View vs copy behavior in NumPy - Practice Questions

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of modifying a NumPy slice?

Consider the following code where we create a NumPy array and then slice it. What will be the output after modifying the slice?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
slice_arr = arr[1:4]
slice_arr[0] = 10
print(arr)

A [ 1 2 3 4 5]

B [ 1 10 3 4 5]

C [ 1 10 2 4 5]

D [10 2 3 4 5]

💡 Hint

Remember that slicing a NumPy array creates a view, not a copy.

In NumPy, slicing returns a view of the original array. So modifying the slice changes the original array as well.

❓ Predict Output

intermediate

What happens when using np.copy() on a slice?

Given the code below, what will be printed after modifying the copied slice?

NumPy

import numpy as np
arr = np.array([5, 6, 7, 8, 9])
copied_slice = np.copy(arr[1:4])
copied_slice[0] = 100
print(arr)

A [ 5 6 100 8 9]

B [ 5 100 7 8 9]

C [100 6 7 8 9]

D [ 5 6 7 8 9]

💡 Hint

np.copy() creates a new independent array.

np.copy() creates a new array that does not share data with the original. So modifying the copy does not affect the original array.

❓ data_output

advanced

How many elements are changed in the original array after this operation?

What is the number of elements in the original array 'arr' that are modified after running this code?

NumPy

import numpy as np
arr = np.arange(10)
view = arr[2:8:2]
view[:] = -1
print(arr)

A 3

B 6

C 5

D 4

💡 Hint

Check the slice indices and how many elements it covers.

The slice arr[2:8:2] selects elements at indices 2, 4, 6 (3 elements). Modifying the view changes these 3 elements in arr.

🔧 Debug

advanced

Why does this code not modify the original array?

Examine the code below. Why does modifying 'sub_arr' not change 'arr'?

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
sub_arr = arr[1:4].copy()
sub_arr[0] = 999
print(arr)

A Because the slice indices are incorrect and no elements are selected.

B Because arr is immutable and cannot be changed.

C Because sub_arr is a copy, not a view, so changes don't affect arr.

D Because sub_arr is a view but changes are buffered and not applied immediately.

💡 Hint

Check how sub_arr is created.

Using .copy() creates a new array independent of the original. So changes to sub_arr do not affect arr.

🚀 Application

expert

Identify the correct way to modify a NumPy array without affecting the original

You want to modify a subset of a NumPy array without changing the original array. Which option correctly achieves this?

A

subset = arr[2:5].copy()
subset[0] = 100

B

subset = arr[2:5]
subset[0] = 100

C

subset = arr[2:5]
arr[2] = 100

D

subset = arr.copy()[2:5]
subset[0] = 100

💡 Hint

Think about which creates an independent copy before modification.

Only option A creates a copy of the slice before modifying it, so the original array remains unchanged.