Indexing with ellipsis in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of ellipsis indexing on 3D array

What is the output of the following code snippet?

NumPy

import numpy as np
arr = np.arange(27).reshape(3,3,3)
result = arr[..., 1]
print(result)

A

[[ 0  3  6]
 [ 9 12 15]
 [18 21 24]]

B

[[ 9 10 11]
 [12 13 14]
 [15 16 17]]

C

[[ 1  4  7]
 [10 13 16]
 [19 22 25]]

D

[[ 2  5  8]
 [11 14 17]
 [20 23 26]]

💡 Hint

Ellipsis (...) replaces all missing full slices in the indexing.

The ellipsis selects all elements in the first two dimensions and fixes the last dimension at index 1, resulting in a 2D array of shape (3,3).

❓ data_output

intermediate

Shape after ellipsis indexing

Given a 4D numpy array with shape (2,3,4,5), what is the shape of the result after indexing with arr[1, ..., 2]?

NumPy

import numpy as np
arr = np.zeros((2,3,4,5))
result = arr[1, ..., 2]
print(result.shape)

A (3, 4, 5, 1)

B (3, 4)

C (3, 4, 5)

D (3, 5)

💡 Hint

Ellipsis fills in the missing dimensions. The fixed indices reduce dimensions accordingly.

Indexing arr[1, ..., 2] fixes the first dimension at 1 and the last dimension at 2, leaving the middle two dimensions (3,4) intact.

🔧 Debug

advanced

Identify the error in ellipsis indexing

What error does the following code raise?

NumPy

import numpy as np
arr = np.arange(24).reshape(2,3,4)
result = arr[..., 4]
print(result)

A No error, prints a 2D array

B TypeError: ellipsis cannot be used with integer index

C ValueError: too many indices for array

D IndexError: index 4 is out of bounds for axis 2 with size 4

💡 Hint

Check the size of the last dimension and the index used.

The last dimension has size 4, so index 4 is out of bounds (valid indices are 0 to 3).

🚀 Application

advanced

Using ellipsis to select all but one dimension

You have a 5D numpy array with shape (2,3,4,5,6). Which indexing expression selects all elements in the first four dimensions but only the element at index 2 in the last dimension?

A arr[..., 2]

B arr[:, :, :, :, 2]

C arr[2, ...]

D arr[2]

💡 Hint

Ellipsis replaces multiple full slices.

arr[..., 2] means select all in all dimensions except the last, where index 2 is fixed.

🧠 Conceptual

expert

Ellipsis behavior with mixed indexing

Consider a numpy array arr with shape (3,4,5,6). What is the shape of arr[1, ..., 2:4]?

NumPy

import numpy as np
arr = np.zeros((3,4,5,6))
result = arr[1, ..., 2:4]
print(result.shape)

A (4, 5, 2)

B (4, 5, 6)

C (4, 5)

D (5, 6)

💡 Hint

Ellipsis fills missing dimensions. Slicing keeps dimension size.

Index 1 fixes the first dimension (3 → removed). Ellipsis fills next two dimensions (4,5). The slice 2:4 selects 2 elements in last dimension (6 → 2).