np.empty() for uninitialized arrays in NumPy - Mini Project: Build & Apply

Using np.empty() to Create Uninitialized Arrays

📖 Scenario: Imagine you are working with sensor data that you want to store temporarily before filling it with actual measurements. You want to create an empty array to hold these values quickly without initializing them to zero.

🎯 Goal: You will create an uninitialized NumPy array using np.empty(), set its shape, and then fill it with sample data.

📋 What You'll Learn

Create a NumPy array using np.empty() with a specific shape

Create a variable to hold the shape as a tuple

Fill the uninitialized array with sample values using a loop

Print the final array to see the filled values

💡 Why This Matters

🌍 Real World

Creating uninitialized arrays is useful when you want to allocate memory quickly and fill it later with actual data, such as sensor readings or image pixels.

💼 Career

Data scientists and engineers often use <code>np.empty()</code> to optimize performance when working with large datasets or simulations.

1

Create the shape tuple for the array

Create a variable called shape and set it to the tuple (3, 2) to represent 3 rows and 2 columns.

NumPy

# Create a variable called shape and set it to (3, 2)
# Your code here

Need a hint?

The shape of the array is a tuple with two numbers: rows and columns.

2

Create an uninitialized array using np.empty()

Use np.empty() with the variable shape to create an uninitialized array called data.

NumPy

shape = (3, 2)
# Create an uninitialized array called data using np.empty() with shape
# Your code here

Need a hint?

Use np.empty(shape) to create the array without initializing values.

3

Fill the uninitialized array with sample values

Use nested for loops with variables i and j to fill the data array with values equal to i * 10 + j.

NumPy

import numpy as np
shape = (3, 2)
data = np.empty(shape)
# Use nested for loops with i and j to fill data[i, j] = i * 10 + j
# Your code here

Need a hint?

Use two loops: outer for rows (i), inner for columns (j). Assign values inside the loops.

4

Print the filled array

Write a print() statement to display the data array.

NumPy

import numpy as np
shape = (3, 2)
data = np.empty(shape)
for i in range(shape[0]):
    for j in range(shape[1]):
        data[i, j] = i * 10 + j
# Print the data array
# Your code here

Need a hint?

Use print(data) to show the array with your filled values.