3D axes with projection='3d' in Matplotlib - Mini Project: Build & Apply

Plotting 3D Points with Matplotlib

📖 Scenario: You are working on a simple data visualization project. You want to show points in three dimensions to better understand their spatial relationships.

🎯 Goal: Create a 3D scatter plot using Matplotlib's projection='3d' feature to visualize points in 3D space.

📋 What You'll Learn

Create three lists named x, y, and z with exact values

Create a Matplotlib figure and add 3D axes using projection='3d'

Plot the points using a scatter plot on the 3D axes

Display the plot

💡 Why This Matters

🌍 Real World

3D plots help scientists and engineers visualize data with three variables, like position in space or measurements over time.

💼 Career

Data scientists and analysts use 3D plotting to explore complex datasets and communicate insights clearly.

1

Create the 3D data points

Create three lists called x, y, and z with these exact values:
x = [1, 2, 3], y = [4, 5, 6], z = [7, 8, 9]

Matplotlib

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

Hint

Use square brackets to create lists. For example, x = [1, 2, 3].

2

Set up the 3D plot axes

Import matplotlib.pyplot as plt. Then create a figure called fig using plt.figure(). Add 3D axes to fig by creating a variable called ax with fig.add_subplot(111, projection='3d').

Matplotlib

x = [1, 2, 3]
y = [4, 5, 6]
z = [7, 8, 9]

# Import matplotlib.pyplot as plt
# Create a figure called fig
# Add 3D axes to fig and assign to ax
# Your code here

Hint

Use import matplotlib.pyplot as plt to import. Then fig = plt.figure() creates the figure. Use fig.add_subplot(111, projection='3d') to add 3D axes.

3

Plot the 3D points

Use the scatter method of ax to plot the points with x, y, and z as coordinates.

Matplotlib

import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [4, 5, 6]
z = [7, 8, 9]

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

# Plot the points using ax.scatter
# Your code here

Hint

Use ax.scatter(x, y, z) to plot the points in 3D.

4

Show the 3D plot

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

Matplotlib

import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [4, 5, 6]
z = [7, 8, 9]

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

ax.scatter(x, y, z)

# Display the plot
# Your code here

Hint

Call plt.show() to open the plot window.

Practice

1. What does setting projection='3d' do when creating axes in matplotlib?

easy

A. It creates a 3D plot area to visualize data in three dimensions.

B. It changes the plot color to 3D style automatically.

C. It enables animation features in the plot.

D. It exports the plot as a 3D model file.

Solution

1. Step 1: Understand the role of projection parameter

   The projection parameter in matplotlib axes defines the type of plot. Setting it to '3d' enables three-dimensional plotting.

2. Step 2: Identify the effect of projection='3d'

   This setting creates a 3D plot area where data can be visualized along x, y, and z axes.

3. Final Answer:

   It creates a 3D plot area to visualize data in three dimensions. -> Option A

4. Quick Check:

   projection='3d' = 3D plot area [OK]

Hint: projection='3d' means 3D plot space [OK]

Common Mistakes:

• Thinking it changes colors automatically
• Assuming it enables animation
• Believing it exports 3D files

2. Which of the following is the correct way to create a 3D axes object in matplotlib?

easy

A. ax = plt.axes3d()

B. ax = plt.subplot(111, projection='3d')

C. ax = plt.figure(projection='3d')

D. ax = plt.plot(projection='3d')

Solution

1. Step 1: Recall the syntax for 3D axes creation

   To create 3D axes, use plt.subplot() or plt.axes() with projection='3d'.

2. Step 2: Check each option

   ax = plt.subplot(111, projection='3d') is correct. The other options use incorrect functions or parameters.

3. Final Answer:

   ax = plt.subplot(111, projection='3d') -> Option B

4. Quick Check:

   Use subplot with projection='3d' = correct syntax [OK]

Hint: Use subplot or axes with projection='3d' [OK]

Common Mistakes:

• Using plt.plot() with projection
• Passing projection to plt.figure()
• Calling non-existent plt.axes3d()

3. What will the following code output?

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter([1,2], [3,4], [5,6])
print(type(ax))

medium

A. <class 'matplotlib.axes._subplots.Axes3DSubplot'>

B. <class 'matplotlib.axes._axes.Axes'>

C. SyntaxError

D. RuntimeError

Solution

1. Step 1: Understand the code creating 3D axes

   The code creates a figure, then adds a 3D subplot with projection='3d'. This returns an Axes3DSubplot object.

2. Step 2: Check the printed type

   Printing type(ax) will show the class of the 3D axes object, which is Axes3DSubplot.

3. Final Answer:

   <class 'matplotlib.axes._subplots.Axes3DSubplot'> -> Option A

4. Quick Check:

   3D subplot type = Axes3DSubplot [OK]

Hint: 3D subplot returns Axes3DSubplot type [OK]

Common Mistakes:

• Expecting base Axes type
• Confusing syntax or runtime errors
• Not importing Axes3D

4. Identify the error in this code snippet:

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax = plt.axes(projection='3d')
ax.plot([1,2,3], [4,5,6], [7,8,9])
plt.show()

medium

A. Missing import of Axes3D causes error.

B. plot() does not accept three lists for 3D plotting.

C. Calling plt.axes() after fig.add_subplot() overwrites ax incorrectly.

D. plt.show() is missing parentheses.

Solution

1. Step 1: Analyze axes creation

   The code first creates ax with fig.add_subplot(111) (2D axes), then immediately overwrites ax with plt.axes(projection='3d'). This is confusing and may cause unexpected behavior.

2. Step 2: Understand the problem

   Overwriting ax without using the figure's subplot can cause the 3D axes to not be linked to the figure properly.

3. Final Answer:

   Calling plt.axes() after fig.add_subplot() overwrites ax incorrectly. -> Option C

4. Quick Check:

   Overwriting ax with plt.axes() causes confusion [OK]

Hint: Avoid overwriting axes objects; create 3D axes once [OK]

Common Mistakes:

• Forgetting to import Axes3D (not needed in recent matplotlib)
• Thinking plot() can't take 3 lists
• Missing plt.show() parentheses

5. You want to plot a 3D scatter plot with points colored by their z-value. Which code snippet correctly creates the 3D axes and colors the points accordingly?

hard

A. fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
z = [1, 2, 3]
ax.scatter([1,2,3], [4,5,6], z)
plt.show()

B. fig = plt.figure()
ax = plt.axes(projection='3d')
z = [1, 2, 3]
ax.scatter([1,2,3], [4,5,6], z, color='z')
plt.show()

C. fig = plt.figure()
ax = fig.add_subplot(111)
z = [1, 2, 3]
ax.scatter([1,2,3], [4,5,6], z, c=z)
plt.show()

D. fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
z = [1, 2, 3]
ax.scatter([1,2,3], [4,5,6], z, c=z, cmap='viridis')
plt.show()

Solution

1. Step 1: Create 3D axes correctly

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

2. Step 2: Color points by z-value

   Pass c=z and a colormap like cmap='viridis' to scatter() to color points based on z.

3. Final Answer:

   fig = plt.figure() ax = fig.add_subplot(111, projection='3d') z = [1, 2, 3] ax.scatter([1,2,3], [4,5,6], z, c=z, cmap='viridis') plt.show() -> Option D

4. Quick Check:

   3D axes + c=z + cmap = fig = plt.figure() ax = fig.add_subplot(111, projection='3d') z = [1, 2, 3] ax.scatter([1,2,3], [4,5,6], z, c=z, cmap='viridis') plt.show() [OK]

Hint: Use c=z and cmap for coloring in 3D scatter [OK]

Common Mistakes:

• Using color='z' instead of c=z
• Creating 2D axes for 3D data
• Not specifying projection='3d'