solution of problem 1

[avishwak]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of the problem and provides two approaches to solve it. Here's a detailed evaluation:

1. Correctness:

   • Both solutions appear to correctly solve the problem of grouping sold products by date, counting unique products, and creating a comma-separated list of sorted product names.
   • The first solution uses a manual approach with dictionaries and sets, while the second leverages pandas' built-in functions, which is more idiomatic for pandas operations.

2. Time Complexity:

   • Solution 1: O(n log n) due to the sorting of products for each date, where n is the number of unique products per date.
   • Solution 2: Also O(n log n) for similar reasons, but pandas' groupby and aggregation operations are optimized and likely more efficient in practice.

3. Space Complexity:

   • Solution 1: O(n) for storing the dictionary and temporary lists.
   • Solution 2: O(n) as well, but likely more memory-efficient due to pandas' internal optimizations.

4. Code Quality:

   • The code is well-structured and readable.
   • The student provides helpful comments explaining their thought process and pandas nuances.
   • The variable names are descriptive (e.g., dict, s_date, pdt could be slightly more descriptive).
   • The second solution is more pandas-idiomatic and preferred for production code.

5. Efficiency:

   • The second solution is more efficient and cleaner, leveraging pandas' optimized operations.
   • The first solution could be improved by using defaultdict from collections to simplify the dictionary initialization.

Additional suggestions:

• The docstring could be improved to clearly state the problem and the function's purpose.
• The variable name dict could be renamed to something more descriptive like date_to_products.
• The comments about pandas operations are excellent but could be moved to a separate documentation section if this were production code.