Real transaction data is never clean — and the worst part is that it looks clean. This is a short story from a real dataset (UCI Online Retail: 541,909 e-commerce transactions) about the quietest way to destroy data: silent type coercion. All numbers below come verbatim from an executed notebook.
The head looks perfect
Peek at the first rows of the file and InvoiceNo parses as clean integers — 100% parse rate, full confidence. Any type-inference step, mine included, would call it int64 and move on.
Measure the whole file instead of the head, and the number drops to ~98%. The other 2%: invoice numbers starting with "C" — which in this dataset marks a cancellation. Coerce the column to numeric and every one of them becomes NaN:
Invoice numbers destroyed by numeric coercion: 9,291
