Completed Pandas10

[Paneripatel]

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[super30admin]

1. Correctness:

   • The solution for "Actors and Directors Who Cooperated At Least Three Times" correctly groups by actor_id and director_id, counts the timestamps, and filters for counts >=3. The output format is also correct.
   • The solution for "Daily Leads and Partners" correctly groups by date_id and make_name, calculates unique leads and partners using nunique, and returns the expected format.
   • The solution for "Group Sold Products by the Date" correctly groups by sell_date, calculates the number of unique products and concatenates them in sorted order. However, the return statement unnecessarily wraps the result in another DataFrame, which is redundant.

2. Time Complexity:

   • All solutions use groupby operations which typically have O(n) time complexity where n is the number of rows in the DataFrame. The operations are efficient for the given problems.

3. Space Complexity:

   • The space complexity is O(m) where m is the number of unique groups formed by the groupby operation. This is optimal for these problems.

4. Code Quality:

   • The code is generally well-structured and readable. Variable names are appropriate.
   • The use of reset_index() after groupby operations is good practice to convert the grouped data back to a DataFrame.
   • The redundant DataFrame wrapping in the "Group Sold Products by the Date" solution could be improved by simply returning the activities DataFrame directly.

5. Efficiency:

   • The solutions are efficient for the given problems. No major optimizations are needed.
   • For the "Group Sold Products by the Date" problem, the lambda function with sorted(x.unique()) is correct but could be replaced with a more explicit function for better readability.