Broadcasting for outer products in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an outer product of two 1D arrays using broadcasting.

NumPy

import numpy as np

x = np.array([1, 2, 3])
y = np.array([4, 5, 6])
outer = x[:, [1]] * y
print(outer)

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

Areshape(3, 1)

Bnp.newaxis

CNone

Dreshape(1, 3)

Attempts:

3 left

💡 Hint

Common Mistakes

Using reshape(1, 3) instead of adding a new axis.

Trying to multiply without changing dimensions, causing shape errors.

Using np.newaxis adds a new axis to x, making it a column vector. This allows broadcasting to multiply it with y as a row vector, producing the outer product.

2fill in blank

medium

Complete the code to compute the outer product of two arrays using broadcasting with shape manipulation.

NumPy

import numpy as np

a = np.array([2, 3])
b = np.array([5, 7, 11])
result = a.[1] * b
print(result)

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Areshape(2, 1)

Breshape(1, 2)

Creshape(3, 1)

Dreshape(1, 3)

Attempts:

3 left

💡 Hint

Common Mistakes

Reshaping a incorrectly as (1, 2) which makes it a row vector.

Not reshaping a at all, causing shape mismatch.

Reshaping a to (2, 1) makes it a column vector. Multiplying by b (shape (3,)) broadcasts b as a row vector, producing a (2, 3) outer product.

3fill in blank

hard

Fix the error in the code to correctly compute the outer product using broadcasting.

NumPy

import numpy as np

x = np.array([1, 2, 3])
y = np.array([4, 5, 6])
outer = x.[1] * y
print(outer)

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

Areshape(3, 1)

Breshape(3, 3)

Creshape(1, 3)

Dflatten()

Attempts:

3 left

💡 Hint

Common Mistakes

Using reshape(3, 3) which changes the data shape incorrectly.

Using flatten() which does not add a new axis.

Reshaping x to (3, 1) makes it a column vector. This allows broadcasting with y (shape (3,)) to compute the outer product.

4fill in blank

hard

Fill both blanks to create an outer product using broadcasting with reshaping.

NumPy

import numpy as np

p = np.array([7, 8])
q = np.array([1, 2, 3])
outer_product = p.[1] * q.[2]
print(outer_product)

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Areshape(2, 1)

Breshape(1, 3)

Cflatten()

Dreshape(3, 1)

Attempts:

3 left

💡 Hint

Common Mistakes

Using flatten() which removes dimensions instead of reshaping.

Swapping the shapes causing broadcasting errors.

Reshape p to (2, 1) to make it a column vector, and q to (1, 3) to make it a row vector. Multiplying them broadcasts to a (2, 3) outer product.

5fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = {word: {BLANK_2}} for word in words if {{BLANK_2}}

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

Blen(word)

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Adding a colon after the word key in the dictionary comprehension.

Using the word itself as the value instead of its length.

Not filtering words by length.

The dictionary comprehension uses word as the key (no extra syntax needed, so blank 1 is empty), len(word) as the value, and filters words with length greater than 3.