Matrix multiplication with @ operator in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to multiply two matrices using the @ operator.

NumPy

import numpy as np
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
result = A [1] B
print(result)

Drag options to blanks, or click blank then click option'

A*

B+

C-

D@

Attempts:

3 left

💡 Hint

Common Mistakes

Using * instead of @ for matrix multiplication.

Using + or - operators which perform addition or subtraction.

The @ operator performs matrix multiplication in numpy.

2fill in blank

medium

Complete the code to multiply matrix A by matrix B and store the result in variable C.

NumPy

import numpy as np
A = np.array([[2, 0], [1, 3]])
B = np.array([[1, 4], [2, 5]])
C = A [1] B
print(C)

Drag options to blanks, or click blank then click option'

A//

B**

C@

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using * which multiplies elements individually.

Using // or ** which are not valid for matrix multiplication.

The @ operator correctly multiplies matrices A and B.

3fill in blank

hard

Fix the error in the code to correctly multiply matrices A and B.

NumPy

import numpy as np
A = np.array([[1, 2, 3], [4, 5, 6]])
B = np.array([[7, 8], [9, 10], [11, 12]])
result = A [1] B
print(result)

Drag options to blanks, or click blank then click option'

A-

B@

C+

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using * which causes shape mismatch error.

Using + or - which are invalid for matrix multiplication.

The @ operator performs matrix multiplication correctly for these shapes.

4fill in blank

hard

Fill both blanks to create a matrix multiplication and print the shape of the result.

NumPy

import numpy as np
A = np.array([[1, 0], [0, 1]])
B = np.array([[4, 1], [2, 2]])
result = A [1] B
print(result[2])

Drag options to blanks, or click blank then click option'

A@

B*

C.shape

D.size

Attempts:

3 left

💡 Hint

Common Mistakes

Using * instead of @ for multiplication.

Using .size instead of .shape to get dimensions.

Use @ for matrix multiplication and .shape to get the dimensions of the result.

5fill in blank

hard

Fill all three blanks to multiply matrices and extract the element at row 1, column 0 of the result.

NumPy

import numpy as np
A = np.array([[3, 5], [7, 9]])
B = np.array([[2, 4], [6, 8]])
result = A [1] B
value = result[2][3]
print(value)

Drag options to blanks, or click blank then click option'

A@

B[1, 0]

D[1][0]

Attempts:

3 left

💡 Hint

Common Mistakes

Using * instead of @ for multiplication.

Using a single bracket with comma inside for indexing.

Use @ for matrix multiplication and [1][0] to access the element at row 1, column 0.