4. pandas1.ipynb:

   • Correctness: Correctly creates a DataFrame from a list of lists.
   • Time Complexity: O(n) where n is the number of elements in the list.
   • Space Complexity: O(n) for the resulting DataFrame.
   • Code Quality: Basic but correct demonstration of DataFrame creation. Could benefit from more context or problem statement.

General Observations:

• The student demonstrates solid pandas skills across different types of operations.
• All solutions follow Python naming conventions and are well-structured.
• Type hints are used appropriately in function definitions.
• The solutions handle the core requirements effectively.
• For production code, adding docstrings to explain the functions' purposes would be beneficial.

Areas for Improvement:

• Consider adding comments or docstrings to explain complex operations.
• For the customers problem, the alternative solution using isin() might be more efficient for very large datasets.
• The pandas1.ipynb file seems to be just a demonstration rather than solving a specific problem.