Why advanced indexing matters in NumPy - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of basic vs advanced indexing

What is the output of the following code comparing basic and advanced indexing in NumPy?

NumPy

import numpy as np
arr = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
result_basic = arr[1, 2]
result_advanced = arr[[1, 2], [2, 0]]
print(result_basic)
print(result_advanced)

A

60
[50 70]

B

60
[60 70]

C

[40 50]
[60 70]

D

60
[40 90]

💡 Hint

Remember that basic indexing selects a single element, while advanced indexing can select multiple elements using arrays of indices.

Basic indexing arr[1, 2] selects the element at row 1, column 2, which is 60. Advanced indexing arr[[1, 2], [2, 0]] selects elements at positions (1,2) and (2,0), which are 60 and 70 respectively.

❓ data_output

intermediate

Shape of array after advanced indexing

What is the shape of the array produced by this advanced indexing operation?

NumPy

import numpy as np
arr = np.arange(24).reshape(4,6)
indexed = arr[[0, 2], [1, 4]]
print(indexed.shape)

A (2, 2)

B (1, 2)

C (2,)

D (4,)

💡 Hint

Advanced indexing with two 1D arrays returns a 1D array with length equal to the arrays' length.

The two index arrays [0, 2] and [1, 4] select two elements, so the result is a 1D array of length 2, shape (2,).

🔧 Debug

advanced

Identify the error in advanced indexing

What error does this code raise when using advanced indexing incorrectly?

NumPy

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

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

B ValueError: shape mismatch: indexing arrays could not be broadcast together

C TypeError: list indices must be integers or slices, not list

D No error, returns array([2, 5, 6])

💡 Hint

Check the size of the first dimension of the array and the indices used.

The array has shape (2,3), so index 2 is out of bounds for axis 0 (rows). Using indices = [0,1,2] tries to access row 2 which does not exist, causing IndexError.

🚀 Application

advanced

Using advanced indexing to modify array elements

Given the array below, which option correctly sets the elements at positions (0,1), (1,2), and (2,0) to 99 using advanced indexing?

NumPy

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

A arr[[0,1,2], [1,2,0]] += 99

B arr[[0,1,2], [1,2,0]] == 99

C arr[0,1,2][1,2,0] = 99

D arr[[0,1,2], [1,2,0]] = 99

💡 Hint

Use advanced indexing with two lists of indices to assign values to specific positions.

Option D uses advanced indexing correctly to assign 99 to the specified positions. Option D uses comparison instead of assignment. Option D is invalid syntax. Option D adds 99 instead of setting.

🧠 Conceptual

expert

Why advanced indexing matters for performance

Why is advanced indexing important when working with large NumPy arrays?

A It allows selecting multiple arbitrary elements efficiently without copying the entire array.

B It automatically sorts the array elements during selection.

C It converts arrays into lists for faster processing.

D It prevents any changes to the original array by creating a deep copy always.

💡 Hint

Think about how advanced indexing helps when you want specific elements scattered in the array.

Advanced indexing lets you pick multiple arbitrary elements efficiently, often returning a new array with just those elements, which is faster and uses less memory than copying or looping manually.