Clean and Transform Disjoint Logs

Using Pandas, how would you clean, merge, and transform a dataset containing disjointed user activity logs?

Translate this data-wrangling task into PostgreSQL using the users and activity_logs tables. Normalize user identifiers and event labels, exclude unusable events, remove duplicate normalized records, and summarize activity by user and date.

Output

1. One row per user and activity date, including users without valid activity.
2. Return user_id, activity_date, total_events, active_minutes, and primary_activity.
3. Sort by user_id, then activity_date ascending, with missing dates first.