Negative indexing in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of negative indexing on 1D numpy array

What is the output of this code snippet using negative indexing on a numpy array?

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
result = arr[-3]
print(result)

A 30

B 20

C 40

D 50

💡 Hint

Negative index -1 refers to the last element, -2 the second last, and so on.

Negative indexing counts from the end of the array. Here, -3 means the third element from the end, which is 30.

❓ data_output

intermediate

Slice output using negative indices on 2D numpy array

What is the output of slicing this 2D numpy array using negative indices?

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
slice_result = arr[-2:, -2:]
print(slice_result)

A

[[2 3]
 [5 6]]

B

[[4 5]
 [7 8]]

C

[[6]
 [9]]

D

[[5 6]
 [8 9]]

💡 Hint

Negative slicing starts counting from the end of each dimension.

The slice arr[-2:, -2:] selects the last two rows and last two columns, resulting in [[5,6],[8,9]].

🔧 Debug

advanced

Identify the error in negative indexing with numpy array

What error will this code raise when using negative indexing?

NumPy

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

A TypeError: negative index not supported

B IndexError: index -4 is out of bounds for axis 0 with size 3

C ValueError: invalid negative index

D No error, output is 1

💡 Hint

Negative indices must be within the array length when counted from the end.

Index -4 is out of bounds because the array length is 3, so valid negative indices are -1, -2, -3 only.

🚀 Application

advanced

Using negative indexing to reverse a numpy array

Which code snippet correctly reverses a numpy array using negative indexing?

A arr[::-1]

B arr[-1::-1]

C arr[-1:-len(arr):-1]

D arr[-1:0:-1]

💡 Hint

Slicing with step -1 reverses the array.

arr[::-1] returns the array reversed. Other options either miss elements or cause empty slices.

🧠 Conceptual

expert

Effect of negative indexing on multidimensional numpy array shape

Given a 3D numpy array with shape (4, 5, 6), what is the shape of the result after slicing with arr[:, -3:, :-2]?

A (4, 3, 5)

B (4, 2, 4)

C (4, 3, 4)

D (4, 2, 5)

💡 Hint

Negative slicing counts from the end, and the slice excludes the last 2 elements in the last dimension.

arr[:, -3:, :-2] selects all in first dim (4), last 3 in second dim (5-3=2 to 4), and all except last 2 in third dim (6-2=4). So shape is (4,3,4).