Why vectorized operations matter in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array from a Python list.

NumPy

import numpy as np
arr = np.[1]([1, 2, 3, 4, 5])

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

Aarray

Blist

Cmatrix

Drange

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' instead of 'array' will cause an error because NumPy has no 'list' function.

Using 'matrix' creates a matrix object, not a general array.

Using 'range' is a Python built-in, not a NumPy function.

The np.array function converts a Python list into a NumPy array, which allows vectorized operations.

2fill in blank

medium

Complete the code to add 5 to every element in the NumPy array using vectorized operations.

NumPy

import numpy as np
arr = np.array([1, 2, 3])
result = arr [1] 5

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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 '/' divides instead of adding.

Using the + operator adds 5 to each element of the array in a vectorized way.

3fill in blank

hard

Fix the error in the code to multiply two NumPy arrays element-wise.

NumPy

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result = arr1 [1] arr2

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A/

B-

C*

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' adds arrays instead of multiplying.

Using '-' subtracts arrays.

Using '/' divides arrays.

The * operator performs element-wise multiplication between two NumPy arrays.

4fill in blank

hard

Fill both blanks to create a vectorized operation that squares each element and then filters elements greater than 10.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
squared = arr [1] 2
filtered = squared[squared [2] 10]

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

A**

B>

C<

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' instead of '**' will multiply by 2, not square.

Using '<' filters elements less than 10, not greater.

Use ** to square elements and > to filter elements greater than 10.

5fill in blank

hard

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

NumPy

words = ['data', 'is', 'fun', 'and', 'cool']
lengths = { [1] : [2] for [3] in words if len([3]) > 3 }

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

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'item' instead of 'word' causes a NameError.

Using the wrong variable name in the loop breaks the comprehension.

The comprehension uses word as the key, len(word) as the value, and iterates over word in words.