Annotating specific points in Matplotlib - Mini Project: Build & Apply

Annotating Specific Points on a Plot

📖 Scenario: You are analyzing sales data over a week. You want to highlight the highest and lowest sales days on a line chart to make the report clearer.

🎯 Goal: Create a line plot of daily sales and annotate the highest and lowest sales points with labels.

📋 What You'll Learn

Create a dictionary with daily sales data

Create variables to find the highest and lowest sales values

Use matplotlib to plot the sales data

Annotate the highest and lowest sales points on the plot

Display the plot

💡 Why This Matters

🌍 Real World

Annotating specific points on charts helps highlight important data in reports and presentations, making it easier for others to understand key insights.

💼 Career

Data analysts and scientists often need to create clear visualizations with annotations to communicate findings effectively to stakeholders.

1

Create the sales data dictionary

Create a dictionary called daily_sales with these exact entries: 'Monday': 150, 'Tuesday': 200, 'Wednesday': 170, 'Thursday': 220, 'Friday': 180, 'Saturday': 90, 'Sunday': 130.

Matplotlib

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

Need a hint?

Use curly braces to create a dictionary with the given day names as keys and sales numbers as values.

2

Find highest and lowest sales values

Create two variables: max_sales to store the highest sales value and min_sales to store the lowest sales value from the daily_sales dictionary.

Matplotlib

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 170, 'Thursday': 220, 'Friday': 180, 'Saturday': 90, 'Sunday': 130}
# Find the maximum and minimum sales values
# Your code here

Need a hint?

Use the max() and min() functions on the dictionary values.

3

Plot sales data and annotate points

Import matplotlib.pyplot as plt. Create a line plot of the sales data using plt.plot() with days on the x-axis and sales on the y-axis. Then, annotate the highest sales point with the label 'Highest' and the lowest sales point with the label 'Lowest' using plt.annotate(). Use the exact variables max_sales and min_sales to find the points to annotate.

Matplotlib

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 170, 'Thursday': 220, 'Friday': 180, 'Saturday': 90, 'Sunday': 130}
max_sales = max(daily_sales.values())
min_sales = min(daily_sales.values())

# Import matplotlib.pyplot as plt
# Plot the sales data
# Annotate the highest and lowest sales points
# Your code here

Need a hint?

Use plt.plot() with markers to show points. Use list comprehensions to find the days for max and min sales. Use plt.annotate() with xy and xytext to label points.

4

Display the plot

Use plt.show() to display the plot with the annotated highest and lowest sales points.

Matplotlib

import matplotlib.pyplot as plt

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 170, 'Thursday': 220, 'Friday': 180, 'Saturday': 90, 'Sunday': 130}
max_sales = max(daily_sales.values())
min_sales = min(daily_sales.values())

days = list(daily_sales.keys())
sales = list(daily_sales.values())

plt.plot(days, sales, marker='o')

max_day = [day for day, sale in daily_sales.items() if sale == max_sales][0]
min_day = [day for day, sale in daily_sales.items() if sale == min_sales][0]

plt.annotate('Highest', xy=(max_day, max_sales), xytext=(max_day, max_sales + 10), arrowprops=dict(facecolor='green', shrink=0.05))
plt.annotate('Lowest', xy=(min_day, min_sales), xytext=(min_day, min_sales - 20), arrowprops=dict(facecolor='red', shrink=0.05))
# Display the plot
# Your code here

Need a hint?

Call plt.show() to open the plot window and see your graph with annotations.