Viewing angle control in Matplotlib - Step-by-Step Execution

Concept Flow - Viewing angle control

Create 3D plot

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Set viewing angle: azim, elev

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Render plot with new angle

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Visualize rotated 3D data

We create a 3D plot, set the viewing angles (azimuth and elevation), then render the plot to see the rotated view.

Execution Sample

Matplotlib

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], [7,8,9])
ax.view_init(elev=30, azim=45)
plt.show()

This code creates a 3D scatter plot and sets the viewing angle to elevation 30° and azimuth 45°.

Execution Table

Step	Action	Parameter	Effect on View	Output
1	Create figure	fig = plt.figure()	Prepare canvas	Empty 3D plot ready
2	Add 3D subplot	ax = fig.add_subplot(111, projection='3d')	3D axes created	Axes ready for 3D data
3	Plot points	ax.scatter([1,2,3], [4,5,6], [7,8,9])	Points placed in 3D space	Points visible in default view
4	Set view angle	ax.view_init(elev=30, azim=45)	Rotate view to elev=30°, azim=45°	View changes to new angle
5	Show plot	plt.show()	Render plot window	3D scatter plot displayed with new angle
6	Exit	Plot window closed	End of visualization	Execution stops

💡 Plot window closed by user, ending visualization

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
fig	None	Figure object created	Figure object created	Figure object created	Figure object created
ax	None	3D Axes object created	3D Axes with points plotted	3D Axes with view angle set	3D Axes with view angle set

Key Moments - 2 Insights

Why does changing azim and elev in view_init rotate the plot instead of moving the points?

Because view_init changes the camera angle, not the data coordinates. The points stay fixed; only the viewpoint changes (see execution_table step 4).

What happens if you call view_init before plotting points?

The view angle is set before points are drawn, so the final plot still shows points from that angle (the order does not affect the final view, see variable_tracker).

Visual Quiz - 3 Questions

Look at the execution table at step 4. What does ax.view_init(elev=30, azim=45) do?

A It changes the viewing angle of the plot

B It moves the points to new coordinates

C It changes the color of the points

D It creates a new figure

💡 Hint

Refer to the 'Effect on View' column in step 4 of the execution_table

According to variable_tracker, what is the state of 'ax' after step 3?

A 3D Axes object created

B 3D Axes with points plotted

C None

D 3D Axes with view angle set

💡 Hint

Check the 'ax' row under 'After Step 3' in variable_tracker

If you change elev to 90 in view_init, what will happen to the plot view?

A The plot will be viewed from the side

B The plot will be viewed from below

C The plot will be viewed from directly above

D The plot will not change

💡 Hint

Elevation controls vertical angle; 90 means looking straight down (see concept_flow)

Concept Snapshot

Viewing angle control in matplotlib 3D plots:
- Use ax.view_init(elev, azim) to set elevation and azimuth angles
- Elevation (elev) is vertical angle in degrees
- Azimuth (azim) is horizontal rotation in degrees
- Changes the camera view, not the data
- Call before plt.show() to see effect

Full Transcript

This lesson shows how to control the viewing angle of a 3D plot in matplotlib. We start by creating a figure and adding 3D axes. Then we plot points in 3D space. Using ax.view_init with elevation and azimuth angles, we rotate the camera view around the data. Finally, we render the plot with plt.show(). The points stay fixed; only the viewpoint changes. This helps us see the data from different angles easily.