Highlight and annotate pattern in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of highlighted region with annotation

What will be the output of this code snippet that highlights a region and adds an annotation on a matplotlib plot?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.axvspan(2, 4, color='yellow', alpha=0.3)
plt.annotate('Peak region', xy=(3, np.sin(3)), xytext=(5, 0.5), arrowprops=dict(facecolor='black', shrink=0.05))
plt.show()

A A sine wave plot with no shaded region and an annotation text 'Peak region' at (3, sin(3)) without arrow.

B A sine wave plot with a red shaded horizontal band between y=2 and y=4 and an arrow pointing from (3, sin(3)) to (5, 0.5) labeled 'Peak region'.

C A sine wave plot with a yellow shaded vertical band between x=3 and x=5 and an annotation text 'Peak region' at (2, 0.5) without arrow.

D A sine wave plot with a yellow shaded vertical band between x=2 and x=4 and an arrow pointing from (5, 0.5) to (3, sin(3)) labeled 'Peak region'.

💡 Hint

Look at the parameters of axvspan and annotate carefully, especially the coordinates and colors.

The code plots a sine wave, highlights the vertical region between x=2 and x=4 with a yellow transparent band, and adds an annotation labeled 'Peak region' with an arrow pointing from the text at (5, 0.5) to the point (3, sin(3)).

❓ data_output

intermediate

Number of annotations and highlighted regions

Given the following code, how many highlighted regions and annotations will appear on the plot?

Matplotlib

import matplotlib.pyplot as plt
x = range(10)
y = [i**2 for i in x]
plt.plot(x, y)
plt.axvspan(2, 4, color='green', alpha=0.2)
plt.axvspan(6, 8, color='red', alpha=0.2)
plt.annotate('Low range', xy=(3, 9), xytext=(1, 20), arrowprops=dict(facecolor='black'))
plt.annotate('High range', xy=(7, 49), xytext=(8, 80), arrowprops=dict(facecolor='black'))
plt.show()

A 2 highlighted vertical regions and 2 annotations with arrows.

B 1 highlighted vertical region and 2 annotations with arrows.

C 2 highlighted vertical regions and 1 annotation with arrow.

D No highlighted regions and 2 annotations without arrows.

💡 Hint

Count the number of axvspan and annotate calls with arrowprops.

The code creates two vertical highlighted regions using axvspan and two annotations each with arrows pointing to specified points.

🔧 Debug

advanced

Identify the error in annotation coordinates

What error will this code produce when run, and why?

Matplotlib

import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [4, 5, 6]
plt.plot(x, y)
plt.annotate('Point', xy=(4, 5), xytext=(2, 6), arrowprops=dict(facecolor='blue'))
plt.show()

A ValueError because xy coordinate (4,5) is outside the plot data range.

B RuntimeWarning about annotation coordinates being outside the axes limits.

C No error; the plot shows with annotation arrow pointing from (2,6) to (4,5).

D SyntaxError due to incorrect annotate parameters.

💡 Hint

Check if matplotlib allows annotations outside the data range.

Matplotlib allows annotations outside the data range; no error occurs. The arrow points from xytext to xy as specified.

❓ visualization

advanced

Visual effect of alpha in highlighted region

What visual difference will changing alpha from 0.1 to 0.8 in axvspan produce on the plot?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 5, 100)
y = np.cos(x)
plt.plot(x, y)
plt.axvspan(1, 3, color='blue', alpha=0.1)
plt.show()

A The blue shaded region color will change to green with alpha=0.8.

B The blue shaded region will become much more opaque and darker with alpha=0.8 compared to alpha=0.1.

C The blue shaded region will become lighter and more transparent with alpha=0.8 compared to alpha=0.1.

D No visible change occurs when changing alpha from 0.1 to 0.8.

💡 Hint

Alpha controls transparency; higher alpha means less transparent.

Increasing alpha from 0.1 to 0.8 makes the shaded region more solid and less transparent, so it appears darker and more visible.

🚀 Application

expert

Highlight and annotate multiple peaks in data

Given a noisy sine wave, which code snippet correctly highlights the peaks between x=1 to 2 and x=4 to 5 with different colors and annotates them with arrows?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 6, 300)
y = np.sin(x) + 0.1 * np.random.randn(300)
plt.plot(x, y)
# Highlight and annotate peaks here
plt.show()

A

plt.axvspan(1, 2, color='blue', alpha=0.5)
plt.axvspan(4, 5, color='yellow', alpha=0.5)
plt.annotate('Peak 1', xy=(1.5, y[75]), xytext=(0.5, 1), arrowprops=dict(facecolor='blue'))
plt.annotate('Peak 2', xy=(4.5, y[224]), xytext=(5.5, 1), arrowprops=dict(facecolor='yellow'))

B

plt.axvspan(1, 2, color='red', alpha=0.3)
plt.axvspan(4, 5, color='green', alpha=0.3)
plt.annotate('Peak 1', xy=(1.5, np.sin(1.5)), xytext=(0.5, 1), arrowprops=dict(facecolor='red'))
plt.annotate('Peak 2', xy=(4.5, np.sin(4.5)), xytext=(5.5, 1), arrowprops=dict(facecolor='green'))

C

plt.axhspan(1, 2, color='red', alpha=0.3)
plt.axhspan(4, 5, color='green', alpha=0.3)
plt.annotate('Peak 1', xy=(1.5, np.sin(1.5)), xytext=(0.5, 1), arrowprops=dict(facecolor='red'))
plt.annotate('Peak 2', xy=(4.5, np.sin(4.5)), xytext=(5.5, 1), arrowprops=dict(facecolor='green'))

D

plt.axvspan(1, 2, color='red', alpha=0.3)
plt.axvspan(4, 5, color='green', alpha=0.3)
plt.annotate('Peak 1', xy=(1.5, y[100]), xytext=(0.5, 1), arrowprops=dict(facecolor='blue'))
plt.annotate('Peak 2', xy=(4.5, y[200]), xytext=(5.5, 1), arrowprops=dict(facecolor='green'))

💡 Hint

Use the noisy y values for annotation coordinates to match the plotted data points.

Option A correctly uses axvspan to highlight vertical regions, uses the noisy y values at the correct indices for annotation points, and matches arrow colors to the highlight colors.

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