Moderate NumPy Question 103 of 220

How does boolean indexing work on a NumPy array?

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

A boolean mask of the same shape selects positions where the mask is True, returning a one-dimensional collection of those values for a 1D array.

WHY — NumPy instead of guessing?

Why interviewers care about NumPy:

This is a process

question about NumPy.

Panels listen for order,

trade-offs, and what you would actually do on a Data Science project - not buzzwords.

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
A boolean mask of
the same shape selects positions where the mask is True, returning a one-dimensional collection of those values for a 1D array.
2
You can combine masks
with &, |, and ~, using parentheses because of operator precedence.
3
Boolean indexing copies data,
unlike some slice views.
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

“You can combine masks with &, |, and ~, using parentheses because of operator pr”

Break into beats

Youcancombinemaskswithand

Speaking order

2987408337471632900

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

Key takeaway

A boolean mask of the same shape selects positions where the mask is True, returning a one-dimensional collection of those values for a 1D array. You can combine masks with &, |, and ~, using parentheses because of operator precedence.