Why can reshape and transpose create arrays that are no longer C-contiguous, and why does that matter?
PICTURE THIS: ARRAY IN MEMORY
Index starts at 0. Scan once for max — O(n).
Simple meaning
Those operations can change strides without moving data, producing Fortran-order or non-contiguous layouts.
WHY — NumPy instead of guessing?
Why interviewers care about NumPy:
on NumPy.
the situation, the default choice, and one exception - that reads as experience.
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:
- 1Those operations can change
strides without moving data, producing Fortran-order or non-contiguous layouts.
- 2Some C extensions and
BLAS calls then copy internally or run slower.
- 3For large pipelines, check
flags and use ascontiguousarray at API boundaries when libraries require it.
- 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
Those operations can change strides without moving data, producing Fortran-order or non-contiguous layouts. Some C extensions and BLAS calls then copy internally or run slower.