Displaying images with imshow in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What does this imshow code display?

Consider this Python code using matplotlib to display an image:

import matplotlib.pyplot as plt
import numpy as np

image = np.array([[0, 1], [2, 3]])
plt.imshow(image, cmap='gray')
plt.colorbar()
plt.show()

What will the displayed image look like?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

image = np.array([[0, 1], [2, 3]])
plt.imshow(image, cmap='gray')
plt.colorbar()
plt.show()

A A 2x2 grayscale image with all pixels the same shade of gray

B A 2x2 grayscale image with the darkest pixel at top-left and brightest at bottom-right

C An error because the image array is not 3-dimensional

D A 2x2 color image with random colors

💡 Hint

Think about how imshow maps numbers to colors using the grayscale colormap.

The imshow function shows the 2x2 array as a grayscale image. The smallest value (0) is black, the largest (3) is white, and values in between are shades of gray. So top-left is darkest, bottom-right is brightest.

❓ data_output

intermediate

What is the shape of the image shown?

Given this code snippet:

import matplotlib.pyplot as plt
import numpy as np

image = np.random.rand(100, 150, 3)
plt.imshow(image)
plt.show()

What is the shape of the image array being displayed?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

image = np.random.rand(100, 150, 3)
plt.imshow(image)
plt.show()

A (100, 150, 3)

B (150, 100, 3)

C (100, 150)

D (3, 100, 150)

💡 Hint

Remember the shape format for color images is (height, width, color_channels).

The image array has 100 rows (height), 150 columns (width), and 3 color channels (RGB), so its shape is (100, 150, 3).

❓ visualization

advanced

Which option produces a heatmap with a colorbar?

Which code snippet correctly displays a 10x10 heatmap with a colorbar using imshow?

A

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(10,10)
plt.imshow(data, cmap='hot')
plt.colorbar(data)
plt.show()

B

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(10,10)
plt.imshow(data)
plt.colorbar('hot')
plt.show()

C

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(10,10)
plt.imshow(data, cmap='hot')
plt.colorbar(cmap='hot')
plt.show()

D

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(10,10)
plt.imshow(data, cmap='hot')
plt.colorbar()
plt.show()

💡 Hint

Check how colorbar() is called and how colormaps are assigned.

Option D correctly sets the colormap in imshow and calls plt.colorbar() without arguments to add the colorbar. Other options misuse colorbar arguments causing errors or ignoring the colormap.

🔧 Debug

advanced

What error does this code raise?

Analyze this code snippet:

import matplotlib.pyplot as plt
import numpy as np

image = np.array([1, 2, 3, 4])
plt.imshow(image)
plt.show()

What error will occur when running this?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

image = np.array([1, 2, 3, 4])
plt.imshow(image)
plt.show()

A No error, displays a 1D image

B TypeError: Image data must be 2D or 3D array

C ValueError: Invalid shape for image data

D IndexError: index out of bounds

💡 Hint

Check the shape requirements for imshow input arrays.

imshow requires a 2D array (grayscale) or 3D array (color). A 1D array like [1,2,3,4] is invalid and raises a ValueError about shape.

🚀 Application

expert

How to display an RGB image with transparency using imshow?

You have an RGB image stored as a NumPy array img with shape (100, 100, 3). You want to display it with 50% transparency over a white background using imshow. Which code snippet achieves this?

A

plt.imshow(img, alpha=0.5)
plt.gca().set_facecolor('white')
plt.show()

B

plt.imshow(img)
plt.alpha(0.5)
plt.show()

C

plt.imshow(img, transparency=0.5)
plt.show()

D

plt.imshow(img, alpha=0.5, background='white')
plt.show()

💡 Hint

Check how to set transparency and background color in matplotlib.

Option A correctly sets the alpha parameter for transparency and sets the axes background color to white. Other options use invalid parameters or functions.