3D plot limitations and alternatives in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the 3D plotting toolkit from matplotlib.

Matplotlib

from mpl_toolkits.mplot3d import [1]

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

AAxes2D

Bplot3d

CAxes3D

Dplot2d

Attempts:

3 left

💡 Hint

Common Mistakes

Using plot3d which does not exist.

Confusing 2D and 3D plotting modules.

The Axes3D module is used to create 3D plots in matplotlib.

2fill in blank

medium

Complete the code to create a 3D scatter plot using matplotlib.

Matplotlib

ax = fig.add_subplot(111, projection=[1])
ax.scatter(x, y, z)

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

A'3d'

B'scatter'

C'2d'

D'plot3d'

Attempts:

3 left

💡 Hint

Common Mistakes

Using '2d' projection for 3D plots.

Passing 'scatter' as projection which is invalid.

The projection='3d' argument tells matplotlib to create a 3D plot.

3fill in blank

hard

Fix the error in the code to correctly set axis labels for a 3D plot.

Matplotlib

ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.[1]('Z axis')

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

Aset_z_axis

Bset_z

Clabel_z

Dset_zlabel

Attempts:

3 left

💡 Hint

Common Mistakes

Using set_z_axis which does not exist.

Trying to use label_z which is invalid.

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

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters points with z > 0 and squares x values.

Matplotlib

filtered_points = {x: x[1]2 for x, z in points.items() if z [2] 0}

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

A**

B>

C<

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using * instead of ** for squaring.

Using < instead of > for filtering.

We square x using **2 and filter points where z is greater than 0 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that converts keys to uppercase, keeps values, and filters values > 10.

Matplotlib

result = [1]: [2] for k, v in data.items() if v [3] 10}

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper().

Using < instead of > for filtering.

We convert keys to uppercase with k.upper(), keep values as v, and filter values greater than 10 with >.

Practice

1. What is a common limitation of 3D plots in matplotlib?

easy

A. They do not support color customization.

B. They cannot display more than two variables.

C. They always run faster than 2D plots.

D. They can be hard to read and interpret clearly.

Solution

1. Step 1: Understand 3D plot complexity

   3D plots show three variables but often become visually complex and hard to interpret.

2. Step 2: Compare with other options

   The other options are incorrect because 3D plots do show three variables, are usually slower, and support color customization.

3. Final Answer:

   They can be hard to read and interpret clearly. -> Option D

4. Quick Check:

   3D plots are complex = A [OK]

Hint: 3D plots often look confusing, so readability is the key issue [OK]

Common Mistakes:

• Thinking 3D plots only show two variables
• Assuming 3D plots are always faster
• Believing 3D plots lack color options

2. Which of the following is the correct way to import the 3D plotting toolkit in matplotlib?

easy

A. from mpl_toolkits.mplot3d import Axes3D

B. from matplotlib import pyplot3d

C. import matplotlib.pyplot as plt3d

D. from matplotlib3d import Axes

Solution

1. Step 1: Recall the standard import for 3D plots

   The correct import for 3D plotting in matplotlib is from mpl_toolkits.mplot3d import Axes3D.

2. Step 2: Check other options for errors

   The other options are invalid module names or incorrect syntax.

3. Final Answer:

   from mpl_toolkits.mplot3d import Axes3D -> Option A

4. Quick Check:

   3D import = B [OK]

Hint: Remember mpl_toolkits.mplot3d for 3D axes import [OK]

Common Mistakes:

• Using pyplot3d which does not exist
• Trying to import matplotlib3d module
• Renaming pyplot incorrectly for 3D

3. What will the following code output?

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

medium

A. A 2D scatter plot ignoring z values

B. A 3D scatter plot showing points where z = x + y

C. SyntaxError due to missing import

D. RuntimeError because z is not defined correctly

Solution

1. Step 1: Analyze the code setup

   The code imports necessary modules, creates a 3D subplot, and defines x, y arrays with 5 points each.

2. Step 2: Understand the plotting command

   It calculates z as x + y element-wise and plots a 3D scatter plot with these points.

3. Final Answer:

   A 3D scatter plot showing points where z = x + y -> Option B

4. Quick Check:

   3D scatter with z = x + y = A [OK]

Hint: Check projection='3d' and scatter usage for 3D plots [OK]

Common Mistakes:

• Thinking it produces 2D plot ignoring z
• Expecting syntax or runtime errors
• Confusing z calculation with undefined variable

4. Identify the error in this 3D plot code snippet:

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
x = [1, 2, 3]
y = [4, 5, 6]
z = [7, 8, 9]
ax.scatter(x, y, z)
plt.show()

medium

A. scatter does not accept three arguments

B. z should be a 2D array

C. Missing projection='3d' in add_subplot

D. plt.show() is missing

Solution

1. Step 1: Check subplot creation

   The subplot is created without specifying projection='3d', so it is a 2D plot.

2. Step 2: Understand scatter usage

   scatter with three arguments requires a 3D axes, which is missing here, causing an error.

3. Final Answer:

   Missing projection='3d' in add_subplot -> Option C

4. Quick Check:

   3D plot needs projection='3d' = C [OK]

Hint: Always add projection='3d' for 3D axes [OK]

Common Mistakes:

• Assuming scatter accepts 3 args on 2D axes
• Thinking z must be 2D array
• Forgetting plt.show() is present

5. You want to visualize a complex dataset with three variables but find 3D plots too cluttered and slow. Which alternative approach is best to clearly show relationships?

hard

A. Use multiple 2D scatter plots showing pairs of variables

B. Plot a single 3D surface plot with all data points

C. Ignore one variable and plot only two variables in 2D

D. Use pie charts for each variable separately

Solution

1. Step 1: Understand 3D plot limitations

   3D plots can be cluttered and slow, making interpretation difficult for complex data.

2. Step 2: Evaluate alternatives

   Multiple 2D scatter plots showing variable pairs allow clearer views of relationships without clutter.

3. Step 3: Reject less effective options

   Single 3D surface plots can still be cluttered; ignoring variables loses info; pie charts do not show relationships well.

4. Final Answer:

   Use multiple 2D scatter plots showing pairs of variables -> Option A

5. Quick Check:

   Clear view with multiple 2D plots = D [OK]

Hint: Break 3D data into multiple 2D plots for clarity [OK]

Common Mistakes:

• Trying to force complex data into one 3D plot
• Dropping variables losing important info
• Using pie charts which don't show variable relations