Highlight and annotate pattern in Matplotlib - Mini Project: Build & Apply

Highlight and Annotate Pattern

📖 Scenario: You are analyzing sales data for a small store. You want to see the sales trend over a week and highlight the day with the highest sales. This will help you understand when the store performs best.

🎯 Goal: Create a line chart of daily sales. Highlight the day with the highest sales using a different color and add an annotation to show the exact sales value on that day.

📋 What You'll Learn

Create a list called days with the days of the week.

Create a list called sales with the sales numbers for each day.

Find the day with the highest sales and store its index in max_index.

Plot the sales data as a line chart using matplotlib.

Highlight the highest sales day with a red dot.

Add an annotation showing the sales value on the highest sales day.

💡 Why This Matters

🌍 Real World

Highlighting important points in data charts helps businesses quickly see key information like best sales days.

💼 Career

Data analysts and scientists often highlight and annotate charts to communicate insights clearly to teams and managers.

1

Create sales data

Create a list called days with these exact values: 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'. Create another list called sales with these exact values: 150, 200, 170, 220, 180, 300, 250.

Matplotlib

# Create the days list and sales list
# Your code here

Hint

Use square brackets to create lists. Put the days as strings in days and numbers in sales.

2

Find the highest sales day index

Create a variable called max_index and set it to the index of the highest value in the sales list using the sales.index() method and the max() function.

Matplotlib

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
sales = [150, 200, 170, 220, 180, 300, 250]
# Find the index of the highest sales day
# Your code here

Hint

Use max(sales) to get the highest sales number, then find its position with sales.index().

3

Plot sales and highlight highest day

Import matplotlib.pyplot as plt. Plot the sales data as a line chart using plt.plot(days, sales). Highlight the highest sales day by plotting a red dot at days[max_index] and sales[max_index] using plt.scatter() with color 'red' and size 100. Add an annotation with plt.annotate() showing the sales value at the highest sales day. Use the text f'Highest: {sales[max_index]}', position the text slightly above the red dot with xytext=(0,10) and textcoords='offset points'.

Matplotlib

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
sales = [150, 200, 170, 220, 180, 300, 250]
max_index = sales.index(max(sales))

# Import matplotlib and plot the sales line chart
# Highlight the highest sales day with a red dot
# Add an annotation for the highest sales
# Your code here

Hint

Use plt.plot() for the line, plt.scatter() for the red dot, and plt.annotate() to add text above the dot.

4

Show the plot

Add plt.show() to display the plot with the highlighted highest sales day and annotation.

Matplotlib

import matplotlib.pyplot as plt

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
sales = [150, 200, 170, 220, 180, 300, 250]
max_index = sales.index(max(sales))

plt.plot(days, sales)
plt.scatter(days[max_index], sales[max_index], color='red', s=100)
plt.annotate(f'Highest: {sales[max_index]}', (days[max_index], sales[max_index]), xytext=(0,10), textcoords='offset points')
# Show the plot
# Your code here

Hint

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

Practice

1. What is the main purpose of using highlight and annotate in a matplotlib plot?

easy

A. To save the plot as an image file

B. To change the color scheme of the entire plot

C. To draw attention to important parts and add notes explaining data points

D. To remove grid lines from the plot

Solution

1. Step 1: Understand the role of highlight

   Highlighting is used to emphasize important areas on a graph to make them stand out.

2. Step 2: Understand the role of annotate

   Annotations add notes with arrows to explain or give more information about specific data points.

3. Final Answer:

   To draw attention to important parts and add notes explaining data points -> Option C

4. Quick Check:

   Highlight + annotate = emphasize + explain [OK]

Hint: Highlight = focus, annotate = explain with notes [OK]

Common Mistakes:

• Thinking highlight changes entire plot color
• Confusing annotate with saving files
• Assuming highlight removes grid lines

2. Which of the following is the correct syntax to add an annotation with an arrow pointing to point (2, 4) in matplotlib?

easy

A. plt.annotate('Note', xy=(2, 4), xytext=(3, 5), arrowprops=dict(facecolor='black'))

B. plt.annotate('Note', point=(2, 4), text=(3, 5), arrow=True)

C. plt.annotation('Note', xy=(2, 4), xytext=(3, 5), arrowprops=True)

D. plt.annotate('Note', xy=(2, 4), text=(3, 5), arrowprops=dict(color='red'))

Solution

1. Step 1: Check the function name and parameters

   The correct function is plt.annotate with parameters xy for point and xytext for text location.

2. Step 2: Verify arrow properties

   arrowprops must be a dictionary specifying arrow style, e.g., dict(facecolor='black').

3. Final Answer:

   plt.annotate('Note', xy=(2, 4), xytext=(3, 5), arrowprops=dict(facecolor='black')) -> Option A

4. Quick Check:

   Correct annotate syntax uses xy, xytext, arrowprops dict [OK]

Hint: Use xy and xytext with arrowprops dict for annotation [OK]

Common Mistakes:

• Using wrong parameter names like point or text
• Passing arrowprops as True instead of dict
• Using plt.annotation instead of plt.annotate

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
plt.plot(x, y)
plt.axvspan(2, 3, color='yellow', alpha=0.3)
plt.annotate('Peak', xy=(3, 25), xytext=(3.5, 27), arrowprops=dict(facecolor='blue'))
plt.show()

medium

A. A line plot with a red shaded area between x=2 and x=3 and no annotation

B. A line plot with a yellow shaded area between x=2 and x=3 and an annotation 'Peak' pointing at (3, 25)

C. A scatter plot with points highlighted in yellow and annotation at (3.5, 27)

D. A line plot with no shading and annotation text 'Peak' at (3, 25) without arrow

Solution

1. Step 1: Understand plt.axvspan usage

   plt.axvspan(2, 3, color='yellow', alpha=0.3) creates a translucent yellow vertical highlight between x=2 and x=3.

2. Step 2: Understand plt.annotate usage

   plt.annotate('Peak', xy=(3, 25), xytext=(3.5, 27), arrowprops=dict(facecolor='blue')) adds an annotation 'Peak' with an arrow pointing at (3, 25).

3. Final Answer:

   A line plot with a yellow shaded area between x=2 and x=3 and an annotation 'Peak' pointing at (3, 25) -> Option B

4. Quick Check:

   axvspan = highlight, annotate = note with arrow [OK]

Hint: axvspan highlights vertical area, annotate adds arrow note [OK]

Common Mistakes:

• Confusing axvspan color or range
• Thinking annotation text appears without arrow
• Mistaking line plot for scatter plot

4. Identify the error in this code that tries to highlight and annotate a point:

import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [5, 7, 9]
plt.plot(x, y)
plt.axhspan(6, 8, color='green')
plt.annotate('Important', xy=(2, 7), xytext=(2, 9), arrowprops='->')
plt.show()

medium

A. plt.axhspan should use x values, not y values

B. plt.plot should use scatter instead for annotation

C. xytext coordinates must be inside the plot range

D. arrowprops should be a dictionary, not a string

Solution

1. Step 1: Check plt.axhspan usage

   plt.axhspan(6, 8, color='green') is correct to highlight horizontal area between y=6 and y=8.

2. Step 2: Check plt.annotate arrowprops parameter

   arrowprops must be a dictionary describing arrow style, not a string like '->'.

3. Final Answer:

   arrowprops should be a dictionary, not a string -> Option D

4. Quick Check:

   arrowprops = dict(...) not string [OK]

Hint: arrowprops needs dict, not string like '->' [OK]

Common Mistakes:

• Passing arrowprops as string instead of dict
• Confusing axhspan with axvspan usage
• Thinking xytext must be inside plot limits

5. You want to highlight the time period between 10 and 15 seconds on a line plot and annotate the highest point in that range with a label 'Max'. Which code snippet correctly achieves this?

hard

A. plt.axvspan(10, 15, color='lightblue', alpha=0.4)
max_x = 12
max_y = 50
plt.annotate('Max', xy=(max_x, max_y), xytext=(max_x+1, max_y+5), arrowprops=dict(facecolor='red'))

B. plt.axhspan(10, 15, color='lightblue', alpha=0.4)
max_x = 12
max_y = 50
plt.annotate('Max', xy=(max_x, max_y), xytext=(max_x-1, max_y-5), arrowprops='->')

C. plt.axvspan(10, 15, color='lightblue')
max_x = 12
max_y = 50
plt.annotate('Max', xy=(max_x, max_y), xytext=(max_x, max_y), arrowprops=dict(color='green'))

D. plt.axvspan(10, 15, color='yellow', alpha=0.4)
max_x = 12
max_y = 50
plt.annotate('Max', xy=(max_x, max_y), xytext=(max_x+2, max_y+2))

Solution

1. Step 1: Highlight the time period on x-axis

   plt.axvspan(10, 15, color='lightblue', alpha=0.4) correctly highlights between 10 and 15 seconds with transparency.

2. Step 2: Annotate the highest point with arrow

   Using plt.annotate with arrowprops=dict(facecolor='red') adds a red arrow pointing to (12, 50) with text offset at (13, 55).

3. Final Answer:

   plt.axvspan(10, 15, color='lightblue', alpha=0.4) max_x = 12 max_y = 50 plt.annotate('Max', xy=(max_x, max_y), xytext=(max_x+1, max_y+5), arrowprops=dict(facecolor='red')) -> Option A

4. Quick Check:

   axvspan for x-range, annotate with arrowprops dict [OK]

Hint: Use axvspan for x-range highlight and dict arrowprops for arrow [OK]

Common Mistakes:

• Using axhspan instead of axvspan for time range
• Passing arrowprops as string instead of dict
• Not setting alpha for transparency in highlight