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.