Views share memory with originals in NumPy - Interactive Code 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.reshape(2, 2)

B.copy()

C.flatten()

D.view()

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 shared memory.

Using .flatten() returns a copy, not a view.

Using .view() creates a new array object that looks at the same data, so changes to the view affect the original array.

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] = [1]
result = arr[1]

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

A20

B25

C50

D40

Attempts:

3 left

💡 Hint

Common Mistakes

Assigning the same original value does not show the effect of shared memory.

Assigning a value outside the array range causes an error.

Changing view_arr[1] to 50 also changes arr[1] because they share the same data.

3fill in blank

hard

Fix the error in the code to confirm if two arrays share memory.

NumPy

import numpy as np
arr = np.array([5, 6, 7, 8])
view_arr = arr.view()
shares_memory = np.shares_memory(arr, [1])

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

Aview_arr

Barr.copy()

Carr.flatten()

Darr.reshape(2, 2)

Attempts:

3 left

💡 Hint

Common Mistakes

Passing arr.copy() instead of the view array returns False.

Passing reshaped or flattened arrays may or may not share memory depending on the operation.

np.shares_memory(arr, view_arr) returns True because view_arr is a view sharing the same data.

4fill in blank

hard

Fill both blanks to create a view and modify it to see the effect on the original array.

NumPy

import numpy as np
arr = np.array([100, 200, 300, 400])
view_arr = arr[1]
view_arr[2] = [2]

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

A.view()

B.copy()

C350

D250

Attempts:

3 left

💡 Hint

Common Mistakes

Using .copy() creates a separate array, so changes do not affect the original.

Assigning the wrong index or value does not demonstrate shared memory.

Using .view() creates a view sharing memory, so changing view_arr[2] to 350 changes arr[2].

5fill in blank

hard

Fill all three blanks to create a view, modify it, and check if the original array changed.

NumPy

import numpy as np
arr = np.array([7, 14, 21, 28])
view_arr = arr[1]
view_arr[0] = [2]
changed = arr[0] == [3]

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

A.view()

B70

D.copy()

Attempts:

3 left

💡 Hint

Common Mistakes

Using .copy() creates a separate array, so arr[0] does not change.

Assigning a different value than 70 causes the check to fail.

Creating a view with .view() and changing view_arr[0] to 70 updates arr[0] to 70, so changed is True.