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.

Practice

1. What does the plt.imshow function do in matplotlib when given a 2D array?

easy

A. It prints the array values as text on the plot.

B. It plots a line graph of the array values.

C. It creates a heatmap by showing the array values as colors.

D. It creates a pie chart from the array values.

Solution

1. Step 1: Understand plt.imshow input

   The function takes a 2D array and maps its values to colors in a grid.

2. Step 2: Recognize heatmap behavior

   This color mapping creates a heatmap, showing patterns visually.

3. Final Answer:

   It creates a heatmap by showing the array values as colors. -> Option C

4. Quick Check:

   plt.imshow = heatmap colors [OK]

Hint: plt.imshow shows 2D data as colored grids [OK]

Common Mistakes:

• Thinking plt.imshow plots lines or text
• Confusing heatmap with pie chart
• Assuming plt.imshow prints numbers

2. Which of these is the correct way to add a color bar to a heatmap created with plt.imshow?

easy

A. plt.colorbar()

B. plt.addcolor()

C. plt.showcolor()

D. plt.colorheat()

Solution

1. Step 1: Recall color bar function

   Matplotlib uses plt.colorbar() to add a color scale next to images.

2. Step 2: Check other options

   Other options are not valid matplotlib functions.

3. Final Answer:

   plt.colorbar() -> Option A

4. Quick Check:

   Color bar function = plt.colorbar() [OK]

Hint: Use plt.colorbar() to add color scale [OK]

Common Mistakes:

• Using non-existent functions like plt.addcolor()
• Forgetting to call plt.colorbar() after plt.imshow()
• Trying to add color bar before plotting heatmap

3. What will the following code display?

import matplotlib.pyplot as plt
import numpy as np

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

medium

A. A scatter plot of points (1,2) and (3,4) with hot colors.

B. A line plot of the numbers 1, 2, 3, 4 with a color bar.

C. An error because cmap='hot' is invalid.

D. A 2x2 heatmap with colors from black to red to yellow, and a color bar showing values 1 to 4.

Solution

1. Step 1: Understand plt.imshow with cmap='hot'

   The data array is 2x2, so plt.imshow creates a 2x2 grid colored by values using the 'hot' color map (black to red to yellow).

2. Step 2: Effect of plt.colorbar()

   The color bar shows the scale from the minimum value 1 to maximum 4, helping interpret colors.

3. Final Answer:

   A 2x2 heatmap with colors from black to red to yellow, and a color bar showing values 1 to 4. -> Option D

4. Quick Check:

   plt.imshow + cmap='hot' + colorbar = heatmap with hot colors [OK]

Hint: cmap='hot' colors low to high values black-red-yellow [OK]

Common Mistakes:

• Thinking plt.imshow plots lines or scatter
• Believing cmap='hot' causes error
• Ignoring color bar meaning

4. The code below is intended to show a heatmap but does not display the color bar. What is the error?

import matplotlib.pyplot as plt
import numpy as np

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

medium

A. plt.colorbar is missing parentheses, so color bar is not shown.

B. plt.imshow needs a cmap argument to show color bar.

C. plt.show() must come before plt.colorbar().

D. np.random.rand(3,3) creates invalid data for heatmap.

Solution

1. Step 1: Identify plt.colorbar usage

   The code uses plt.colorbar without parentheses, so the function is not called.

2. Step 2: Correct function call

   Adding parentheses plt.colorbar() calls the function and displays the color bar.

3. Final Answer:

   plt.colorbar is missing parentheses, so color bar is not shown. -> Option A

4. Quick Check:

   Call plt.colorbar() with () to show color bar [OK]

Hint: Always call plt.colorbar() with parentheses [OK]

Common Mistakes:

• Forgetting parentheses on plt.colorbar
• Thinking cmap is required for color bar
• Calling plt.show() before plt.colorbar

5. You have a 4x4 matrix with values from 0 to 15. You want to create a heatmap using plt.imshow that shows the values clearly with a color bar, but you want the color scale to always be from 0 to 20 regardless of data. Which code snippet correctly does this?

hard

A. plt.imshow(matrix, vmin=0, vmax=15)
plt.colorbar()
plt.show()

B. plt.imshow(matrix, vmin=0, vmax=20)
plt.colorbar()
plt.show()

C. plt.imshow(matrix, cmap='gray')
plt.colorbar()
plt.show()

D. plt.imshow(matrix)
plt.colorbar(vmin=0, vmax=20)
plt.show()

Solution

1. Step 1: Set color scale limits in plt.imshow

   Use vmin=0 and vmax=20 in plt.imshow to fix color scale from 0 to 20.

2. Step 2: Add color bar normally

   Call plt.colorbar() to show the color scale matching the fixed limits.

3. Final Answer:

   plt.imshow(matrix, vmin=0, vmax=20) plt.colorbar() plt.show() -> Option B

4. Quick Check:

   Set vmin/vmax in imshow, then add colorbar [OK]

Hint: Use vmin and vmax in plt.imshow to fix color scale [OK]

Common Mistakes:

• Setting vmin/vmax in plt.colorbar instead of plt.imshow
• Not setting vmax to 20, so scale auto adjusts
• Using wrong vmin/vmax values