Histogram vs bar chart distinction in Matplotlib - Hands-On Comparison

Histogram vs Bar Chart Distinction

📖 Scenario: Imagine you are a data analyst working with sales data and customer age data. You want to understand how to visualize these two types of data correctly using charts.

🎯 Goal: You will create a bar chart to show sales by product category and a histogram to show the distribution of customer ages. This will help you see the difference between these two chart types.

📋 What You'll Learn

Create a dictionary called sales with product categories and their sales numbers.

Create a list called customer_ages with ages of customers.

Set a variable bins to 5 for the histogram.

Use plt.bar() to create a bar chart for sales data.

Use plt.hist() to create a histogram for customer ages.

Display both charts with plt.show().

💡 Why This Matters

🌍 Real World

Bar charts are used to compare categories like product sales, while histograms help understand the distribution of continuous data like customer ages.

💼 Career

Data analysts and scientists use these charts to summarize and communicate data insights clearly to teams and stakeholders.

1

Create sales data and customer ages

Create a dictionary called sales with these exact entries: 'Books': 120, 'Electronics': 90, 'Clothing': 150. Also create a list called customer_ages with these exact values: 22, 25, 47, 35, 46, 52, 23, 43, 36, 27.

Matplotlib

# Create the sales dictionary and customer_ages list
# Your code here

Need a hint?

Use curly braces {} to create the dictionary sales with keys and values. Use square brackets [] to create the list customer_ages.

2

Set number of bins for histogram

Create a variable called bins and set it to 5 to define the number of bins for the histogram.

Matplotlib

sales = {'Books': 120, 'Electronics': 90, 'Clothing': 150}
customer_ages = [22, 25, 47, 35, 46, 52, 23, 43, 36, 27]
# Create a variable bins and set it to 5
# Your code here

Need a hint?

Just write bins = 5 to set the number of bins.

3

Create bar chart and histogram

Import matplotlib.pyplot as plt. Use plt.bar() with sales.keys() and sales.values() to create a bar chart. Then use plt.hist() with customer_ages and bins to create a histogram. Use plt.figure() to create separate figures for each chart.

Matplotlib

sales = {'Books': 120, 'Electronics': 90, 'Clothing': 150}
customer_ages = [22, 25, 47, 35, 46, 52, 23, 43, 36, 27]
bins = 5
# Import matplotlib.pyplot as plt
# Create bar chart and histogram with separate figures
# Your code here

Need a hint?

Use plt.figure() before each chart to separate them. Use plt.bar() for the sales dictionary and plt.hist() for the customer_ages list.

4

Display the charts

Use plt.show() to display both the bar chart and the histogram.

Matplotlib

import matplotlib.pyplot as plt

sales = {'Books': 120, 'Electronics': 90, 'Clothing': 150}
customer_ages = [22, 25, 47, 35, 46, 52, 23, 43, 36, 27]
bins = 5

plt.figure()
plt.bar(sales.keys(), sales.values())
plt.title('Sales by Product Category')

plt.figure()
plt.hist(customer_ages, bins=bins)
plt.title('Customer Age Distribution')
# Display the charts
# Your code here

Need a hint?

Just add plt.show() at the end to display the charts.