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This approach involves using SQL joins between the 'Trips' and 'Users' tables to filter out trips involving banned users. Then, group the results by date and count the total and cancelled trips to compute the cancellation rate for each day.
Time Complexity: Not applicable
Space Complexity: Not applicable
1// C++ code is not applicable for database queries.C++ is not typically used for direct database querying tasks like this. A common approach would be to use a database library.
This approach involves extracting trip data and user data, performing filtering operations in the application logic to compute stats, and then using aggregation to calculate the cancellation rate for each day.
Time Complexity: Not applicable
Space Complexity: Not applicable
1import pandas
This solution in Python uses pandas to manipulate in-memory DataFrames (simulating database tables). It joins tables, filters out banned users, marks cancelled trips, and finally computes cancellation rates per day.