problem 1 added

[Tejas274]

No description provided.

[super30admin]

The student's solution demonstrates a good understanding of pandas operations for grouping and aggregating data. Here are the key points:

1. Correctness: The solution appears correct for grouping sold products by date, counting unique products, and creating a sorted comma-separated list of products. However, without seeing the exact problem statement, it's hard to verify all requirements.

2. Time Complexity: The groupby operation is O(n), and the aggregation operations (nunique and join) are O(m) per group where m is the number of elements in each group. The final sort is O(k log k) where k is the number of unique dates. Overall reasonable for typical datasets.

3. Space Complexity: The solution creates a new DataFrame with one row per unique date, which is efficient. The products string concatenation could potentially use more memory for large groups.

4. Code Quality: The code is clean and follows good practices:

   • Proper use of pandas operations
   • Clear variable naming
   • Proper indentation and formatting
   • Includes type hints (good practice)

5. Efficiency: One potential improvement could be to use 'unique' instead of 'set' in the lambda function since pandas' unique() might be more efficient than converting to a set. Also, the sort_values could potentially be combined with the groupby operation.

Potential edge cases to consider:

• Empty input DataFrame
• Dates with only one product
• Very large product lists that might make the comma-separated string unwieldy