Data Science with Python Certification Data Science with Python NumPy Array Manipulation 4

Question 1

What is the purpose of `np.einsum('ij,jk->ik', A, B)`?

Accepted Answer

Matrix multiplication of A and B using Einstein summation notation

Answer

The subscript `'ij,jk->ik'` contracts over the shared index j, performing standard matrix multiplication.

Question 2

What does `np.argmax(a, axis=0)` return for a 2-D array?

Accepted Answer

The row index of the maximum value in each column

Answer

`argmax` with `axis=0` reduces along rows, returning the row index where the maximum occurs for each column.

Question 3

Which NumPy function performs element-wise comparison and returns the element-wise minimum?

Accepted Answer

np.minimum(a, b)

Answer

`np.minimum(a, b)` compares two arrays element-wise and returns a new array with the smaller value at each position.

Question 4

What is a structured array in NumPy?

Accepted Answer

An array with a dtype composed of named fields, like a table of records

Answer

Structured arrays have a compound dtype with named fields, enabling each element to hold heterogeneous data types like a database row.

Question 5

How do you make a deep copy of a NumPy array so that modifications don't affect the original?

Accepted Answer

b = a.copy()

Answer

`a.copy()` creates an independent array with its own data buffer; changes to b will not propagate to a.

Data Science with Python Certification Practice Test