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

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

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

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

Hint

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

Practice

1. What is the main purpose of adding annotations to a matplotlib plot?

easy

A. To increase the size of the plot

B. To change the color of the plot lines

C. To add labels or notes to specific points on the plot

D. To remove grid lines from the plot

Solution

1. Step 1: Understand what annotations do

   Annotations are used to add extra information like labels or notes to specific points on a plot.

2. Step 2: Compare options with the purpose

   Only To add labels or notes to specific points on the plot describes adding labels or notes to points, which matches the purpose of annotations.

3. Final Answer:

   To add labels or notes to specific points on the plot -> Option C

4. Quick Check:

   Annotations = labels/notes on points [OK]

Hint: Annotations label points to explain data clearly [OK]

Common Mistakes:

• Confusing annotations with changing plot colors
• Thinking annotations resize the plot
• Assuming annotations remove grid lines

2. Which of the following is the correct syntax to add an annotation at point (2, 5) with text 'Peak' in matplotlib?

easy

A. plt.annotate('Peak', xy=(2, 5))

B. plt.annotation('Peak', (2, 5))

C. plt.annotate('Peak', point=(2, 5))

D. plt.annotate(text='Peak', position=(2, 5))

Solution

1. Step 1: Recall the correct function and parameters

   The correct function is plt.annotate with parameters: text first, then xy for the point coordinates.

2. Step 2: Check each option's syntax

   plt.annotate('Peak', xy=(2, 5)) uses plt.annotate('Peak', xy=(2, 5)) which is correct. Others use wrong function names or parameter names.

3. Final Answer:

   plt.annotate('Peak', xy=(2, 5)) -> Option A

4. Quick Check:

   Correct syntax uses xy= for point [OK]

Hint: Use plt.annotate(text, xy=(x,y)) to label points [OK]

Common Mistakes:

• Using plt.annotation instead of plt.annotate
• Wrong parameter names like point or position
• Swapping text and coordinate order

3. What will the following code display?

import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [2, 5, 1]
plt.plot(x, y)
plt.annotate('High', xy=(2, 5), xytext=(3, 6), arrowprops=dict(facecolor='black'))
plt.show()

medium

A. A plot with a point at (3,6) labeled 'High' with an arrow pointing from (2,5)

B. Syntax error due to wrong arrowprops usage

C. A plot with no labels or arrows

D. A plot with a point at (2,5) labeled 'High' with an arrow pointing from (3,6)

Solution

1. Step 1: Understand plt.annotate parameters

   xy=(2,5) is the point to annotate, xytext=(3,6) is where the text is placed, arrowprops adds an arrow from text to point.

2. Step 2: Interpret the plot output

   The label 'High' appears at (3,6) with an arrow pointing to (2,5) on the plot line.

3. Final Answer:

   A plot with a point at (2,5) labeled 'High' with an arrow pointing from (3,6) -> Option D

4. Quick Check:

   xy = point, xytext = label position [OK]

Hint: xy=point, xytext=label position with arrow [OK]

Common Mistakes:

• Confusing xy and xytext roles
• Thinking arrow points from point to text
• Assuming no arrow appears without extra code

4. Identify the error in this code snippet that tries to annotate point (4, 7):

plt.plot([1,2,3,4], [3,5,2,7])
plt.annotate('Max', point=(4,7))
plt.show()

medium

A. plt.show() must come before plt.annotate

B. The parameter 'point' is invalid; should use 'xy' instead

C. plt.plot needs labels before annotation

D. The text 'Max' should be a number

Solution

1. Step 1: Check plt.annotate parameters

   The correct parameter for the point to annotate is 'xy', not 'point'.

2. Step 2: Identify the error cause

   Using 'point' causes a TypeError because plt.annotate does not recognize it.

3. Final Answer:

   The parameter 'point' is invalid; should use 'xy' instead -> Option B

4. Quick Check:

   Use xy= for annotation point [OK]

Hint: Use xy=, not point=, to specify annotation location [OK]

Common Mistakes:

• Using wrong parameter names like point or position
• Placing plt.show() before annotation
• Thinking annotation text must be numeric

5. You want to highlight the maximum value point on a plot with an annotation that places the label 10 units above the point and draws an arrow. Which code correctly achieves this for data x=[1,2,3], y=[4,9,7]?

hard

A. plt.plot(x, y)\nmax_idx = y.index(max(y))\nplt.annotate('Max', xy=(x[max_idx], y[max_idx]), xytext=(x[max_idx], y[max_idx]+10), arrowprops=dict(arrowstyle='->'))\nplt.show()

B. plt.plot(x, y)\nmax_idx = y.index(max(y))\nplt.annotate('Max', xytext=(x[max_idx], y[max_idx]), xy=(x[max_idx], y[max_idx]+10), arrowprops=dict(arrowstyle='->'))\nplt.show()

C. plt.plot(x, y)\nmax_val = max(y)\nplt.annotate('Max', xy=(max_val, x[max_idx]), xytext=(max_val, x[max_idx]+10), arrowprops=dict(arrowstyle='->'))\nplt.show()

D. plt.plot(x, y)\nmax_idx = y.index(max(y))\nplt.annotate('Max', xy=(x[max_idx], y[max_idx]), xytext=(x[max_idx], y[max_idx]-10), arrowprops=dict(arrowstyle='->'))\nplt.show()

Solution

1. Step 1: Find the max value index correctly

   max_idx = y.index(max(y)) finds the index of the highest y value.

2. Step 2: Set annotation point and label position

   xy is the max point (x[max_idx], y[max_idx]). xytext places label 10 units above: y[max_idx]+10.

3. Step 3: Use arrowprops to draw arrow

   arrowprops=dict(arrowstyle='->') correctly adds an arrow from label to point.

4. Final Answer:

   Code in option A correctly annotates max point with label 10 units above and arrow -> Option A

5. Quick Check:

   xy=point, xytext=above point, arrowprops=arrow [OK]

Hint: Use xytext=(x, y+offset) to place label above point [OK]

Common Mistakes:

• Swapping xy and xytext parameters
• Using wrong coordinates for annotation
• Placing label below instead of above
• Mixing x and y values in annotation