Multi-dimensional fancy indexing in NumPy - Mini Project: Build & Apply

Multi-dimensional Fancy Indexing with NumPy

📖 Scenario: You work at a sports analytics company. You have a 2D array representing scores of 4 players in 5 games. You want to select specific scores using multi-dimensional fancy indexing to analyze performance.

🎯 Goal: Learn how to use multi-dimensional fancy indexing in NumPy to select specific elements from a 2D array.

📋 What You'll Learn

Create a 2D NumPy array called scores with exact values

Create two 1D NumPy arrays called row_indices and col_indices for indexing

Use multi-dimensional fancy indexing with row_indices and col_indices to select elements from scores

Print the selected elements

💡 Why This Matters

🌍 Real World

Selecting specific data points from multi-dimensional datasets is common in sports analytics, image processing, and scientific computing.

💼 Career

Data scientists and analysts often use fancy indexing to efficiently extract and analyze subsets of data from large arrays.

1

Create the scores array

Import NumPy as np and create a 2D NumPy array called scores with these exact values: [[10, 20, 30, 40, 50], [15, 25, 35, 45, 55], [12, 22, 32, 42, 52], [17, 27, 37, 47, 57]]

NumPy

import numpy as np
# Create the 2D array 'scores' with the exact values
# Your code here

Need a hint?

Use np.array() to create the 2D array with the exact nested lists.

2

Create row and column index arrays

Create two 1D NumPy arrays called row_indices and col_indices with these exact values: row_indices = np.array([0, 1, 2, 3]) and col_indices = np.array([4, 3, 2, 1])

NumPy

import numpy as np
scores = np.array([[10, 20, 30, 40, 50], [15, 25, 35, 45, 55], [12, 22, 32, 42, 52], [17, 27, 37, 47, 57]])
# Create 'row_indices' and 'col_indices' arrays with the exact values
# Your code here

Need a hint?

Use np.array() to create the index arrays with the exact values.

3

Select elements using multi-dimensional fancy indexing

Use multi-dimensional fancy indexing with row_indices and col_indices on scores to create a new array called selected_scores that contains the elements at those positions.

NumPy

import numpy as np
scores = np.array([[10, 20, 30, 40, 50], [15, 25, 35, 45, 55], [12, 22, 32, 42, 52], [17, 27, 37, 47, 57]])
row_indices = np.array([0, 1, 2, 3])
col_indices = np.array([4, 3, 2, 1])
# Use multi-dimensional fancy indexing to select elements
# Your code here

Need a hint?

Use scores[row_indices, col_indices] to select elements at matching pairs of indices.

4

Print the selected scores

Print the selected_scores array to display the selected elements.

NumPy

import numpy as np
scores = np.array([[10, 20, 30, 40, 50], [15, 25, 35, 45, 55], [12, 22, 32, 42, 52], [17, 27, 37, 47, 57]])
row_indices = np.array([0, 1, 2, 3])
col_indices = np.array([4, 3, 2, 1])
selected_scores = scores[row_indices, col_indices]
# Print the selected_scores array
# Your code here

Need a hint?

Use print(selected_scores) to display the array.