Indexing returns views not copies in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of modifying a view created by slicing a NumPy array?

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

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
slice_arr = arr[1:4]
slice_arr[0] = 99
print(arr)

A [10 99 30 40 50]

B [10 20 30 40 50]

C [10 99 99 40 50]

D [10 20 99 40 50]

💡 Hint

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

Slicing a NumPy array returns a view, so modifying the slice changes the original array at the corresponding positions.

❓ Predict Output

intermediate

What happens when you modify a copy created by fancy indexing in NumPy?

Look at this code where we use fancy indexing to select elements. What will be the output of the original array after modifying the selected elements?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
selected = arr[[1, 3]]
selected[0] = 99
print(arr)

A [1 99 3 4 5]

B [1 2 99 4 5]

C [1 2 3 4 5]

D [1 99 3 99 5]

💡 Hint

Fancy indexing returns a copy, not a view.

Fancy indexing returns a copy, so modifying the selected array does not affect the original array.

🔧 Debug

advanced

Why does modifying a slice not change the original array here?

This code tries to modify a slice of a 2D NumPy array but the original array remains unchanged. What is the reason?

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6]])
slice_arr = arr[[0, 2]]
slice_arr[0, 0] = 99
print(arr)

A Because NumPy arrays are immutable.

B Because the array is 2D, slices always return copies.

C Because the assignment syntax is incorrect.

D Because fancy indexing returns a copy, not a view.

💡 Hint

Check the type of indexing used to create the slice.

Using fancy indexing (list of indices) returns a copy, so modifying the slice does not affect the original array.

❓ data_output

advanced

How many elements are changed in the original array after modifying a boolean mask view?

Given this code, how many elements in the original array are changed after modifying the masked view?

NumPy

import numpy as np
arr = np.array([5, 10, 15, 20, 25])
mask = arr > 10
view = arr[mask]
view[:] = 0
print(arr)

A 0

B 2

C 3

D 5

💡 Hint

Boolean indexing returns a copy, not a view.

Boolean indexing returns a copy, so modifying view does not change the original array. No elements are changed.

🚀 Application

expert

How to safely modify a subset of a NumPy array without affecting the original?

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

A

subset = arr[[1, 2, 3]]
subset[0] = 100

B

subset = arr[1:4].copy()
subset[0] = 100

C

subset = arr[1:4]
subset[0] = 100

D

subset = arr
subset[1] = 100

💡 Hint

Think about when views or copies are created and how to avoid changing the original.

Using .copy() creates a new array independent of the original. Modifying it does not affect the original array.