Transform clickstream with pandas sessionization

Q: Transform clickstream with pandas sessionization

This is a Data Manipulation (SQL/Python) interview question from OneMain Financial for Data Scientist roles. View the full question and solution on PracHub.

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Question

Given a pandas DataFrame events with columns [user_id:int, ts:str ISO8601 or NaT, url:str, server_log_ts:datetime], build 30-minute inactivity sessions per user: 1) Use server_log_ts to impute ts when ts is missing; 2) Robustly sort events per user with potentially out-of-order rows; 3) Define session_id when the gap > 30 minutes; 4) Compute, for each user, session_count, median_session_duration, and the 95th percentile of pages per session; 5) Ensure the solution works in streaming-sized chunks (cannot load all users into memory). Provide vectorized code sketches and explain correctness on edge cases (exactly-30-minute gaps, duplicated events, DST shifts).

Transform clickstream with pandas sessionization

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