Highlighting date ranges in Matplotlib - Mini Project: Build & Apply

Highlighting Date Ranges

📖 Scenario: You work as a data analyst for a company that tracks daily sales over a month. You want to visually highlight a specific date range on the sales chart to show a promotional period.

🎯 Goal: Create a line chart of daily sales and highlight the sales during the promotional date range using a shaded area.

📋 What You'll Learn

Create a dictionary called sales_data with dates as keys (strings in 'YYYY-MM-DD' format) and sales numbers as values.

Create a variable called promo_start with the start date of the promotion as a string.

Create a variable called promo_end with the end date of the promotion as a string.

Use matplotlib to plot the sales data as a line chart.

Highlight the promotional date range on the chart using ax.axvspan().

💡 Why This Matters

🌍 Real World

Highlighting date ranges on charts helps businesses see important periods like promotions, holidays, or events clearly in their data.

💼 Career

Data analysts and scientists often need to visualize time series data and emphasize specific periods to communicate insights effectively.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: '2024-06-01': 150, '2024-06-02': 200, '2024-06-03': 180, '2024-06-04': 220, '2024-06-05': 210.

Matplotlib

# Create the sales_data dictionary with the given dates and sales
# Your code here

Need a hint?

Use curly braces {} to create a dictionary. Each entry has a date string as key and an integer as value.

2

Set the promotional date range

Create two variables: promo_start set to '2024-06-02' and promo_end set to '2024-06-04'.

Matplotlib

sales_data = {
    '2024-06-01': 150,
    '2024-06-02': 200,
    '2024-06-03': 180,
    '2024-06-04': 220,
    '2024-06-05': 210
}
# Create promo_start and promo_end variables with the given dates
# Your code here

Need a hint?

Assign the date strings directly to the variables.

3

Plot the sales data and highlight the promo range

Import matplotlib.pyplot as plt. Convert the keys of sales_data to datetime objects using pd.to_datetime from pandas. Plot the sales data as a line chart with dates on the x-axis and sales on the y-axis. Use ax.axvspan() to highlight the area between promo_start and promo_end with a light blue color and 30% transparency.

Matplotlib

sales_data = {
    '2024-06-01': 150,
    '2024-06-02': 200,
    '2024-06-03': 180,
    '2024-06-04': 220,
    '2024-06-05': 210
}
promo_start = '2024-06-02'
promo_end = '2024-06-04'

# Import matplotlib.pyplot as plt and pandas as pd
# Convert sales_data keys to datetime
# Plot the sales line chart
# Highlight the promo range with ax.axvspan()
# Your code here

Need a hint?

Use pd.to_datetime() to convert date strings. Use ax.plot() to draw the line. Use ax.axvspan() to shade the date range.

4

Display the plot

Add a line to display the plot using plt.show().

Matplotlib

import matplotlib.pyplot as plt
import pandas as pd

sales_data = {
    '2024-06-01': 150,
    '2024-06-02': 200,
    '2024-06-03': 180,
    '2024-06-04': 220,
    '2024-06-05': 210
}
promo_start = '2024-06-02'
promo_end = '2024-06-04'

# Convert date strings to datetime objects
dates = pd.to_datetime(list(sales_data.keys()))
sales = list(sales_data.values())

fig, ax = plt.subplots()
ax.plot(dates, sales, marker='o')

# Highlight the promotional date range
ax.axvspan(pd.to_datetime(promo_start), pd.to_datetime(promo_end), color='lightblue', alpha=0.3)

# Show the plot
# Your code here

Need a hint?

Use plt.show() to display the plot window.