3D plot limitations and alternatives in Matplotlib - Mini Project: Build & Apply

3D Plot Limitations and Alternatives

📖 Scenario: You are working as a data analyst. You want to visualize some 3D data points to understand their distribution. However, 3D plots can be hard to read and interpret. You want to explore the limitations of 3D plots and learn how to use alternative 2D visualizations to better understand the data.

🎯 Goal: Create a 3D scatter plot of data points, then create alternative 2D plots (scatter and heatmap) to better visualize the data distribution.

📋 What You'll Learn

Use matplotlib to create a 3D scatter plot

Create a 2D scatter plot alternative

Create a 2D heatmap alternative

Understand the limitations of 3D plots in data visualization

💡 Why This Matters

🌍 Real World

Data scientists often need to visualize multi-dimensional data. Understanding the limits of 3D plots helps them choose better visualization methods.

💼 Career

Knowing how to create and interpret different plots is essential for data analysis roles to communicate insights clearly.

1

Create 3D data points

Create three lists called x, y, and z each containing these exact values: [1, 2, 3, 4, 5], [5, 4, 3, 2, 1], and [2, 3, 4, 5, 6] respectively.

Matplotlib

# Create lists x, y, and z with the given values
# Your code here

Need a hint?

Use three separate lists named exactly x, y, and z with the values given.

2

Set up 3D plot configuration

Import matplotlib.pyplot as plt and Axes3D from mpl_toolkits.mplot3d. Then create a figure called fig and add a 3D subplot called ax.

Matplotlib

x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]
z = [2, 3, 4, 5, 6]

# Import matplotlib.pyplot as plt and Axes3D
# Create figure fig and add 3D subplot ax
# Your code here

Need a hint?

Use plt.figure() to create the figure and fig.add_subplot(111, projection='3d') to create the 3D axes.

3

Plot 3D scatter and 2D alternatives

Use ax.scatter(x, y, z) to create a 3D scatter plot. Then create a 2D scatter plot of x vs y using plt.scatter(x, y). Finally, create a 2D heatmap of x and y using plt.hist2d(x, y, bins=5).

Matplotlib

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

x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]
z = [2, 3, 4, 5, 6]

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

# Plot 3D scatter with ax.scatter
# Plot 2D scatter with plt.scatter
# Plot 2D heatmap with plt.hist2d
# Your code here

Need a hint?

Use ax.scatter(x, y, z) for 3D points, plt.scatter(x, y) for 2D scatter, and plt.hist2d(x, y, bins=5) for heatmap.

4

Show all plots

Use plt.show() to display all the plots you created.

Matplotlib

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

x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]
z = [2, 3, 4, 5, 6]

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

ax.scatter(x, y, z)

plt.figure()
plt.scatter(x, y)

plt.figure()
plt.hist2d(x, y, bins=5)

# Show all plots
# Your code here

Need a hint?

Call plt.show() once at the end to display all figures.