Python Data Cleaning Approach | Dataford Interview Questions

Explain how you would approach a data cleaning process in Python.

For this PostgreSQL exercise, apply the same data-cleaning objectives with SQL. Return only the latest valid record for each transaction reference, standardize status values, reject missing or invalid fields, and retain only transactions linked to active customers.

Output

One row per accepted transaction, with transaction_ref, customer_name, amount, transaction_date, and normalized_status.
Exclude duplicate, malformed, non-positive, unmatched, and inactive-customer records.
Order by transaction_ref ascending.

Schema

raw_transactions

Column	Type	Description
raw_idPK	INT	Unique identifier for the raw record
transaction_ref	VARCHAR(30)	Raw transaction reference
customer_ref	VARCHAR(20)	Raw customer reference
amount_text	VARCHAR(30)	Transaction amount received as text
transaction_date_text	VARCHAR(20)	Transaction date received as text
status_text	VARCHAR(20)	Raw transaction status
ingested_at	TIMESTAMP	Timestamp when the raw record was ingested

customer_reference

Column	Type	Description
customer_refPK	VARCHAR(20)	Customer reference used for validation
customer_name	VARCHAR(100)	Customer display name
account_status	VARCHAR(20)	Current customer account status

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