Combining fancy and slice indexing in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of combining fancy and slice indexing on a 2D array

What is the output of this code snippet?

import numpy as np
arr = np.arange(16).reshape(4,4)
result = arr[[1,3], 1:3]

NumPy

import numpy as np
arr = np.arange(16).reshape(4,4)
result = arr[[1,3], 1:3]
print(result)

A

[[1 2]
 [9 10]]

B

[[1 2 3]
 [9 10 11]]

C

[[5 6 7]
 [13 14 15]]

D

[[5 6]
 [13 14]]

💡 Hint

Remember that fancy indexing selects specific rows, and slice indexing selects columns.

The fancy index [1,3] selects rows 1 and 3. The slice 1:3 selects columns 1 and 2. So the output is a 2x2 array with elements from rows 1 and 3 and columns 1 and 2.

❓ data_output

intermediate

Shape of array after combining fancy and slice indexing

Given the code below, what is the shape of the resulting array?

import numpy as np
arr = np.arange(27).reshape(3,3,3)
result = arr[[0,2], :, 1:3]

NumPy

import numpy as np
arr = np.arange(27).reshape(3,3,3)
result = arr[[0,2], :, 1:3]
print(result.shape)

A (2, 3, 2)

B (3, 2, 2)

C (2, 2, 3)

D (3, 3, 2)

💡 Hint

Fancy indexing on the first axis selects 2 slices, then full slice on second axis, then slice on third axis.

Fancy indexing selects 2 elements along the first axis, full slice selects all 3 elements along the second axis, and slice 1:3 selects 2 elements along the third axis, so shape is (2,3,2).

🔧 Debug

advanced

Identify the error in combining fancy and slice indexing

What error does this code raise?

import numpy as np
arr = np.arange(9).reshape(3,3)
result = arr[1:3, [0, 2], 1]

NumPy

import numpy as np
arr = np.arange(9).reshape(3,3)
result = arr[1:3, [0, 2], 1]

A TypeError: unhashable type: 'slice'

B IndexError: too many indices for array

C ValueError: shape mismatch in fancy indexing

D No error, outputs a 2D array

💡 Hint

Check how many indices the array has and how many are used.

The array is 2D but the code uses 3 indices, causing IndexError: too many indices for array.

🚀 Application

advanced

Extract specific rows and columns using combined indexing

You have a 5x5 numpy array. You want to extract rows 0, 2, and 4, but only columns 1 to 3 (inclusive start, exclusive end). Which code snippet correctly does this?

A arr[1:4, 0:3]

B arr[1:4, [0, 2, 4]]

C arr[[0, 2, 4], 1:4]

D arr[[1, 2, 3], 0:3]

💡 Hint

Remember fancy indexing selects rows, slice selects columns.

Option C selects rows 0, 2, 4 and columns 1, 2, 3 correctly. Others select wrong axes or ranges.

🧠 Conceptual

expert

Understanding the output shape when combining fancy and slice indexing

Consider a 3D numpy array with shape (4, 5, 6). If you use fancy indexing on the first axis with 2 indices and slice indexing on the second axis with 3:5, and slice indexing on the third axis with 1:4, what will be the shape of the resulting array?

A (2, 2, 3)

B (4, 2, 3)

C (2, 3, 3)

D (2, 5, 3)

💡 Hint

Fancy indexing changes the size of the first axis to the number of indices selected.

Fancy indexing selects 2 elements on axis 0, slice 3:5 selects 2 elements on axis 1, slice 1:4 selects 3 elements on axis 2, so shape is (2, 2, 3).