Heatmap with plt.imshow in Matplotlib - Mini Project: Build & Apply

Heatmap with plt.imshow

📖 Scenario: You work in a small bakery that tracks daily sales of different types of bread. You want to visualize the sales data to see which bread types sell best on which days.

🎯 Goal: Create a heatmap using plt.imshow to visualize the sales of bread types over a week.

📋 What You'll Learn

Create a 2D list called sales with exact sales numbers for 3 bread types over 4 days.

Create a list called days with the names of the 4 days.

Create a list called bread_types with the names of the 3 bread types.

Use plt.imshow to create a heatmap of the sales data.

Add x-axis labels using days and y-axis labels using bread_types.

Add a color bar to show the scale of sales.

💡 Why This Matters

🌍 Real World

Heatmaps are used in many fields like sales analysis, weather data, and biology to quickly see patterns and differences in data.

💼 Career

Knowing how to create heatmaps helps data analysts and scientists communicate complex data visually to teams and decision makers.

1

Create the sales data

Create a 2D list called sales with these exact values: [[10, 15, 7, 12], [5, 8, 6, 9], [12, 18, 14, 20]] representing sales of 3 bread types over 4 days.

Matplotlib

# Create the sales data list
# Your code here

Hint

Think of sales as a table with 3 rows and 4 columns.

2

Create labels for days and bread types

Create a list called days with these exact values: ["Monday", "Tuesday", "Wednesday", "Thursday"] and a list called bread_types with these exact values: ["Sourdough", "Baguette", "Rye"].

Matplotlib

sales = [[10, 15, 7, 12], [5, 8, 6, 9], [12, 18, 14, 20]]
# Create the days and bread_types lists
# Your code here

Hint

Use simple list syntax with exact strings for days and bread types.

3

Create the heatmap with plt.imshow

Import matplotlib.pyplot as plt. Use plt.imshow with the sales data and cmap='YlGn' to create a heatmap. Set x-axis ticks to the range of days and y-axis ticks to the range of bread types. Use plt.xticks with range(len(days)) and days, and plt.yticks with range(len(bread_types)) and bread_types. Add a color bar with plt.colorbar().

Matplotlib

sales = [[10, 15, 7, 12], [5, 8, 6, 9], [12, 18, 14, 20]]
days = ["Monday", "Tuesday", "Wednesday", "Thursday"]
bread_types = ["Sourdough", "Baguette", "Rye"]
# Import matplotlib.pyplot as plt
# Use plt.imshow with sales and cmap='YlGn'
# Set x and y ticks with days and bread_types
# Add a color bar
# Your code here

Hint

Remember to import matplotlib.pyplot as plt before using it.

4

Display the heatmap

Use plt.show() to display the heatmap.

Matplotlib

import matplotlib.pyplot as plt

sales = [[10, 15, 7, 12], [5, 8, 6, 9], [12, 18, 14, 20]]
days = ["Monday", "Tuesday", "Wednesday", "Thursday"]
bread_types = ["Sourdough", "Baguette", "Rye"]

plt.imshow(sales, cmap='YlGn')
plt.xticks(range(len(days)), days)
plt.yticks(range(len(bread_types)), bread_types)
plt.colorbar()
# Display the heatmap
# Your code here

Hint

Use plt.show() to see the heatmap window.

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