Box plot with plt.boxplot in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a box plot of the data list using matplotlib.

Matplotlib

import matplotlib.pyplot as plt

data = [7, 8, 5, 6, 9, 10, 4]
plt.[1](data)
plt.show()

Drag options to blanks, or click blank then click option'

Ascatter

Bhist

Cboxplot

Dplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using plt.plot() instead of plt.boxplot()

Using plt.hist() which creates a histogram, not a box plot

The plt.boxplot() function creates a box plot for the given data.

2fill in blank

medium

Complete the code to add a title 'My Box Plot' to the plot.

Matplotlib

import matplotlib.pyplot as plt

data = [3, 5, 7, 9, 11]
plt.boxplot(data)
plt.[1]('My Box Plot')
plt.show()

Drag options to blanks, or click blank then click option'

Atitle

Bylabel

Cxlabel

Dlegend

Attempts:

3 left

💡 Hint

Common Mistakes

Using plt.xlabel() or plt.ylabel() which label axes, not the title

Using plt.legend() which adds a legend, not a title

The plt.title() function adds a title to the plot.

3fill in blank

hard

Fix the error in the code to correctly display the box plot for multiple data sets.

Matplotlib

import matplotlib.pyplot as plt

data1 = [1, 2, 3, 4, 5]
data2 = [2, 3, 4, 5, 6]
plt.boxplot([1])
plt.show()

Drag options to blanks, or click blank then click option'

Adata1

B[data1, data2]

Cdata1 + data2

Ddata1, data2

Attempts:

3 left

💡 Hint

Common Mistakes

Passing data1 and data2 separated by comma without brackets

Adding the two lists which merges data instead of separate boxes

To plot multiple data sets in one box plot, pass a list of lists: [data1, data2].

4fill in blank

hard

Fill both blanks to create a box plot with custom labels for two data sets.

Matplotlib

import matplotlib.pyplot as plt

data1 = [10, 20, 30]
data2 = [15, 25, 35]
plt.boxplot([data1, data2], labels=[1])
plt.[2]('Custom Box Plot')
plt.show()

Drag options to blanks, or click blank then click option'

A['Set A', 'Set B']

B['Data1', 'Data2']

Ctitle

Dxlabel

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect label format like a string instead of list

Using plt.xlabel() instead of plt.title() for the plot title

The labels parameter sets the box labels. Use plt.title() to add a title.

5fill in blank

hard

Fill all three blanks to create a box plot with notch style, custom labels, and a y-axis label.

Matplotlib

import matplotlib.pyplot as plt

data1 = [5, 7, 9]
data2 = [6, 8, 10]
plt.boxplot([data1, data2], [1]=True, labels=[2])
plt.[3]('Values')
plt.show()

Drag options to blanks, or click blank then click option'

Anotch

B['Group 1', 'Group 2']

Cylabel

Dxlabel

Attempts:

3 left

💡 Hint

Common Mistakes

Using plt.xlabel() instead of plt.ylabel() for y-axis label

Not passing labels as a list

Forgetting to set notch=True for notch style

Use notch=True for notch style, labels for box labels, and plt.ylabel() to label the y-axis.

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