feat" problem 3,4,2 added

[Tejas274]

No description provided.

[super30admin]

1. Correctness:

   • Problem 2 (big_countries): The solution correctly filters countries based on area or population criteria and returns the required columns. This appears correct.
   • Problem 3 (find_products): The solution correctly filters products that are both low fat and recyclable, returning only the product_id as required.
   • Problem 4 (find_customers): The solution correctly identifies customers who never placed orders using a left merge and null check on customerId.

2. Time Complexity:

   • Problem 2: O(n) where n is the number of rows in the DataFrame, due to the filtering operation.
   • Problem 3: O(n) for the same reason as above.
   • Problem 4: O(n + m) for the merge operation where n and m are the sizes of customers and orders DataFrames respectively, plus O(n) for the filtering.

3. Space Complexity:

   • Problem 2: O(n) in the worst case if all rows meet the criteria.
   • Problem 3: O(n) in the worst case if all products meet the criteria.
   • Problem 4: O(n + m) for the merged DataFrame.

4. Code Quality:

   • The code is generally clean, concise, and follows pandas best practices.
   • Variable names are appropriate and the logic is straightforward.
   • The solutions are well-structured with proper spacing and imports.
   • One minor improvement could be adding docstrings to explain the functions' purposes.

5. Efficiency:

   • All solutions are efficient for their respective problems.
   • For Problem 4, an alternative approach using ~customers['id'].isin(orders['customerId']) might be slightly more memory efficient as it avoids creating a merged DataFrame, but the current solution is perfectly acceptable.