Easy NumPy Question 31 of 220

What does numpy.mean do, and what should you watch for with missing values?

Data Science track · Speak this in 60–90 seconds · Faridabad & Delhi NCR

PICTURE THIS: ARRAY IN MEMORY

01234

Index starts at 0. Scan once for max — O(n).

Simple meaning

numpy.mean computes the arithmetic mean along an axis of an array.

WHY — NumPy instead of guessing?

Why interviewers care about NumPy:

NumPy questions separate people

who only read docs from people who shipped.

Keep it short, concrete,

and tied to Data Science work.

Stay structured

Name the idea, why it exists, then one short example.

Close cleanly

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:

1
numpy.mean computes the arithmetic
mean along an axis of an array.
2
NaN values propagate, so
a single NaN can make the whole mean NaN unless you use nanmean.
3
Always decide whether missing
values should be skipped, imputed, or treated as a signal before averaging.
4
Give an example
One tiny concrete case you can say aloud.
5
Common mistake
What juniors usually get wrong.
6
Close
When you pick this over the alternative.

EXAMPLE — See it in action

Here's a short line you can speak, broken into clear beats:

Say this line

“NaN values propagate, so a single NaN can make the whole mean NaN unless you use”

Break into beats

NaNvaluespropagatesoasingle

Speaking order

2987408337471632900

Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.

Key takeaway

numpy.mean computes the arithmetic mean along an axis of an array. NaN values propagate, so a single NaN can make the whole mean NaN unless you use nanmean.