3D axes with projection='3d' in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a 3D axes object using matplotlib.

Matplotlib

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection=[1])

Drag options to blanks, or click blank then click option'

A'3d'

B'xyz'

C'2d'

D'3D'

Attempts:

3 left

💡 Hint

Common Mistakes

Using '3D' instead of '3d' (case sensitive).

Using '2d' which creates a 2D plot.

The correct projection string to create 3D axes is '3d'. It tells matplotlib to create a 3D plot.

2fill in blank

medium

Complete the code to plot a 3D scatter plot with points at coordinates (1,2,3).

Matplotlib

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter([1], 2, 3)
plt.show()

Drag options to blanks, or click blank then click option'

A'1'

B(1,)

C1

D[1]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string '1' instead of a number.

Using a list or tuple when a single number is enough.

The scatter method expects numbers or arrays for coordinates. Using 1 as a number is correct for a single point.

3fill in blank

hard

Fix the error in the code to correctly set labels for the 3D axes.

Matplotlib

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.[1]('Z axis')
plt.show()

Drag options to blanks, or click blank then click option'

Aset_z_axis

Blabel_z

Cset_z

Dset_zlabel

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like set_z_axis or label_z.

Forgetting to set the Z label.

The correct method to set the Z axis label in a 3D plot is set_zlabel.

4fill in blank

hard

Fill both blanks to create a 3D line plot with points (1,2,3) and (4,5,6).

Matplotlib

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection=[1])
ax.plot([1, 4], [2, 5], [2])
plt.show()

Drag options to blanks, or click blank then click option'

A'3d'

B[3, 6]

C[3, 5]

D[4, 6]

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong projection strings like '3D'.

Using incorrect z coordinate lists.

The projection must be '3d' to create 3D axes. The z coordinates for the line plot are [3, 6].

5fill in blank

hard

Fill all three blanks to create a 3D surface plot of Z = X^2 + Y^2 over a grid.

Matplotlib

import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection=[1])
X = np.linspace(-1, 1, 10)
Y = np.linspace(-1, 1, 10)
X, Y = np.meshgrid(X, Y)
Z = X[2]2 + Y[3]2
ax.plot_surface(X, Y, Z)
plt.show()

Drag options to blanks, or click blank then click option'

A'3d'

B**

C^

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '^' which is bitwise XOR, not power.

Using '*' which is multiplication, not power.

The projection must be '3d' for 3D plotting. The power operator in Python is **, so X**2 and Y**2 compute squares.

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'