Added three problems

[hinduja97]

No description provided.

[super30admin]

The student has provided solutions to three different pandas problems. Here's an evaluation of each:

1. problem2.py (big_countries):

• Correctness: The solution correctly filters countries based on area or population criteria.
• Time Complexity: O(n) where n is the number of rows, which is optimal for this filtering operation.
• Space Complexity: O(m) where m is the number of matching rows, as it creates a new DataFrame.
• Code Quality: Good, but the print statement should be removed as it's not needed for the solution.
• Efficiency: The solution is efficient for the given task.

2. problem3.py (find_products):

• Correctness: Correctly identifies products that are both low-fat and recyclable.
• Time Complexity: O(n) for filtering, which is optimal.
• Space Complexity: O(m) where m is the number of matching products.
• Code Quality: Good, though the commented line could be removed for cleanliness.
• Efficiency: The solution is efficient and straightforward.

3. problem4.py (find_customers):

• Correctness: Both implementations correctly find customers who haven't placed orders.
• Time Complexity:
  • First implementation: O(n) for the isin operation
  • Second implementation: O(n) for the merge operation
• Space Complexity: O(m) where m is the number of matching customers.
• Code Quality: The file contains two implementations of the same function, which is confusing. Only one should be kept.
• Efficiency: Both approaches are valid, with the merge approach potentially being more readable.

General improvements needed:

1. Remove debug print statements (problem2.py)
2. Remove commented code (problem3.py)
3. Choose one implementation and remove the duplicate (problem4.py)
4. Add docstrings to explain each function's purpose
5. Consider adding type hints for return values

The solutions are generally correct and efficient, but would benefit from some cleanup and documentation.