Views share memory with originals in NumPy - Mini Project: Build & Apply

Understanding Views Sharing Memory with Originals in NumPy

📖 Scenario: Imagine you have a list of daily temperatures recorded in Celsius. You want to analyze a part of this data without copying it, so changes in your smaller dataset reflect in the original data.

🎯 Goal: You will create a NumPy array of temperatures, create a view of part of this array, modify the view, and observe how the original array changes because the view shares memory.

📋 What You'll Learn

Create a NumPy array called temps with exact values: [20, 22, 21, 19, 23, 24]

Create a variable called temp_view that is a view of temps from index 2 to 5

Change the first element of temp_view to 30

Print both temps and temp_view to see the effect

💡 Why This Matters

🌍 Real World

In data analysis, working with views helps save memory and processing time when working with large datasets.

💼 Career

Understanding views and memory sharing is important for efficient data manipulation and avoiding unintended side effects in data science and machine learning projects.

1

Create the original NumPy array

Create a NumPy array called temps with these exact values: [20, 22, 21, 19, 23, 24]

NumPy

# Create the NumPy array temps with the given values
# Your code here

Need a hint?

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

2

Create a view of part of the array

Create a variable called temp_view that is a view of temps from index 2 to 5 (inclusive start, exclusive end). Use slicing to get this view.

NumPy

import numpy as np

temps = np.array([20, 22, 21, 19, 23, 24])

# Create a view temp_view of temps from index 2 to 5
# Your code here

Need a hint?

Use slicing temps[2:5] to create the view.

3

Modify the view to see shared memory effect

Change the first element of temp_view to 30 to see how it affects the original temps array.

NumPy

import numpy as np

temps = np.array([20, 22, 21, 19, 23, 24])
temp_view = temps[2:5]

# Change the first element of temp_view to 30
# Your code here

Need a hint?

Assign 30 to temp_view[0].

4

Print both arrays to observe the change

Print the temps array and the temp_view array to observe how changing the view affected the original array.

NumPy

import numpy as np

temps = np.array([20, 22, 21, 19, 23, 24])
temp_view = temps[2:5]
temp_view[0] = 30

# Print temps and temp_view
# Your code here

Need a hint?

Use two print() statements, one for temps and one for temp_view.