Pandas1 Completed

[HaswathaSridharan]

No description provided.

[super30admin]

1. Correctness:

   • Problem 1: The solution correctly creates a DataFrame from a 2D list with specified column names. This is a standard approach.
   • Problem 2: The solution correctly filters countries based on area and population criteria, returning the required columns.
   • Problem 3: The solution correctly filters products that are both low fat and recyclable, returning only the product_id.
   • Problem 4: The solution correctly identifies customers who never placed an order by checking if their id is not in the orders' customerId column.

2. Time Complexity:

   • Problem 1: O(n) where n is the number of rows in the 2D list, as it needs to process each row once.
   • Problem 2: O(n) where n is the number of rows in the DataFrame, as it performs a linear scan.
   • Problem 3: O(n) where n is the number of rows in the DataFrame, as it performs a linear scan.
   • Problem 4: O(n + m) where n is the number of customers and m is the number of orders, due to the isin operation.

3. Space Complexity:

   • Problem 1: O(n) where n is the number of rows, as it stores the entire DataFrame.
   • Problem 2: O(n) in the worst case if all rows meet the criteria, as it returns a new DataFrame.
   • Problem 3: O(n) in the worst case if all rows meet the criteria, as it returns a new DataFrame.
   • Problem 4: O(n) in the worst case if no customers have placed orders, as it returns a new DataFrame.

4. Code Quality:

   • The code is generally well-structured and readable.
   • Variable names are appropriate and descriptive.
   • The solutions follow standard pandas practices.
   • Minor improvement: In Problem 4, the intermediate variable 'df' could be eliminated by chaining the operations.

5. Efficiency:

   • All solutions are efficient for their respective problems.
   • No major optimizations are needed, as pandas operations are already optimized under the hood.