Heatmap with plt.imshow in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of plt.imshow with a simple 2D array

What will be the color intensity pattern shown by plt.imshow for this 2D array?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

array = np.array([[1, 2], [3, 4]])
plt.imshow(array, cmap='gray')
plt.colorbar()
plt.show()

A A heatmap with the top-left corner darkest and bottom-right corner brightest

B A heatmap with uniform color intensity across all cells

C A heatmap with the top-left corner brightest and bottom-right corner darkest

D A heatmap showing only two colors alternating in a checkerboard pattern

💡 Hint

Remember that in grayscale, lower values are darker and higher values are brighter.

The array values increase from 1 to 4 from top-left to bottom-right. Using 'gray' colormap, smaller values appear darker and larger values brighter, so the heatmap shows a gradient from dark (top-left) to bright (bottom-right).

❓ data_output

intermediate

Shape of the array displayed by plt.imshow

Given this code, what is the shape of the array that plt.imshow displays as a heatmap?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

data = np.random.rand(5, 3)
plt.imshow(data)
plt.show()

A (5, 3)

B (3, 5)

C (5, 5)

D (3, 3)

💡 Hint

Check the shape of the numpy array before plotting.

The array 'data' is created with shape (5, 3), so plt.imshow displays a heatmap with 5 rows and 3 columns.

❓ visualization

advanced

Effect of aspect parameter in plt.imshow

What visual effect does setting aspect='auto' have on the heatmap created by plt.imshow compared to the default aspect setting?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

matrix = np.arange(12).reshape(3,4)
plt.subplot(1,2,1)
plt.title('Default aspect')
plt.imshow(matrix)
plt.subplot(1,2,2)
plt.title('aspect="auto"')
plt.imshow(matrix, aspect='auto')
plt.show()

A The heatmap disappears when aspect='auto' is used.

B The heatmap cells remain perfect squares in both plots.

C The heatmap colors invert when aspect='auto' is set.

D The heatmap cells stretch to fill the plot area, changing their shape from square to rectangles.

💡 Hint

Aspect controls the ratio of height to width of the heatmap cells.

By default, plt.imshow uses aspect='equal' which keeps cells square. Setting aspect='auto' lets cells stretch to fill the axes, changing their shape to rectangles if the axes shape is not square.

🔧 Debug

advanced

Identify the error in this plt.imshow heatmap code

What error will this code produce when run?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

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

A No error, heatmap displays correctly

B TypeError: Unsupported data type for imshow

C ValueError: Input must be 2D array, got 1D array instead

D IndexError: index out of bounds

💡 Hint

Check the shape of the array passed to plt.imshow.

plt.imshow requires a 2D array to display a heatmap. Passing a 1D array causes a ValueError indicating the input must be 2D.

🚀 Application

expert

Interpreting a heatmap with plt.imshow and custom colormap

You create a heatmap with plt.imshow using this code. What color corresponds to the highest value in the data?

Matplotlib

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

values = np.array([[0, 1], [2, 3]])
colors = [(0, 'blue'), (0.5, 'white'), (1, 'red')]
custom_cmap = LinearSegmentedColormap.from_list('custom', colors)
plt.imshow(values, cmap=custom_cmap)
plt.colorbar()
plt.show()

A White

B Red

C Blue

D Black

💡 Hint

Look at how the colormap maps values from 0 to 1 to colors.

The colormap maps the lowest value (0) to blue, middle value (1.5) to white, and highest value (3) normalized to 1 to red. So the highest value appears red.