View vs copy behavior in NumPy - Hands-On Comparison

Understanding View vs Copy Behavior in NumPy

📖 Scenario: Imagine you are working with a large dataset of temperatures recorded every hour for a week. You want to analyze a subset of this data without changing the original dataset accidentally.

🎯 Goal: You will learn how to create a subset of a NumPy array as a view or a copy and understand how changes to these subsets affect the original data.

📋 What You'll Learn

Create a NumPy array with specific values

Create a subset of the array using slicing

Create a copy of the subset

Modify the subsets and observe effects on the original array

💡 Why This Matters

🌍 Real World

Data scientists often work with large datasets and need to manipulate subsets without changing the original data unintentionally.

💼 Career

Understanding views and copies in NumPy helps prevent bugs and data corruption in data analysis and machine learning workflows.

1

Create the original NumPy array

Import NumPy as np and create a NumPy array called temps with these exact values: [20, 22, 21, 19, 23, 24, 22].

NumPy

# Import numpy and create the array temps
# Your code here

Need a hint?

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

2

Create a view of the array using slicing

Create a variable called temps_view that is a slice of temps from index 2 to 6 (inclusive start, exclusive end).

NumPy

import numpy as np

temps = np.array([20, 22, 21, 19, 23, 24, 22])
# Create temps_view as a slice of temps from index 2 to 6
# Your code here

Need a hint?

Remember slicing in Python uses start index inclusive and end index exclusive.

3

Create a copy of the slice

Create a variable called temps_copy that is a copy of temps_view using the copy() method.

NumPy

import numpy as np

temps = np.array([20, 22, 21, 19, 23, 24, 22])
temps_view = temps[2:6]
# Create temps_copy as a copy of temps_view
# Your code here

Need a hint?

Use the copy() method on the slice to create an independent copy.

4

Modify subsets and print results

Change the first element of temps_view to 100 and the first element of temps_copy to 200. Then print temps, temps_view, and temps_copy each on a separate line.

NumPy

import numpy as np

temps = np.array([20, 22, 21, 19, 23, 24, 22])
temps_view = temps[2:6]
temps_copy = temps_view.copy()
# Change first element of temps_view to 100
# Change first element of temps_copy to 200
# Print temps, temps_view, temps_copy
# Your code here

Need a hint?

Changing the view affects the original array, but changing the copy does not.