3D axes with projection='3d' in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of 3D scatter plot code

What will be the output of this code snippet that creates a 3D scatter plot using matplotlib?

Matplotlib

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

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

x = np.array([1, 2, 3])
y = np.array([4, 5, 6])
z = np.array([7, 8, 9])

ax.scatter(x, y, z)
plt.show()

A A 2D scatter plot with points at (1,4), (2,5), and (3,6) ignoring z values

B A 3D scatter plot with points at coordinates (1,4,7), (2,5,8), and (3,6,9) displayed

C SyntaxError due to incorrect import of Axes3D

D RuntimeError because projection='3d' is not supported

💡 Hint

Check how the projection='3d' argument affects the plot type.

The code creates a 3D scatter plot with the given x, y, z coordinates. The projection='3d' enables 3D plotting in matplotlib.

❓ data_output

intermediate

Number of plotted points in 3D line plot

How many points will be connected by the line in this 3D plot code?

Matplotlib

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

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

x = np.linspace(0, 1, 5)
y = np.linspace(0, 1, 5)
z = np.linspace(0, 1, 5)

ax.plot(x, y, z)
plt.show()

A 5

B 3

C 1

D 0

💡 Hint

Count the number of points generated by np.linspace.

np.linspace(0,1,5) generates 5 points evenly spaced between 0 and 1. The plot connects these 5 points in 3D space.

🔧 Debug

advanced

Identify the error in 3D scatter plot code

What error will this code produce when run?

Matplotlib

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

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

ax.scatter(x, y, z)
plt.show()

A No error; plot displays correctly

B TypeError because lists cannot be used in scatter

C NameError because 'projection' is not a valid argument

D ValueError due to mismatched array lengths

💡 Hint

Check if all coordinate arrays have the same length.

The y list has length 2, while x and z have length 3. This mismatch causes a ValueError when plotting.

❓ visualization

advanced

Effect of changing projection parameter

What visual difference occurs when changing projection='3d' to projection='2d' in this code?

Matplotlib

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

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

x = np.arange(3)
y = np.arange(3)
z = np.arange(3)

ax.plot(x, y, z)
plt.show()

A Changing to projection='2d' causes a TypeError because '2d' is invalid

B Changing to projection='2d' creates a scatter plot instead of a line plot

C Changing to projection='2d' creates a 2D line plot ignoring z values

D Changing to projection='2d' creates a 3D plot with default projection

💡 Hint

Consider how matplotlib handles 2D vs 3D axes.

projection='2d' is not a valid argument; matplotlib defaults to 2D plotting ignoring z values, so the plot is 2D line connecting (x,y).

🧠 Conceptual

expert

Understanding 3D axes creation in matplotlib

Which statement correctly describes how matplotlib creates 3D axes with projection='3d'?

A projection='3d' creates a special Axes3D object that supports 3D plotting methods

B projection='3d' is a style setting that changes colors but keeps 2D axes

C projection='3d' automatically imports mpl_toolkits.mplot3d and adds 3D data support

D projection='3d' converts 2D plots into 3D by adding a z-axis with zeros

💡 Hint

Think about what kind of object is created with projection='3d'.

Setting projection='3d' creates an Axes3D object that has methods for 3D plotting, different from regular 2D axes.

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'