3D scatter plots in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of 3D scatter plot with color mapping

What will be the color of the point at coordinates (2, 3, 4) in the 3D scatter plot generated by the code below?

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 = [1, 2, 3]
y = [2, 3, 4]
z = [3, 4, 5]
colors = ['red', 'green', 'blue']

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

A Yellow

B Green

C Blue

D Red

💡 Hint

Colors are assigned in order to the points based on the list provided.

The second point has coordinates (2, 3, 4) and the color list assigns 'green' to the second point.

❓ data_output

intermediate

Number of points plotted in 3D scatter plot

How many points will be plotted by the following 3D scatter 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, 10)
y = np.linspace(0, 1, 10)
z = np.linspace(0, 1, 10)

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

A 1

B 1000

C 30

D 10

💡 Hint

The scatter plot plots points for each index in the arrays.

Each array has 10 points, and scatter plots points pairwise by index, so 10 points are plotted.

❓ visualization

advanced

Effect of marker size on 3D scatter plot

Which option shows the correct effect of changing marker size in a 3D scatter plot?

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([2, 3, 4])
z = np.array([3, 4, 5])

ax.scatter(x, y, z, s=100)
plt.show()

A Points appear larger than default size

B Points are connected by lines

C Points are invisible

D Points appear smaller than default size

💡 Hint

The 's' parameter controls marker size in scatter plots.

Setting s=100 increases marker size making points larger than default.

🔧 Debug

advanced

Identify the error in 3D scatter plot code

What error will the following code produce when run?

Matplotlib

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

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()

A No error, plot displays correctly

B AttributeError: 'AxesSubplot' object has no attribute 'scatter'

C TypeError: scatter() missing 1 required positional argument: 'zs'

D ValueError: x, y, and z must be the same length

💡 Hint

Check if the subplot is created with 3D projection.

The subplot is created without 3D projection, so scatter expects only x and y, but z is given causing TypeError.

🚀 Application

expert

Interpreting clustering from 3D scatter plot data

Given the 3D scatter plot data below, which cluster center is closest to the point (5, 5, 5)?

Matplotlib

import numpy as np

points = np.array([[1, 2, 3], [5, 5, 5], [9, 8, 7]])
cluster_centers = np.array([[0, 0, 0], [5, 5, 6], [10, 10, 10]])

# Calculate distances from point (5,5,5) to each cluster center
point = np.array([5, 5, 5])
distances = np.linalg.norm(cluster_centers - point, axis=1)
closest_index = np.argmin(distances)
closest_center = cluster_centers[closest_index]
closest_center

A [5 5 6]

B [0 0 0]

C [10 10 10]

D [1 2 3]

💡 Hint

Distance is shortest between points closest in all three dimensions.

The cluster center [5, 5, 6] is closest to point [5, 5, 5] with distance 1, less than others.

Practice

1. What is the main purpose of using a 3D scatter plot in matplotlib?

easy

A. To display text annotations in 3D space

B. To visualize data points in three dimensions and observe patterns

C. To plot a line graph with three lines

D. To create a bar chart with three bars

Solution

1. Step 1: Understand the role of 3D scatter plots

   3D scatter plots show points in three dimensions, helping to see relationships among three variables.

2. Step 2: Compare with other plot types

   Bar charts and line graphs do not show points in 3D space, and text annotations are not the main purpose.

3. Final Answer:

   To visualize data points in three dimensions and observe patterns -> Option B

4. Quick Check:

   3D scatter plots = visualize points in 3D [OK]

Hint: 3D scatter plots show points in 3D space [OK]

Common Mistakes:

• Confusing 3D scatter with bar or line plots
• Thinking 3D scatter is for text annotations
• Assuming 3D scatter plots show continuous surfaces

2. Which of the following is the correct way to create a 3D scatter plot axis in matplotlib?

easy

A. ax = plt.axes(projection='3d')

B. ax = plt.subplots(projection='3d')

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

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

Solution

1. Step 1: Recall how to create 3D axes

   In matplotlib, plt.axes(projection='3d') creates a 3D axes object.

2. Step 2: Check other options

   plt.subplot and plt.subplots do not accept projection directly; plt.figure creates a figure, not axes.

3. Final Answer:

   ax = plt.axes(projection='3d') -> Option A

4. Quick Check:

   Use plt.axes with projection='3d' for 3D axes [OK]

Hint: Use plt.axes(projection='3d') to get 3D axes [OK]

Common Mistakes:

• Using plt.subplot instead of plt.axes for 3D
• Passing projection to plt.figure instead of axes
• Confusing plt.subplots with plt.subplot

3. What will be the output of this code snippet?

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], c='r', marker='o')
plt.show()

medium

A. A 3D scatter plot with two red circular points at coordinates (1,3,5) and (2,4,6)

B. A 2D scatter plot with red points

C. A syntax error due to missing import

D. An empty plot with no points

Solution

1. Step 1: Analyze the code for 3D scatter plot creation

   The code creates a figure, adds a 3D subplot, and plots two points with coordinates (1,3,5) and (2,4,6) in red circles.

2. Step 2: Confirm the plot output

   The points will appear in 3D space as red circles; no errors occur.

3. Final Answer:

   A 3D scatter plot with two red circular points at coordinates (1,3,5) and (2,4,6) -> Option A

4. Quick Check:

   3D scatter with given points = red circles at (1,3,5) and (2,4,6) [OK]

Hint: Check coordinates and color for scatter points [OK]

Common Mistakes:

• Thinking it creates 2D plot instead of 3D
• Assuming syntax error without checking imports
• Expecting no points plotted

4. Identify the error in this code that tries to plot a 3D scatter plot:

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

medium

A. scatter function does not accept three arguments

B. The lists for x, y, z have different lengths

C. plt.figure() is not imported correctly

D. Missing projection='3d' in add_subplot, so 3D plotting fails

Solution

1. Step 1: Check subplot creation for 3D

   The code uses fig.add_subplot(111) without projection='3d', so it creates a 2D axes.

2. Step 2: Understand scatter with 3D data

   On 2D axes, passing three lists to scatter will treat the third list as point sizes instead of z-coordinates, producing a 2D scatter plot rather than 3D.

3. Final Answer:

   Missing projection='3d' in add_subplot, so 3D plotting fails -> Option D

4. Quick Check:

   3D scatter needs projection='3d' [OK]

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

Common Mistakes:

• Forgetting projection='3d' in add_subplot
• Thinking scatter can't take three arguments
• Assuming list length mismatch causes error

5. You want to plot a 3D scatter plot with points colored by their z-value using a colormap. Which code snippet correctly achieves this?

hard

A. ax.scatter(x, y, z, c='z', colormap='viridis')

B. ax.scatter(x, y, z, color='z', cmap='viridis')

C. ax.scatter(x, y, z, c=z, cmap='viridis')

D. ax.scatter(x, y, z, colors=z, cmap='viridis')

Solution

1. Step 1: Understand color mapping in scatter

   To color points by a variable, pass that variable to c= and specify cmap for colormap.

2. Step 2: Check correct parameter names

   c is correct for colors; color or colors with string 'z' or colormap are incorrect.

3. Final Answer:

   ax.scatter(x, y, z, c=z, cmap='viridis') -> Option C

4. Quick Check:

   Use c=variable and cmap='name' for color mapping [OK]

Hint: Use c=values and cmap='name' to color points [OK]

Common Mistakes:

• Using color='z' instead of c=z
• Using colormap instead of cmap
• Passing colors=z which is invalid