Why saving and loading matters in NumPy - See It in Action

Why Saving and Loading Matters

📖 Scenario: Imagine you are working on a data science project where you create important data arrays. You want to save your work so you can use it later without starting over.

🎯 Goal: You will create a NumPy array, save it to a file, load it back, and print it to see that your data is safe and ready to use anytime.

📋 What You'll Learn

Create a NumPy array with exact values

Save the array to a file using NumPy

Load the array from the file

Print the loaded array to confirm it matches the original

💡 Why This Matters

🌍 Real World

Data scientists often work with large datasets that take time to prepare. Saving processed data means they can pause and continue work later without losing progress.

💼 Career

Knowing how to save and load data files is essential for data science jobs to manage data efficiently and share results with others.

1

Create a NumPy array

Import NumPy as np and create a NumPy array called data with these exact values: [10, 20, 30, 40, 50].

NumPy

import numpy as np
# Create a NumPy array called data with values [10, 20, 30, 40, 50]
# Your code here

Need a hint?

Use np.array() to create the array with the exact list of numbers.

2

Save the array to a file

Save the NumPy array data to a file named data.npy using the np.save function.

NumPy

import numpy as np
data = np.array([10, 20, 30, 40, 50])
# Save the array data to 'data.npy'
# Your code here

Need a hint?

Use np.save(filename, array) to save the array to a file.

3

Load the array from the file

Load the array from the file data.npy into a new variable called loaded_data using the np.load function.

NumPy

import numpy as np
data = np.array([10, 20, 30, 40, 50])
np.save('data.npy', data)
# Load the array from 'data.npy' into loaded_data
# Your code here

Need a hint?

Use np.load(filename) to load the array from the file.

4

Print the loaded array

Print the variable loaded_data to show the saved and loaded array values.

NumPy

import numpy as np
data = np.array([10, 20, 30, 40, 50])
np.save('data.npy', data)
loaded_data = np.load('data.npy')
# Print loaded_data
# Your code here

Need a hint?

Use print(loaded_data) to display the array.