What is a NumPy ndarray?
PICTURE THIS: ARRAY IN MEMORY
Index starts at 0. Scan once for max — O(n).
Simple meaning
An ndarray is a homogeneous, fixed-type, multidimensional array stored in contiguous memory.
WHY — NumPy instead of guessing?
Why interviewers care about NumPy:
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:
- 1An ndarray is a
homogeneous, fixed-type, multidimensional array stored in contiguous memory.
- 2Homogeneous dtypes let operations
run in compiled code rather than per-element Python.
- 3Shape and dtype together
determine memory use and which operations are valid.
- 4Give an example
One tiny concrete case you can say aloud.
- 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
An ndarray is a homogeneous, fixed-type, multidimensional array stored in contiguous memory. Homogeneous dtypes let operations run in compiled code rather than per-element Python.