3D scatter plots in Matplotlib - Mini Project: Build & Apply

3D Scatter Plots

📖 Scenario: You are a data analyst working with a dataset of points in 3D space. You want to visualize these points to understand their distribution and relationships.

🎯 Goal: Create a 3D scatter plot using matplotlib to visualize the points in three dimensions.

📋 What You'll Learn

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

Create a variable color to specify the color of the points.

Use matplotlib to create a 3D scatter plot with the given data and color.

Display the plot using plt.show().

💡 Why This Matters

🌍 Real World

3D scatter plots help scientists and engineers visualize data points in three dimensions, such as geographic locations, sensor data, or experimental results.

💼 Career

Data analysts and data scientists use 3D scatter plots to explore complex datasets and communicate insights visually to teams and stakeholders.

1

Create the 3D data points

Create three lists called x, y, and z with these exact values: x = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11], y = [99, 86, 87, 88, 100, 86, 103, 87, 94, 78], and z = [10, 15, 13, 15, 10, 15, 10, 15, 10, 15].

Matplotlib

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

Hint

Use square brackets to create lists and separate numbers with commas.

2

Set the color for the points

Create a variable called color and set it to the string 'green' to specify the color of the scatter points.

Matplotlib

x = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11]
y = [99, 86, 87, 88, 100, 86, 103, 87, 94, 78]
z = [10, 15, 13, 15, 10, 15, 10, 15, 10, 15]
# Create a variable color and set it to 'green'
# Your code here

Hint

Assign the string 'green' to the variable color using the equals sign.

3

Create the 3D scatter plot

Import matplotlib.pyplot as plt and Axes3D from mpl_toolkits.mplot3d. Then create a figure and add a 3D subplot. Use ax.scatter(x, y, z, c=color) to create the 3D scatter plot with the data and color.

Matplotlib

x = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11]
y = [99, 86, 87, 88, 100, 86, 103, 87, 94, 78]
z = [10, 15, 13, 15, 10, 15, 10, 15, 10, 15]
color = 'green'
# Import matplotlib.pyplot as plt and Axes3D
# Create a figure and add a 3D subplot
# Use ax.scatter(x, y, z, c=color) to plot
# Your code here

Hint

Use plt.figure() to create a figure and fig.add_subplot(111, projection='3d') to add a 3D plot.

4

Display the 3D scatter plot

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

Matplotlib

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

x = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11]
y = [99, 86, 87, 88, 100, 86, 103, 87, 94, 78]
z = [10, 15, 13, 15, 10, 15, 10, 15, 10, 15]
color = 'green'

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z, c=color)
# Display the plot
# Your code here

Hint

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

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