In pandas, how do NaN and None typically differ?
PICTURE THIS: HOW TO EXPLAIN IT
Simple meaning
None is a Python object, while NaN is a floating-point missing marker
WHY — Missing Data instead of guessing?
Why interviewers care about Missing Data:
people who only read docs from people who shipped.
and tied to Data Science work.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1None is a Python
object, while NaN is a floating-point missing marker
- 2pandas often converts None
to NaN in numeric columns.
- 3Object columns can mix
both, which makes isna() still true but can break equality checks.
- 4Prefer pandas missing helpers
over comparing with None by hand.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
None is a Python object, while NaN is a floating-point missing marker pandas often converts None to NaN in numeric columns.