Fancy indexing with integer arrays in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of fancy indexing with repeated indices

What is the output of the following code snippet using NumPy fancy indexing?

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
result = arr[[1, 2, 2, 4]]
print(result)

A [10 20 30 40]

B [20 30 30 50]

C [1 2 2 4]

D [20 30 40 50]

💡 Hint

Remember that fancy indexing returns elements at the specified indices, including duplicates.

The array is indexed at positions 1, 2, 2, and 4. Position 1 is 20, position 2 is 30, repeated twice, and position 4 is 50.

❓ data_output

intermediate

Shape of result after 2D fancy indexing

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

NumPy

import numpy as np
arr = np.arange(16).reshape(4,4)
indices = np.array([[0, 1], [2, 3]])
result = arr[indices]
print(result.shape)

A (2, 2, 4)

B (4, 4)

C (2, 4)

D (2, 2)

💡 Hint

Fancy indexing with a 2D array returns an array with shape equal to the indices shape plus the remaining dimensions.

The indices array has shape (2,2). Each index selects a row of length 4 from arr, so the result shape is (2,2,4).

🔧 Debug

advanced

Identify the error in fancy indexing with mixed types

What error will the following code raise?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
indices = [1, '2', 3]
result = arr[indices]
print(result)

A ValueError: invalid literal for int() with base 10: '2'

B IndexError: index 2 is out of bounds for axis 0 with size 5

C TypeError: only integer scalar arrays can be converted to a scalar index

D No error, output: [2 3 4]

💡 Hint

Check the types inside the indices list used for indexing.

The list contains a string '2' which is not a valid integer index, causing a TypeError when used for fancy indexing.

🚀 Application

advanced

Extracting specific elements using fancy indexing

You have a 2D NumPy array representing scores of 5 students in 4 subjects:

scores = np.array([[80, 90, 70, 60],
                   [85, 95, 75, 65],
                   [78, 88, 68, 58],
                   [82, 92, 72, 62],
                   [79, 89, 69, 59]])

You want to extract the scores of students 0, 2, and 4 in subjects 1 and 3 using fancy indexing. Which code snippet produces the correct result?

NumPy

import numpy as np
scores = np.array([[80, 90, 70, 60],
                   [85, 95, 75, 65],
                   [78, 88, 68, 58],
                   [82, 92, 72, 62],
                   [79, 89, 69, 59]])

A result = scores[[0, 2, 4], [1, 3, 1]]

B result = scores[:, [1, 3]][[0, 2, 4]]

C result = scores[[0, 2, 4], :][:, [1, 3]]

D result = scores[[0, 2, 4]][:, [1, 3]]

💡 Hint

Remember that fancy indexing with two arrays selects elements pairwise, but you want a submatrix.

Option D first selects the rows 0, 2, and 4, then selects columns 1 and 3, producing a 3x2 array of the desired scores.

🧠 Conceptual

expert

Understanding broadcasting in fancy indexing

Consider the following code snippet:

import numpy as np
arr = np.arange(12).reshape(3,4)
rows = np.array([0, 1, 2])
cols = np.array([[0, 1], [2, 3]])
result = arr[rows[:, None], cols]
print(result)

What is the shape of result and why?

A (3, 2) because rows[:, None] broadcasts with cols to select elements pairwise

B (3, 4) because arr is 3x4 and indexing preserves shape

C (2, 3) because cols shape is (2,2) and rows shape is (3,)

D (3, 2, 4) because of advanced indexing with 3D output

💡 Hint

Think about how broadcasting works between rows[:, None] and cols arrays during indexing.

rows[:, None] has shape (3,1) and cols has shape (2,2). They broadcast to (3,2,2), so result shape is (3,2,2).