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

Need a 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

Need a 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

Need a 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

Need a hint?

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