Done Pandas1

[sakshiasati17]

No description provided.

[super30admin]

1. Correctness:

• BigCountries.py: The solution correctly filters countries with population >= 25 million or area >= 3 million square km and returns the required columns.
• CustomerWhoNeverOrder.py: The solution correctly identifies customers who never placed orders using a left merge and filtering for null customerId values.
• MakeAPandasDataFrameWithTwo-dimensionalList.py: The solution correctly creates a DataFrame from a 2D list with specified column names.
• RecyclableAndLowFatProducts.py: The solution correctly filters products that are both low fat and recyclable.

2. Time Complexity:

• All solutions use pandas operations which are generally efficient. The merge operation in CustomerWhoNeverOrder.py is O(n) where n is the number of rows in the larger DataFrame.

3. Space Complexity:

• All solutions create new DataFrames which requires additional space, but this is typical for pandas operations. The space complexity is reasonable for the given problems.

4. Code Quality:

• The code is generally well-structured and readable.
• Variable names could be more descriptive (e.g., 'big_count' could be 'big_countries_df').
• Consistent indentation and spacing is maintained.
• The MakeAPandasDataFrameWithTwo-dimensionalList.py file includes a print statement which might not be necessary if this is meant to be a function.

5. Efficiency:

• The solutions are efficient for their respective problems.
• In CustomerWhoNeverOrder.py, using merge is appropriate, but an alternative could be to use ~customers['id'].isin(orders['customerId']) which might be more memory efficient.

Areas for Improvement:

• Add docstrings to functions to explain their purpose and parameters.
• Consider adding type hints for return values in functions.
• In RecyclableAndLowFatProducts.py, the column name 'product_id' should be consistent with the problem's requirements (if any).