Pandas Cleaning and Aggregation | Dataford Interview Questions

Given a raw CSV dataset, write a Pandas script to clean missing values, normalize timestamps, and aggregate transactions by user.

Implement clean_and_aggregate(csv_text), where csv_text contains user_id, amount, and timestamp columns. Ignore rows with missing or invalid required values, trim user IDs, interpret naive timestamps as UTC, convert timezone-aware timestamps to UTC, and return sorted user summaries containing transaction count, total amount, earliest timestamp, and latest timestamp.

Constraints

csv_text contains a header with user_id, amount, and timestamp columns.
There can be up to 100000 transaction rows.
Amounts are decimal numbers when valid.
Timestamps are ISO 8601 strings, optionally with a timezone offset or Z.
Rows with missing or invalid required values must be ignored.
Output users must be sorted lexicographically by user_id.

Function Signature

def clean_and_aggregate(csv_text):

Your question is Pandas Cleaning and Aggregation. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem

Given a raw CSV dataset, write a Pandas script to clean missing values, normalize timestamps, and aggregate transactions by user.

Constraints

csv_text contains a header with user_id, amount, and timestamp columns.
There can be up to 100000 transaction rows.
Amounts are decimal numbers when valid.
Timestamps are ISO 8601 strings, optionally with a timezone offset or Z.
Rows with missing or invalid required values must be ignored.
Output users must be sorted lexicographically by user_id.

Function Signature

def clean_and_aggregate(csv_text):

Your question is Pandas Cleaning and Aggregation. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.