Why annotations tell the data story in Matplotlib - See It in Action

Why annotations tell the data story

📖 Scenario: Imagine you are a data analyst presenting sales data to your team. You want to highlight important points on your sales chart so everyone understands the story behind the numbers.

🎯 Goal: You will create a simple sales line chart and add annotations to explain key points. This will help you learn how annotations make data easier to understand.

📋 What You'll Learn

Create a dictionary called monthly_sales with exact sales data for 6 months

Create a list called months with the month names in order

Create a matplotlib line plot of sales over months

Add at least two annotations to highlight important sales points

Display the plot with annotations

💡 Why This Matters

🌍 Real World

Annotations help explain charts in reports or presentations so viewers quickly understand key insights.

💼 Career

Data analysts and scientists use annotations to communicate findings clearly to non-technical audiences.

1

Create the sales data

Create a dictionary called monthly_sales with these exact entries: 'Jan': 150, 'Feb': 200, 'Mar': 180, 'Apr': 220, 'May': 210, 'Jun': 230. Also create a list called months with these exact values in order: 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'.

Matplotlib

# Create the monthly_sales dictionary and months list
# Your code here

Need a hint?

Use curly braces {} to create the dictionary and square brackets [] to create the list.

2

Prepare the sales values for plotting

Create a list called sales_values that contains the sales numbers from monthly_sales in the order of the months list.

Matplotlib

monthly_sales = {'Jan': 150, 'Feb': 200, 'Mar': 180, 'Apr': 220, 'May': 210, 'Jun': 230}
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
# Create sales_values list from monthly_sales using months order
# Your code here

Need a hint?

Use a list comprehension to get sales values in the order of months.

3

Create the sales line plot with annotations

Import matplotlib.pyplot as plt. Create a line plot using months on the x-axis and sales_values on the y-axis. Add two annotations: one at 'Feb' with text 'Sales jump' pointing to the point, and one at 'Apr' with text 'Peak sales' pointing to the point.

Matplotlib

monthly_sales = {'Jan': 150, 'Feb': 200, 'Mar': 180, 'Apr': 220, 'May': 210, 'Jun': 230}
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales_values = [monthly_sales[month] for month in months]
# Import matplotlib.pyplot as plt
# Create line plot of sales_values over months
# Add annotations for 'Feb' and 'Apr'
# Your code here

Need a hint?

Use plt.plot() to create the line chart and plt.annotate() to add text with arrows.

4

Display the plot

Use plt.show() to display the plot with the annotations.

Matplotlib

import matplotlib.pyplot as plt

monthly_sales = {'Jan': 150, 'Feb': 200, 'Mar': 180, 'Apr': 220, 'May': 210, 'Jun': 230}
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales_values = [monthly_sales[month] for month in months]

plt.plot(months, sales_values, marker='o')
plt.annotate('Sales jump', xy=(1, 200), xytext=(1, 170), arrowprops=dict(facecolor='black', shrink=0.05))
plt.annotate('Peak sales', xy=(3, 220), xytext=(3, 190), arrowprops=dict(facecolor='red', shrink=0.05))
# Display the plot
# Your code here

Need a hint?

Call plt.show() to open the plot window.