Clean Messy Data With Pandas or Tidyverse | Dataford Interview Questions

How do you clean and manipulate messy, real-world data in Python or R using Pandas, NumPy, or tidyverse for Amplify?

For this SQL version, apply equivalent cleaning logic to the provided Amplify event data. Exclude unusable records and summarize the remaining events by normalized channel.

Output

One row per cleaned channel, with cleaned_channel, valid_event_count, and total_duration_seconds.
Exclude rows with blank user IDs, missing durations, or negative durations.
Order by total duration descending, then cleaned channel ascending.

Schema

amplify_events

Column	Type	Description
event_idPK	INT	Unique event identifier
user_id	VARCHAR(50)	Identifier of the user associated with the event
channel	VARCHAR(50)	Source channel recorded for the event
duration_seconds	INT	Event duration in seconds

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