Pandas 1 Completed

[alokusw2]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of creating Pandas DataFrames from 2D lists and solving basic DataFrame filtering problems. Here are some specific observations:

Strengths:

1. The solution covers multiple methods for creating DataFrames (pd.DataFrame, from_records, from_dict) which shows comprehensive knowledge.
2. The filtering logic for the LeetCode problems (Big Countries, Recyclable Products, Customers Who Never Order) is correct and efficient.
3. The code is generally readable with appropriate comments and structure.

Areas for Improvement:

1. There's a typo in the first example: pd.Dataframe should be pd.DataFrame (capital F) and there's a missing quote in columns=['Tag', 'number].
2. The solution could benefit from more explanation of why certain approaches are used (e.g., why merge with how='left' for the customer orders problem).
3. For the customer orders problem, it might be more memory efficient to use ~customers['id'].isin(orders['customerId']) instead of a merge operation.
4. Some examples could include edge case handling (e.g., empty DataFrames or None values).
5. The solution could be better organized with clear separation between different problems/examples.

Time Complexity:

1. The DataFrame creation operations are O(n) where n is the number of elements.
2. The filtering operations are O(n) for scanning through the DataFrame.
3. The merge operation is O(n*m) in worst case, but could be optimized as mentioned above.

Space Complexity:

1. All operations create new DataFrames, so space complexity is O(n) for the new DataFrames.
2. The merge operation creates a potentially large intermediate DataFrame.

Code Quality:

1. Generally good, but could be improved with consistent formatting (some examples have extra whitespace).
2. Variable names are clear and appropriate.
3. Missing docstrings for the functions would be helpful.