transactions +-----------+----------+------------+------------+ | user_id | order_id | product_id | order_time | +-----------+----------+------------+------------+ | 101 | 5001 | 23 | 2023-09-01 | | 101 | 5001 | 45 | 2023-09-01 | | 102 | 5002 | 23 | 2023-09-01 | | 103 | 5003 | 67 | 2023-09-02 | | 103 | 5003 | 45 | 2023-09-02 | +-----------+----------+------------+------------+ ##### Scenario A video-streaming start-up needs a scalable database; later you are asked to solve quick data-ops questions for an e-commerce team. ##### Question Design a relational database for the video company: what tables and key columns would you create and how are they related? Write an SQL query that returns the pair of products most frequently purchased together. Using Python/Pandas, add a new column to a DataFrame based on logical conditions from existing columns. ##### Hints Think normalization, primary/foreign keys, group by order_id, value_counts or window functions, and vectorized Pandas operations.