np.full() for custom-filled arrays in NumPy - Mini Project: Build & Apply

Create Custom-Filled Arrays Using np.full()

📖 Scenario: You are working on a data science project where you need to create arrays filled with specific values to represent initial conditions or placeholders.

🎯 Goal: Learn how to use np.full() to create NumPy arrays filled with a custom value.

📋 What You'll Learn

Create a NumPy array with a specific shape filled with a custom value using np.full().

Use variables to define the shape and fill value.

Print the resulting array to see the output.

💡 Why This Matters

🌍 Real World

Creating arrays filled with specific values is useful in data science for initializing data structures, placeholders, or masks.

💼 Career

Understanding how to create and manipulate arrays with NumPy is a fundamental skill for data scientists and analysts working with numerical data.

1

Create the array shape variable

Create a variable called shape and set it to a tuple with values (3, 4) representing the dimensions of the array.

NumPy

# Create a variable called shape with the tuple (3, 4)
# Your code here

Need a hint?

Use parentheses to create a tuple for the shape, like (3, 4).

2

Set the fill value

Create a variable called fill_value and set it to the integer 7 which will be used to fill the array.

NumPy

shape = (3, 4)
# Create a variable called fill_value and set it to 7
# Your code here

Need a hint?

Just assign the number 7 to the variable fill_value.

3

Create the filled array using np.full()

Import the numpy library as np. Then create a variable called filled_array using np.full() with the shape and fill_value variables.

NumPy

shape = (3, 4)
fill_value = 7
# Import numpy as np
# Create filled_array using np.full() with shape and fill_value
# Your code here

Need a hint?

Use import numpy as np and then np.full(shape, fill_value) to create the array.

4

Print the filled array

Print the variable filled_array to display the array filled with the custom value.

NumPy

import numpy as np
shape = (3, 4)
fill_value = 7
filled_array = np.full(shape, fill_value)
# Print the filled_array
# Your code here

Need a hint?

Use print(filled_array) to show the array.