Performance tips and vectorization in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

SciPy

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

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

Avector

Blist

Cmatrix

Darray

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' instead of 'array' causes an error.

Using 'matrix' creates a different object type.

Use np.array to convert a Python list into a NumPy array for vectorized operations.

2fill in blank

medium

Complete the code to compute the element-wise square of a NumPy array.

SciPy

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

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

A**

B*

C^

D//

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' multiplies arrays element-wise but does not square.

Using '^' is a bitwise XOR, not exponentiation.

The ** operator raises each element to the power given, here squaring each element.

3fill in blank

hard

Fix the error in the code to compute the dot product of two arrays using SciPy.

SciPy

from scipy import [1]
import numpy as np
arr1 = np.array([1, 2])
arr2 = np.array([3, 4])
dot_product = [1].dot(arr1, arr2)

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

Alinalg

Bintegrate

Coptimize

Dstats

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'stats' or 'optimize' modules which do not have dot product functions.

Not importing the correct module causes AttributeError.

The dot function is in the scipy.linalg module for linear algebra operations.

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.

SciPy

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

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

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word > 3' compares strings to numbers, causing errors.

Not using len(word) for length calculation.

The dictionary comprehension maps each word to its length using len(word). The condition 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 if length is greater than 2.

SciPy

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

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

Aword.upper()

Blen(word)

Clen(word) > 2

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of uppercase.

Filtering with incorrect length condition.

The comprehension maps each word in uppercase to its length, filtering only words longer than 2 characters.