Why categorical visualization matters in Matplotlib - See It in Action

Why categorical visualization matters

📖 Scenario: Imagine you work in a small bakery. You want to understand which types of bread sell the most each day. You have data about bread types and how many were sold. Visualizing this data helps you see patterns quickly.

🎯 Goal: You will create a simple bar chart using matplotlib to visualize sales of different bread types. This will help you understand why categorical visualization is important for comparing groups.

📋 What You'll Learn

Create a dictionary with bread types and their sales

Set a color for the bars

Use a bar chart to show sales by bread type

Display the chart with labels

💡 Why This Matters

🌍 Real World

Businesses often need to compare categories like product sales, customer types, or survey answers. Visual charts make these comparisons clear and fast.

💼 Career

Data analysts and scientists use categorical visualizations to communicate insights and support decisions in marketing, sales, and operations.

1

Create the sales data dictionary

Create a dictionary called bread_sales with these exact entries: 'Sourdough': 30, 'Baguette': 45, 'Rye': 20, 'Whole Wheat': 35.

Matplotlib

# Create the bread_sales dictionary with exact values
# Your code here

Need a hint?

Use curly braces {} to create the dictionary with keys as bread types and values as sales numbers.

2

Set the bar color

Create a variable called bar_color and set it to the string 'skyblue' to use as the color for the bars in the chart.

Matplotlib

bread_sales = {'Sourdough': 30, 'Baguette': 45, 'Rye': 20, 'Whole Wheat': 35}
# Set the bar_color variable to 'skyblue'
# Your code here

Need a hint?

Assign the string 'skyblue' to the variable bar_color.

3

Create the bar chart

Use matplotlib.pyplot to create a bar chart. Use plt.bar() with the keys of bread_sales as categories and the values as heights. Use the bar_color variable for the color. Import matplotlib.pyplot as plt.

Matplotlib

bread_sales = {'Sourdough': 30, 'Baguette': 45, 'Rye': 20, 'Whole Wheat': 35}
bar_color = 'skyblue'
# Import matplotlib.pyplot as plt and create a bar chart with bread types and sales
# Your code here

Need a hint?

Use plt.bar() with bread_sales.keys() and bread_sales.values(). Pass color=bar_color.

4

Show the chart with labels

Add labels for the x-axis as 'Bread Type' and y-axis as 'Number Sold' using plt.xlabel() and plt.ylabel(). Then use plt.show() to display the chart.

Matplotlib

import matplotlib.pyplot as plt

bread_sales = {'Sourdough': 30, 'Baguette': 45, 'Rye': 20, 'Whole Wheat': 35}
bar_color = 'skyblue'

plt.bar(bread_sales.keys(), bread_sales.values(), color=bar_color)
# Add x-axis label, y-axis label, and show the plot
# Your code here

Need a hint?

Use plt.xlabel('Bread Type') and plt.ylabel('Number Sold'). Then call plt.show() to display the chart.