Broadcasting performance implications in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a 2D array of shape (3, 3) filled with ones using NumPy.

NumPy

import numpy as np
arr = np.[1]((3, 3))

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Azeros

Bones

Cempty

Dfull

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.zeros creates an array of zeros, not ones.

Using np.empty creates uninitialized values.

The np.ones function creates an array filled with ones. This is useful to test broadcasting performance with non-zero values.

2fill in blank

medium

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

NumPy

import numpy as np
arr2d = np.ones((3, 3))
arr1d = np.array([1, 2, 3])
result = arr2d [1] arr1d

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A+

B*

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' multiplies instead of adding.

Using '-' subtracts instead of adding.

Using the + operator adds the 1D array to each row of the 2D array by broadcasting.

3fill in blank

hard

Fix the error in the code to correctly broadcast a 1D array to a 2D array for multiplication.

NumPy

import numpy as np
arr2d = np.ones((3, 3))
arr1d = np.array([1, 2, 3])
result = arr2d * arr1d.[1]((3, 1))

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Areshape

Bflatten

Ctranspose

Dravel

Attempts:

3 left

💡 Hint

Common Mistakes

Using flatten or ravel returns a 1D array, no shape change.

Using transpose on 1D array has no effect.

Using reshape changes the shape of arr1d to (3,1) so it broadcasts correctly with arr2d.

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 = ['data', 'science', 'ai', 'ml']
lengths = {word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Cword

Dlen(words)

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' as value instead of length.

Using 'len(words)' which is total words, not per word.

The dictionary comprehension uses len(word) as the value and filters words with length greater than 3.

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 2.

NumPy

words = ['cat', 'dog', 'a', 'bird']
result = { [1]: [2] for w in words if [3] }

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Aw.upper()

Blen(w)

Clen(w) > 2

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using w.lower() instead of uppercase.

Filtering with wrong length condition.

The comprehension maps the uppercase version of each word to its length, filtering words longer than 2 characters.