Box plot with plt.boxplot in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What does this box plot code output?

Look at this code that creates a box plot. What will the plot show about the data?

Matplotlib

import matplotlib.pyplot as plt

data = [7, 8, 5, 6, 9, 10, 12, 15, 7, 8]
plt.boxplot(data)
plt.show()

A A box plot showing median around 9, with whiskers from 6 to 15

B A box plot showing median around 10, with whiskers from 7 to 15

C A box plot showing median around 8, with whiskers from 5 to 12

D A box plot showing median around 7.5, with whiskers from 5 to 15

💡 Hint

Remember the median is the middle value when data is sorted.

The median of the data is 8. The whiskers extend to the minimum and maximum values within 1.5 times the interquartile range, which are 5 and 12 here.

❓ data_output

intermediate

How many outliers are shown by this box plot?

This code creates a box plot. How many points will be shown as outliers?

Matplotlib

import matplotlib.pyplot as plt

data = [10, 12, 14, 15, 16, 18, 20, 100, 105]
plt.boxplot(data)
plt.show()

A 2

B 0

C 1

D 3

💡 Hint

Outliers are points outside 1.5 times the interquartile range from the quartiles.

The values 100 and 105 are far from the rest and will be marked as outliers, so 2 points.

❓ visualization

advanced

Which option produces a box plot with horizontal orientation?

You want to create a horizontal box plot using plt.boxplot. Which code does this correctly?

A plt.boxplot(data, vert=False)

B plt.boxplot(data, orientation='horizontal')

C plt.boxplot(data, vert=True)

D plt.boxplot(data, horizontal=True)

💡 Hint

Check the vert parameter in plt.boxplot.

The correct parameter to set horizontal orientation is vert=False. orientation='horizontal' is not a valid parameter.

🧠 Conceptual

advanced

What does the box in a box plot represent?

In a box plot created by plt.boxplot, what does the box itself show?

A The range between minimum and maximum values

B The median and outliers

C The mean and standard deviation

D The interquartile range (from Q1 to Q3)

💡 Hint

Think about the middle 50% of the data.

The box shows the interquartile range, which is the range between the first quartile (Q1) and third quartile (Q3).

🔧 Debug

expert

Why does this box plot code raise an error?

This code tries to create a box plot but raises an error. What is the cause?

Matplotlib

import matplotlib.pyplot as plt

data = [1, 2, 3, 4, 5]
plt.boxplot(data, orientation='sideways')
plt.show()

A plt.boxplot does not accept keyword arguments

B orientation='sideways' is not a valid parameter value

C plt.boxplot requires data to be a pandas DataFrame

D data must be a numpy array, not a list

💡 Hint

Check the parameters accepted by plt.boxplot.

plt.boxplot does not accept an 'orientation' parameter (use 'vert' instead). Passing 'orientation' causes an error.

Practice

1. What does the plt.boxplot() function primarily show about a dataset?

easy

A. The exact values of all data points

B. The spread and center of the data using quartiles

C. Only the average value of the data

D. The correlation between two variables

Solution

1. Step 1: Understand box plot purpose

   A box plot summarizes data distribution by showing quartiles and spread.

2. Step 2: Identify what plt.boxplot() displays

   This function creates a box plot that visualizes median, quartiles, and outliers.

3. Final Answer:

   The spread and center of the data using quartiles -> Option B

4. Quick Check:

   Box plot = Data spread and center [OK]

Hint: Box plots show spread and center, not individual points [OK]

Common Mistakes:

• Thinking boxplot shows all data points exactly
• Confusing boxplot with scatter plot
• Assuming boxplot shows correlation

2. Which of the following is the correct way to create a vertical box plot of data stored in a list named data using matplotlib?

easy

A. plt.boxplot(data, orientation='horizontal')

B. plt.boxplot(data, vertical=False)

C. plt.boxplot(data, vert=True)

D. plt.boxplot(data, vert='no')

Solution

1. Step 1: Check parameter for vertical boxplot

   The parameter to control vertical or horizontal is vert, which accepts True or False.

2. Step 2: Match correct syntax

   Using plt.boxplot(data, vert=True) creates a vertical box plot correctly.

3. Final Answer:

   plt.boxplot(data, vert=True) -> Option C

4. Quick Check:

   Use vert=True for vertical boxplot [OK]

Hint: Use vert=True for vertical boxplot in plt.boxplot [OK]

Common Mistakes:

• Using wrong parameter name like 'vertical'
• Passing string instead of boolean for vert
• Confusing orientation parameter which plt.boxplot doesn't use

3. What will be the output of the following code?

import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
data = np.random.normal(0, 1, 100)
plt.boxplot(data, notch=True)
plt.show()

medium

A. An error because notch=True is invalid

B. A scatter plot of the 100 data points

C. A histogram of the data distribution

D. A box plot with a notch showing median confidence interval

Solution

1. Step 1: Understand the code

   The code generates 100 random normal values and plots a box plot with notch=True.

2. Step 2: Interpret notch=True effect

   The notch creates a narrow region around the median to indicate confidence interval visually.

3. Final Answer:

   A box plot with a notch showing median confidence interval -> Option D

4. Quick Check:

   notch=True adds median confidence notch [OK]

Hint: notch=True adds median confidence notch in boxplot [OK]

Common Mistakes:

• Thinking notch=True creates scatter or histogram
• Assuming notch=True causes error
• Confusing boxplot with other plot types

4. Identify the error in this code snippet that tries to plot a box plot:

import matplotlib.pyplot as plt
data = [1, 2, 3, 4, 5]
plt.boxplot(data, notch='yes')
plt.show()

medium

A. The 'notch' parameter should be a boolean, not a string

B. The data list must be a numpy array

C. plt.boxplot requires data to be a dictionary

D. plt.show() is missing

Solution

1. Step 1: Check the 'notch' parameter type

   The 'notch' parameter expects a boolean (True or False), not a string like 'yes'.

2. Step 2: Confirm data type and plt.show()

   Data as list is valid, and plt.show() is present, so no error there.

3. Final Answer:

   The 'notch' parameter should be a boolean, not a string -> Option A

4. Quick Check:

   notch must be True or False, not 'yes' [OK]

Hint: Use True/False for notch, not strings [OK]

Common Mistakes:

• Passing string instead of boolean for notch
• Thinking data must be numpy array
• Forgetting plt.show() (not error here though)

5. You have two datasets: data1 and data2. You want to compare their distributions side by side using box plots with different colors and horizontal orientation. Which code snippet correctly achieves this?

hard

A. plt.boxplot([data1, data2], vert=False, patch_artist=True, boxprops=[dict(facecolor='lightblue'), dict(facecolor='orange')])

B. plt.boxplot(data1, data2, vert=True, color=['red', 'green'])

C. plt.boxplot([data1, data2], orientation='horizontal', colors=['blue', 'orange'])

D. plt.boxplot([data1, data2], vert=True, patch_artist=True, boxprops=dict(facecolor='red'))

Solution

1. Step 1: Check data input format

   To plot multiple datasets side by side, pass a list of datasets: [data1, data2].

2. Step 2: Verify horizontal orientation and color options

   Use vert=False for horizontal boxplots, patch_artist=True to fill boxes with color, and boxprops to set facecolor.

3. Step 3: Evaluate options

   plt.boxplot([data1, data2], vert=False, patch_artist=True, boxprops=[dict(facecolor='lightblue'), dict(facecolor='orange')]) correctly uses these parameters. Options A, B, and D use invalid parameters or syntax. plt.boxplot([data1, data2], vert=True, patch_artist=True, boxprops=dict(facecolor='red')) is vertical, not horizontal.

4. Final Answer:

   plt.boxplot([data1, data2], vert=False, patch_artist=True, boxprops=[dict(facecolor='lightblue'), dict(facecolor='orange')]) -> Option A

5. Quick Check:

   Multiple data + vert=False + patch_artist for color [OK]

Hint: Use vert=False and patch_artist=True for colored horizontal boxplots [OK]

Common Mistakes:

• Passing datasets separately instead of as list
• Using wrong parameter names like 'orientation'
• Forgetting patch_artist=True to fill colors