Data Science with Python Certification Data Science with Python NumPy Array Manipulation 3

Question 1

When you index a NumPy array with another integer array (fancy indexing), does the result share memory with the original?

Accepted Answer

No, it returns a copy

Answer

Fancy (advanced) indexing always returns a copy, not a view, of the selected elements.

Question 2

What is the output of `np.zeros((3,3), dtype=int).diagonal()`?

Accepted Answer

array([0, 0, 0])

Answer

`diagonal()` extracts main-diagonal elements; a 3×3 zero matrix has three zeros on its diagonal.

Question 3

Which method returns a flattened copy of the array, always regardless of memory layout?

Accepted Answer

array.flatten()

Answer

`flatten()` always returns a new 1-D copy, while `ravel()` may return a view if possible.

Question 4

What does `np.roll(a, shift, axis)` do?

Accepted Answer

Rolls array elements along the given axis, wrapping around boundaries

Answer

`np.roll` circularly shifts elements along the specified axis, wrapping elements that go past the edge.

Question 5

What is the shape of `np.expand_dims(np.array([1,2,3]), axis=0)`?

Accepted Answer

(1, 3)

Answer

`expand_dims` with `axis=0` inserts a new axis at position 0, changing shape from (3,) to (1,3).

Data Science with Python Certification Practice Test