Website Activity Analytics: Unique Users + Session Time

Given website action records with `user_id`, timestamp, and activity, compute per-activity unique-user counts and average time spent on the site per user in seconds. The screen paired this Python task with a lighter SQL section and emphasized edge-case discussion over perfect final code. Requirements Input is a collection of user-activity records. Each record contains: user_id: numeric user identifier timestamp: timestamp string such as 2024-07-26 10:00:00 activity: action name such as login, view, purchase, or logout Implement two analytics tasks: Return a mapping from each activity type to the number of unique users who performed that activity. Calculate the average time spent on the website per user in seconds. Clarify session semantics before coding. The prompt uses login/logout-style events, but the exact treatment of users without an explicit logout, multiple sessions per user, and out-of-order records must be agreed with the interviewer. Examples user_activity = [ {'user_id': 1, 'timestamp': '2024-07-26 10:00:00', 'activity': 'login'}, {'user_id': 2, 'timestamp': '2024-07-26 10:05:00', 'activity': 'login'}, {'user_id': 1, 'timestamp': '2024-07-26 10:10:00', 'activity': 'view'}, {'user_id': 1, 'timestamp': '2024-07-26 10:15:00', 'activity': 'purchase'}, {'user_id': 2, 'timestamp': '2024-07-26 10:20:00', 'activity': 'view'}, {'user_id': 3, 'timestamp': '2024-07-26 10:25:00', 'activity': 'login'}, {'user_id': 1, 'timestamp': '2024-07-26 10:30:00', 'activity': 'logout'}, ] Notes SQL in the same screen was described as simple and covered joins / aggregation style work, but the exact SQL prompt did not leak. The interviewer accepted a strong draft when all edge cases were discussed, even though the final code did not cover every case. Spend time up front on timestamp parsing, ordering, repeated login/logout events, and users with missing terminal actions. Preparation Practice writing small Python analytics functions in a plain document without IDE help. Drill grouping records by user and by activity with dictionaries / sets. Rehearse explaining incomplete edge-case handling and dry-running a small event stream.