Completed Pandas1

[DhruvParashar246]

No description provided.

[super30admin]

1. Correctness:

   • Big Countries: Correctly filters countries based on area or population criteria.
   • Customer Who Never Order: Correctly identifies customers who haven't placed orders using a left merge and null check.
   • Recyclable and Low Fat Products: Correctly filters products that are both low fat and recyclable.

2. Time Complexity:

   • All solutions use pandas operations that are generally O(n) for filtering/merging, which is optimal for these problems.

3. Space Complexity:

   • All solutions create new DataFrames but don't use excessive additional space. The space complexity is O(n) in all cases, which is expected.

4. Code Quality:

   • The code is clean, concise, and follows good pandas practices.
   • Variable names are appropriate (df for DataFrame).
   • Could improve by adding docstrings explaining the functions' purposes.

5. Efficiency:

   • All solutions are efficient for their respective problems.
   • For the merge operation in "Customer Who Never Order", using indicator=True might make the intention clearer, though current approach works fine.

Areas for Improvement:

• Adding docstrings would improve documentation.
• In "Big Countries", the column selection could be done before filtering for minor performance improvement.
• In "Customer Who Never Order", the rename operation could be avoided by selecting the column directly with the desired name.