What’s the worst 'bad data term' you’ve encountered in your work? or How do you handle a 'bad data te

Ugh, bad data terms are the worst. Had a project where "Male/Female" was mixed with "M/F", "1/0", and even "Boy/Girl".

Spent more time cleaning than analyzing.

Now I always run a quick `value_counts()` in pandas first. Saves so much headache.