np.expand_dims() and np.squeeze() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add a new axis to the array using np.expand_dims.

NumPy

import numpy as np
arr = np.array([1, 2, 3])
new_arr = np.expand_dims(arr, axis=[1])
print(new_arr.shape)

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A2

B1

C0

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 adds the new axis at the start, changing shape to (1, 3).

Using axis=2 is invalid for 1D array.

Using axis=1 adds a new axis as the second dimension, changing shape from (3,) to (3, 1).

2fill in blank

medium

Complete the code to remove single-dimensional entries from the shape using np.squeeze.

NumPy

import numpy as np
arr = np.array([[[1], [2], [3]]])
squeezed_arr = np.squeeze(arr, axis=[1])
print(squeezed_arr.shape)

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A-1

B1

C2

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to squeeze axis 1 which has size 3 causes an error.

Not specifying axis removes all single dimensions.

Axis 0 has size 1, so squeezing it removes that dimension, changing shape from (1, 3, 1) to (3, 1).

3fill in blank

hard

Fix the error in the code by choosing the correct axis to expand.

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4]])
new_arr = np.expand_dims(arr, axis=[1])
print(new_arr.shape)

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A2

B3

C-1

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=3 causes an IndexError because the array has only 2 dimensions.

Using axis=1 changes shape differently than expected.

For a 2D array with shape (2, 2), axis=2 adds a new axis at the end, resulting in shape (2, 2, 1). Axis 3 is invalid.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Cword.startswith('a')

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using a condition unrelated to length.

The dictionary maps each word to its length using len(word). The condition len(word) > 3 filters words longer than 3 letters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is greater than 3.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = { [1]: [2] for word in words if [3] }

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Aword.upper()

Blen(word)

Clen(word) > 3

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Not filtering words by length.

The dictionary keys are uppercase words using word.upper(). Values are lengths with len(word). The condition filters words longer than 3.