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

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

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

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

Hint

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

Practice

1. What does the plt.axvspan(start_date, end_date) function do in matplotlib?

easy

A. It draws a horizontal line at start_date.

B. It highlights a vertical span (date range) on the plot between start_date and end_date.

C. It creates a scatter plot of dates between start_date and end_date.

D. It sets the x-axis limits to start_date and end_date.

Solution

1. Step 1: Understand the function purpose

   plt.axvspan is used to highlight a vertical area on the plot, usually to mark a range on the x-axis.

2. Step 2: Match parameters to behavior

   The parameters start_date and end_date define the start and end of this vertical span on the x-axis, highlighting that date range.

3. Final Answer:

   It highlights a vertical span (date range) on the plot between start_date and end_date. -> Option B

4. Quick Check:

   Highlight date range = plt.axvspan(start_date, end_date) [OK]

Hint: Remember axvspan highlights vertical areas between two x-values [OK]

Common Mistakes:

• Confusing axvspan with line drawing functions
• Thinking it changes axis limits
• Assuming it plots points instead of highlighting

2. Which of the following is the correct syntax to highlight the date range from '2023-01-01' to '2023-01-10' with a semi-transparent red color in matplotlib?

easy

A. plt.axvspan(start='2023-01-01', end='2023-01-10', color='red', alpha=0.5)

B. plt.axvspan('2023-01-01', '2023-01-10', color='red', transparency=0.5)

C. plt.axvspan('2023-01-01', '2023-01-10', color='red', alpha=0.5)

D. plt.axvspan('2023-01-01', '2023-01-10', alpha=50, color='red')

Solution

1. Step 1: Check parameter names and order

   plt.axvspan takes start and end positions as first two arguments, then keyword arguments like color and alpha.

2. Step 2: Validate correct keyword for transparency

   alpha controls transparency and should be between 0 and 1. The value 0.5 means 50% transparent.

3. Final Answer:

   plt.axvspan('2023-01-01', '2023-01-10', color='red', alpha=0.5) -> Option C

4. Quick Check:

   Use alpha for transparency, not transparency keyword [OK]

Hint: Use alpha (0-1) for transparency, not transparency keyword [OK]

Common Mistakes:

• Using 'transparency' instead of 'alpha'
• Passing alpha as 50 instead of 0.5
• Using named parameters start= and end= which are invalid

3. Given the following code, what will be the color and transparency of the highlighted date range?

import matplotlib.pyplot as plt
import pandas as pd

dates = pd.date_range('2023-01-01', periods=5)
values = [10, 20, 15, 25, 30]
plt.plot(dates, values)
plt.axvspan(pd.Timestamp('2023-01-02'), pd.Timestamp('2023-01-04'), color='green', alpha=0.3)
plt.show()

medium

A. A green shaded area with 30% opacity between Jan 2 and Jan 4

B. A green shaded area with 70% opacity between Jan 2 and Jan 4

C. A fully opaque green shaded area between Jan 2 and Jan 4

D. No shaded area will appear because alpha is too low

Solution

1. Step 1: Understand alpha parameter meaning

   Alpha=0.3 means 30% opacity (or 70% transparent), so the color is faint but visible.

2. Step 2: Confirm color and date range

   The color is green, and the highlighted range is from Jan 2 to Jan 4 as specified.

3. Final Answer:

   A green shaded area with 30% opacity between Jan 2 and Jan 4 -> Option A

4. Quick Check:

   alpha=0.3 means 30% opacity, color='green' [OK]

Hint: Alpha 0.3 means 30% opacity, not 70% [OK]

Common Mistakes:

• Confusing alpha as transparency percentage
• Thinking alpha=0.3 means 70% opacity
• Assuming no highlight appears if alpha is low

4. Identify the error in the following code snippet that tries to highlight a date range:

import matplotlib.pyplot as plt
plt.axvspan('2023-01-01', '2023-01-05', color='blue', alpha=0.4)
plt.show()

medium

A. Dates are passed as strings; they should be converted to datetime objects.

B. The alpha value must be an integer, not a float.

C. The color parameter should be 'colour' instead of 'color'.

D. plt.show() is missing and must be added.

Solution

1. Step 1: Check parameter types for axvspan

   plt.axvspan expects numeric or datetime values for start and end. Passing strings directly causes errors.

2. Step 2: Correct usage for dates

   Convert strings to datetime objects using pd.Timestamp or datetime before passing.

3. Final Answer:

   Dates are passed as strings; they should be converted to datetime objects. -> Option A

4. Quick Check:

   axvspan needs datetime, not string dates [OK]

Hint: Convert date strings to datetime objects before axvspan [OK]

Common Mistakes:

• Passing date strings directly without conversion
• Misusing alpha as integer
• Wrong color parameter spelling

5. You have a time series plot of daily sales data from '2023-01-01' to '2023-01-31'. You want to highlight weekends (Saturdays and Sundays) with a light gray transparent background. Which approach correctly applies this using matplotlib?

hard

A. Use plt.fill_between with weekend dates and color='gray', alpha=0.2.

B. Use plt.axvspan once with start='2023-01-01' and end='2023-01-31' and color='gray', alpha=0.2.

C. Plot vertical lines on weekends using plt.axvline with color='gray' and alpha=0.2.

D. Loop through dates, check if weekday is Saturday or Sunday, then use plt.axvspan for each weekend with color='gray' and alpha=0.2.

Solution

1. Step 1: Identify weekend dates in the range

   Loop through each date, check if it is Saturday (weekday=5) or Sunday (weekday=6).

2. Step 2: Highlight each weekend separately

   Use plt.axvspan for each weekend day or range with desired color and transparency.

3. Step 3: Understand why other options fail

   Use plt.axvspan once with start='2023-01-01' and end='2023-01-31' and color='gray', alpha=0.2. highlights entire month, not weekends. Plot vertical lines on weekends using plt.axvline with color='gray' and alpha=0.2. draws lines, not spans. Use plt.fill_between with weekend dates and color='gray', alpha=0.2. is for y-axis fills, not x-axis spans.

4. Final Answer:

   Loop through dates, check if weekday is Saturday or Sunday, then use plt.axvspan for each weekend with color='gray' and alpha=0.2. -> Option D

5. Quick Check:

   Highlight weekends individually with axvspan [OK]

Hint: Highlight each weekend separately with axvspan in a loop [OK]

Common Mistakes:

• Highlighting entire date range instead of weekends
• Using axvline instead of axvspan for spans
• Trying to fill y-axis area instead of x-axis date range