Your question is Pandas Data Cleaning for Messy Data. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you use Python libraries like Pandas to perform data cleaning on messy datasets?
Explain a practical workflow for profiling input data, standardizing column names and types, handling missing and malformed values, removing duplicates, validating business rules, and preserving rejected records for review. Discuss how you would make the process reproducible in an ETL pipeline, including schema checks, logging, output formats, and tests.