data-cleaning

Real data arrives dirty. I usually start with missing-value audits, duplicate removal, explicit type conversion, and canonical text cleanup. The trick is to make each cleanup rule reproducible rather than burying it in notebook state. I prefer small,

Regex is not glamorous, but it remains one of the fastest ways to turn messy text into useful structured fields. I use it for IDs, dates, codes, and log fragments before reaching for heavier NLP. The important part is making patterns specific enough t