Clean and Transform Disjoint Logs | Dataford Interview Questions

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

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

Schema

users

Column	Type	Description
user_idPK	INT	Canonical user identifier
full_name	VARCHAR(100)	User's full name
email	VARCHAR(255)	Canonical user email address

activity_logs

Column	Type	Description
log_idPK	INT	Activity log identifier
user_email	VARCHAR(255)	Raw user email from the activity source
event_timestamp	TIMESTAMP	Timestamp when the activity occurred
event_type	VARCHAR(50)	Raw activity label
duration_minutes	INT	Duration associated with the activity

Your question is Clean and Transform Disjoint Logs. Start with the requirements and the two tables in the Question tab.

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Problem