Data Science with Python Certification Data Science with Python NumPy Array Manipulation 5

Question 1

What does `np.lib.stride_tricks.as_strided` allow you to do?

Accepted Answer

Create a view of an array with custom shape and strides, enabling advanced windowing operations

Answer

`as_strided` gives direct control over strides and shape, making it possible to create sliding-window views without copying data.

Question 2

Which NumPy function can be used to split an array into multiple sub-arrays along an axis?

Accepted Answer

np.array_split

Answer

`np.array_split` splits an array into N roughly equal sub-arrays, allowing unequal splits unlike `np.split`.

Question 3

What is the difference between `np.dot(A, B)` and `A @ B` for 2-D arrays?

Accepted Answer

They are equivalent for 2-D arrays (both perform matrix multiplication)

Answer

For 2-D arrays, `np.dot(A, B)` and `A @ B` (matmul operator) both perform matrix multiplication and give identical results.

Question 4

What does `np.pad(a, pad_width=1, mode='constant', constant_values=0)` do to a 2-D array?

Accepted Answer

Adds a border of zeros one element wide around the array

Answer

`np.pad` with `mode='constant'` and `constant_values=0` adds one row/column of zeros to every edge of the 2-D array.

Question 5

What is the result of `np.nonzero(np.array([0, 3, 0, 7, 0]))`?

Accepted Answer

(array([1, 3]),)

Answer

`np.nonzero` returns a tuple of arrays (one per dimension) containing the indices of non-zero elements; for 1-D input it's a tuple with one array.

Data Science with Python Certification Practice Test