View vs copy behavior in NumPy - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a view of the original NumPy array.

NumPy

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

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

A.view()

B.copy()

C.reshape(2, 2)

D.flatten()

Attempts:

3 left

💡 Hint

Common Mistakes

Using .copy() instead of .view() creates a separate array, not a view.

Using .reshape() changes shape but does not guarantee a view.

Using .view() creates a view of the original array, sharing the same data.

2fill in blank

medium

Complete the code to modify the view and see the change in the original array.

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40])
view_arr = arr.view()
view_arr[1] = 99
result = arr

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

A[1]

B[0]

C[2]

D[3]

Attempts:

3 left

💡 Hint

Common Mistakes

Modifying an index other than 0 and expecting no change in original array.

Using .copy() instead of .view() so changes don't reflect.

Changing view_arr[0] modifies the original array because the view shares data.

3fill in blank

hard

Fix the error in the code to ensure modifying the copy does NOT change the original array.

NumPy

import numpy as np
arr = np.array([5, 6, 7, 8])
copy_arr = arr[1]
copy_arr[0] = 100
result = arr

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

A.copy()

B.view()

C.reshape(2, 2)

D[:]

Attempts:

3 left

💡 Hint

Common Mistakes

Using .view() instead of .copy() causes changes to reflect in original array.

Using slicing [:] creates a view, not a copy.

Using .copy() creates a new array independent of the original, so changes do not affect it.

4fill in blank

hard

Fill both blanks to create a dictionary with word lengths only for words longer than 3 letters.

NumPy

words = ['cat', 'house', 'dog', 'elephant']
lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword.startswith('e')

Dword == 'dog'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Filtering words incorrectly with wrong condition.

The dictionary comprehension uses len(word) for values and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values for words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
result = [1]: [2] for w in words if [3]

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

Aw.upper()

Bw

Clen(w) > 3

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase.

Not filtering words by length correctly.

The dictionary comprehension uses uppercase words as keys, original words as values, and filters words longer than 3 letters.