How would you determine the acceptance time for each request, given that each request is accepted two days, five days, or ten days after being sent?

To determine the acceptance time for each request, you first need to capture both the request timestamp and the acceptance timestamp. Once you have these two columns, compute the difference in days. In SQL, you can use the DATEDIFF function: SELECT request_id, DATEDIFF(day, request_time, acceptance_time) AS days_to_accept FROM requests; This returns the number of days between the two events. You can then group by days_to_accept to see how many requests fall into each bucket: SELECT days_to_accept, COUNT(*) AS cnt FROM (SELECT request_id, DATEDIFF(day, request_time, acceptance_time) AS days_to_accept FROM requests) t GROUP BY days_to_accept ORDER BY days_to_accept; If you’re working in Python with Pandas, you can convert the timestamps to datetime objects and subtract them: df['days_to_accept'] = (df['acceptance_time'] - df['request_time']).dt.days. After computing the difference, you can use value_counts() or groupby to analyze the distribution. This approach works regardless of whether the difference is 2, 5, or 10 days, and it scales to millions of rows because both SQL and Pandas operations are vectorized. If you need to handle time zones or partial days, consider using TIMESTAMPDIFF with a finer granularity or normalizing timestamps to UTC before subtraction.