1D and 2D broadcasting in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add a 1D array to each row of a 2D array using broadcasting.

NumPy

import numpy as np
arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
arr_1d = np.array([10, 20, 30])
result = arr_2d + [1]
print(result)

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Aarr_1d

Barr_2d

Cnp.array([1, 2])

Dnp.array([[10], [20], [30]])

Attempts:

3 left

💡 Hint

Common Mistakes

Adding the 2D array to itself instead of the 1D array.

Using a 2D array with incompatible shape for broadcasting.

Adding arr_1d to arr_2d broadcasts the 1D array across each row of the 2D array.

2fill in blank

medium

Complete the code to multiply a 2D array by a 1D array using broadcasting along columns.

NumPy

import numpy as np
arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
arr_1d = np.array([2, 3])
result = arr_2d * [1]
print(result)

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Aarr_1d.reshape(3, 1)

Barr_1d.reshape(2, 1)

Carr_1d.reshape(1, 2)

Darr_1d

Attempts:

3 left

💡 Hint

Common Mistakes

Multiplying without reshaping causing shape mismatch error.

Reshaping to wrong dimensions that don't broadcast correctly.

Reshaping arr_1d to (2,1) allows broadcasting along columns to multiply each row by the corresponding element.

3fill in blank

medium

Complete the code to add a 1D array to each column of a 2D array using broadcasting.

NumPy

import numpy as np
arr_2d = np.array([[1, 2], [3, 4], [5, 6]])
arr_1d = np.array([10, 20, 30])
result = arr_2d + [1]
print(result)

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

Aarr_1d.reshape(3, 1)

Barr_1d

Carr_1d.reshape(1, 3)

Darr_1d.reshape(3, 2)

Attempts:

3 left

💡 Hint

Common Mistakes

Using arr_1d directly, causing a broadcasting error.

Reshaping to dimensions that don't align properly.

Reshaping arr_1d to (3,1) broadcasts it across the columns (second dimension) of arr_2d.

4fill in blank

hard

Fix the error in the code to subtract a 1D array from each column of a 2D array using broadcasting.

NumPy

import numpy as np
arr_2d = np.array([[5, 10, 15], [20, 25, 30]])
arr_1d = np.array([1, 2])
result = arr_2d - [1]
print(result)

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Aarr_1d

Barr_1d.reshape(1, 2)

Carr_1d.reshape(3, 1)

Darr_1d.reshape(2, 1)

Attempts:

3 left

💡 Hint

Common Mistakes

Using the 1D array without reshaping causing broadcasting error.

Reshaping to (1, 2) which aligns incorrectly for subtraction.

Reshaping arr_1d to (2,1) aligns it with the rows of arr_2d for broadcasting subtraction along columns.

5fill in blank

hard

Complete the code to compute the outer product of two arrays using broadcasting (no explicit reshape needed).

NumPy

import numpy as np
a = np.array([[1], [2]])
b = np.array([[10, 20, 30]])
result = a * [1]
print(result)

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

Ab.reshape(3, 1)

Ba

Cb

Dnp.outer(a[:,0], b[0,:])

Attempts:

3 left

💡 Hint

Common Mistakes

Attempting to reshape when not needed.

Confusing the order of multiplication or using np.outer explicitly.

The shapes (2,1) and (1,3) are compatible for broadcasting: the 1s are expanded to match, resulting in (2,3) outer product multiplication.