3D scatter plots in Matplotlib - Step-by-Step Execution

Concept Flow - 3D scatter plots

Import matplotlib and mpl_toolkits

↓

Create 3D Axes object

↓

Prepare x, y, z data arrays

↓

Call scatter() with x, y, z

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Render 3D scatter plot

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Show plot window

The flow starts by importing needed libraries, then creating a 3D plot area, preparing data points, plotting them in 3D, and finally displaying the plot.

Execution Sample

Matplotlib

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

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

x = [1, 2, 3]
y = [4, 5, 6]
z = [7, 8, 9]

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

This code creates a 3D scatter plot with three points at coordinates (1,4,7), (2,5,8), and (3,6,9).

Execution Table

Step	Action	Variable/Function	Value/Result	Notes
1	Import matplotlib.pyplot	plt	module loaded	Ready for plotting
2	Import Axes3D from mpl_toolkits.mplot3d	Axes3D	class loaded	Enables 3D plotting
3	Create figure	fig	Figure object created	Canvas for plots
4	Add 3D subplot	ax	3D Axes object	Plotting area with 3D projection
5	Define x data	x	[1, 2, 3]	X coordinates of points
6	Define y data	y	[4, 5, 6]	Y coordinates of points
7	Define z data	z	[7, 8, 9]	Z coordinates of points
8	Call ax.scatter(x, y, z)	scatter plot	3 points plotted	Points placed in 3D space
9	Call plt.show()	plot window	3D scatter plot displayed	User sees the plot
10	End	-	-	Plot window open, execution stops

💡 Execution stops after displaying the 3D scatter plot window.

Variable Tracker

Variable	Start	After Step 5	After Step 6	After Step 7	Final
x	undefined	[1, 2, 3]	[1, 2, 3]	[1, 2, 3]	[1, 2, 3]
y	undefined	undefined	[4, 5, 6]	[4, 5, 6]	[4, 5, 6]
z	undefined	undefined	undefined	[7, 8, 9]	[7, 8, 9]
fig	undefined	Figure object	Figure object	Figure object	Figure object
ax	undefined	undefined	undefined	3D Axes object	3D Axes object

Key Moments - 3 Insights

Why do we need to import Axes3D from mpl_toolkits.mplot3d?

Axes3D enables 3D plotting capabilities. Without it, the 'projection="3d"' argument in add_subplot would not work. See execution_table step 2 and 4.

What does ax.scatter(x, y, z) do exactly?

It plots points in 3D space using the x, y, and z coordinates. Each list element corresponds to one point. See execution_table step 8.

Why do we call plt.show() at the end?

plt.show() opens the plot window so we can see the graph. Without it, the plot is created but not displayed. See execution_table step 9.

Visual Quiz - 3 Questions

Look at the execution_table at step 7, what is the value of variable z?

A [7, 8, 9]

B [1, 2, 3]

C [4, 5, 6]

D undefined

💡 Hint

Check the 'Value/Result' column for step 7 in execution_table.

At which step is the 3D Axes object created?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look at the 'Action' and 'Variable/Function' columns in execution_table.

If we skip plt.show(), what happens?

A The plot window will still open automatically.

B The plot will be created but not displayed.

C An error will occur at ax.scatter().

D The data points will not be plotted.

💡 Hint

Refer to key_moments explanation about plt.show() and execution_table step 9.

Concept Snapshot

3D scatter plots with matplotlib:
- Import Axes3D for 3D support
- Create figure and 3D axes: fig.add_subplot(111, projection='3d')
- Prepare x, y, z data lists
- Use ax.scatter(x, y, z) to plot points
- Call plt.show() to display the plot

Full Transcript

This visual execution traces how to create a 3D scatter plot using matplotlib in Python. First, we import matplotlib.pyplot and Axes3D from mpl_toolkits.mplot3d to enable 3D plotting. Then, we create a figure object and add a 3D subplot to it. Next, we define three lists x, y, and z containing coordinates of points. We call ax.scatter(x, y, z) to plot these points in 3D space. Finally, plt.show() displays the plot window so the user can see the 3D scatter plot. Variables like x, y, z, fig, and ax are initialized and updated step-by-step. Key moments include understanding why Axes3D is needed, what ax.scatter does, and the role of plt.show(). The execution table shows each step's action and result, helping beginners follow the process visually.

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