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

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple 3D wireframe plot code

What will be the output of the following code snippet that creates a 3D wireframe plot?

Matplotlib

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

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

x = np.linspace(-5, 5, 10)
y = np.linspace(-5, 5, 10)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

ax.plot_wireframe(X, Y, Z)
plt.show()

A A 2D line plot of sine wave

B A bar chart with 3D bars

C A scatter plot with random points in 3D space

D A 3D wireframe plot showing a circular wave pattern with peaks and troughs

💡 Hint

Think about what plot_wireframe does with meshgrid data in 3D.

The code creates a 3D wireframe plot of the function sin(sqrt(x^2 + y^2)), which looks like circular waves radiating from the center.

❓ data_output

intermediate

Number of lines in a 3D wireframe plot

Given the following code, how many wireframe lines will be drawn along the X and Y directions respectively?

Matplotlib

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

x = np.linspace(0, 1, 15)
y = np.linspace(0, 1, 10)
X, Y = np.meshgrid(x, y)
Z = X * Y

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_wireframe(X, Y, Z)
plt.show()

A 10 lines along X and 15 lines along Y

B 14 lines along X and 9 lines along Y

C 15 lines along X and 10 lines along Y

D 16 lines along X and 11 lines along Y

💡 Hint

The number of lines corresponds to the number of points in each meshgrid dimension.

The meshgrid creates 15 points along X and 10 points along Y, so the wireframe draws 10 lines along X and 15 lines along Y.

🔧 Debug

advanced

Identify the error in 3D wireframe plot code

What error will this code produce when run, and why?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

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

x = np.linspace(-3, 3, 20)
y = np.linspace(-3, 3, 20)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) + np.cos(Y)

ax.plot_wireframe(X, Y, Z)
plt.show()

A No error, the code runs and shows a 3D wireframe plot

B NameError: name 'Axes3D' is not defined

C TypeError: plot_wireframe() missing 1 required positional argument

D AttributeError: 'AxesSubplot' object has no attribute 'plot_wireframe'

💡 Hint

Check if the projection='3d' is correctly set and if the imports are sufficient.

The code imports matplotlib.pyplot and numpy correctly, uses fig.add_subplot with projection='3d', so ax is a 3D axes object with plot_wireframe method. No error occurs.

❓ visualization

advanced

Effect of changing stride parameters in wireframe plot

What is the visual effect of changing the stride parameters in the plot_wireframe method as shown below?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

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

x = np.linspace(-5, 5, 50)
y = np.linspace(-5, 5, 50)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

ax.plot_wireframe(X, Y, Z, rstride=10, cstride=5)
plt.show()

A The wireframe lines are denser along rows and sparser along columns

B The wireframe lines are spaced more sparsely along rows (rstride=10) and more densely along columns (cstride=5)

C The plot shows a filled surface instead of wireframe

D The plot will raise a ValueError due to invalid stride values

💡 Hint

rstride controls row spacing, cstride controls column spacing of wireframe lines.

rstride=10 means every 10th row is drawn, so lines are spaced sparsely along rows; cstride=5 means every 5th column is drawn, so lines are denser along columns.

🚀 Application

expert

Using 3D wireframe plots to analyze terrain elevation data

You have elevation data of a terrain stored in a 2D numpy array `elevation` with shape (100, 100). Which code snippet correctly creates a 3D wireframe plot of this terrain assuming x and y coordinates range from 0 to 10?

A

import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.linspace(0, 10, 100)
y = np.linspace(0, 10, 100)
X, Y = np.meshgrid(x, y)
ax.plot_wireframe(X, Y, elevation.T)
plt.show()

B

import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.linspace(0, 10, 100)
y = np.linspace(0, 10, 100)
X, Y = np.meshgrid(y, x)
ax.plot_wireframe(X, Y, elevation)
plt.show()

C

import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.linspace(0, 10, 100)
y = np.linspace(0, 10, 100)
X, Y = np.meshgrid(x, y)
ax.plot_wireframe(X, Y, elevation)
plt.show()

D

import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.linspace(0, 10, 100)
y = np.linspace(0, 10, 100)
X, Y = np.meshgrid(x, y)
ax.plot_wireframe(Y, X, elevation)
plt.show()

💡 Hint

Remember that meshgrid creates X and Y with shape matching (len(y), len(x)) and elevation shape must align accordingly.

np.meshgrid(x, y) creates X and Y with shape (100, 100) matching the elevation array shape (100, 100). The indexing aligns such that elevation[i,j] corresponds to the position (X[i,j], Y[i,j]), so no transposition is needed.