Why 3D visualization matters in Matplotlib - See It in Action

Why 3D visualization matters

📖 Scenario: Imagine you work in a company that studies how different factors affect sales. You have data with three features: advertising budget, price, and sales. You want to understand how these three things relate to each other. A 3D plot can help you see the relationship clearly.

🎯 Goal: You will create a simple 3D scatter plot using matplotlib to visualize how advertising budget and price relate to sales. This will show why 3D visualization matters when you have three variables.

📋 What You'll Learn

Create three lists named advertising, price, and sales with exact values

Create a variable fig for the figure and ax for 3D axes using matplotlib

Plot a 3D scatter plot using ax.scatter with the three lists

Label the axes with ax.set_xlabel, ax.set_ylabel, and ax.set_zlabel

Show the plot using plt.show()

💡 Why This Matters

🌍 Real World

3D visualization helps businesses and scientists see how three factors relate, like how advertising and price affect sales together.

💼 Career

Data scientists and analysts use 3D plots to explore and explain data with three variables, making insights clearer for decision makers.

1

Create the data lists

Create three lists called advertising, price, and sales with these exact values:
advertising = [100, 200, 300, 400, 500]
price = [10, 9, 8, 7, 6]
sales = [20, 40, 60, 80, 100]

Matplotlib

# Create the three lists advertising, price, and sales with the exact values
# Your code here

Need a hint?

Use square brackets to create lists. Separate numbers with commas.

2

Set up the 3D plot

Import matplotlib.pyplot as plt and import Axes3D from mpl_toolkits.mplot3d. Then create a figure called fig using plt.figure() and create 3D axes called ax using fig.add_subplot(111, projection='3d').

Matplotlib

advertising = [100, 200, 300, 400, 500]
price = [10, 9, 8, 7, 6]
sales = [20, 40, 60, 80, 100]

# Import matplotlib.pyplot as plt and Axes3D
# Create fig with plt.figure()
# Create ax with fig.add_subplot(111, projection='3d')
# Your code here

Need a hint?

Use import matplotlib.pyplot as plt and from mpl_toolkits.mplot3d import Axes3D. Then create the figure and 3D axes as shown.

3

Plot the 3D scatter plot

Use ax.scatter to plot the points with advertising on the x-axis, price on the y-axis, and sales on the z-axis. Then label the axes with ax.set_xlabel('Advertising Budget'), ax.set_ylabel('Price'), and ax.set_zlabel('Sales').

Matplotlib

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

advertising = [100, 200, 300, 400, 500]
price = [10, 9, 8, 7, 6]
sales = [20, 40, 60, 80, 100]

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# Plot the 3D scatter plot with ax.scatter
# Label the axes with ax.set_xlabel, ax.set_ylabel, ax.set_zlabel
# Your code here

Need a hint?

Use ax.scatter(x, y, z) to plot points in 3D. Then label each axis with ax.set_xlabel, ax.set_ylabel, and ax.set_zlabel.

4

Show the 3D plot

Use plt.show() to display the 3D scatter plot.

Matplotlib

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

advertising = [100, 200, 300, 400, 500]
price = [10, 9, 8, 7, 6]
sales = [20, 40, 60, 80, 100]

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

ax.scatter(advertising, price, sales)
ax.set_xlabel('Advertising Budget')
ax.set_ylabel('Price')
ax.set_zlabel('Sales')

# Show the plot with plt.show()
# Your code here

Need a hint?

Use plt.show() to open the window with the 3D plot.