The idea
A DataFrame is a labeled table. Inspect column names, types, example rows and missing counts before cleaning. A numeric-looking string can still have a text type; conversion should expose invalid values rather than silently erase them.
Worked example
A fictional amount column contains 10, 20 and unknown as text. Converting the first two gives numbers, but unknown needs an explicit exception or missing-value policy. Zero would claim a known amount that the source never supplied.
Try it
Create six fictional rows with an amount, date and category. Record expected types and two invalid values. Sketch an inspection report using head, dtypes and isna. Explain how invalid conversion results will be counted and reviewed.