Box plot with plt.boxplot in Matplotlib - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a box plot used for in data analysis?

A box plot shows the distribution of data by displaying the median, quartiles, and possible outliers. It helps us understand the spread and skewness of the data.

beginner

Which matplotlib function is used to create a box plot?

The function plt.boxplot() is used to create box plots in matplotlib.

beginner

What does the line inside the box in a box plot represent?

The line inside the box represents the median value of the data set.

intermediate

How does plt.boxplot show outliers in the data?

Outliers are shown as individual points outside the whiskers of the box plot.

intermediate

What are the 'whiskers' in a box plot?

Whiskers extend from the box to the smallest and largest values within 1.5 times the interquartile range from the quartiles. They show the range of most data points.

What does the box in a box plot represent?

A The interquartile range (middle 50% of data)

B The mean and standard deviation

C All data points

D Only outliers

The box shows the interquartile range, which covers the middle 50% of the data.

Which matplotlib function creates a box plot?

A plt.hist()

B plt.scatter()

C plt.plot()

D plt.boxplot()

plt.boxplot() is the function used to create box plots.

In a box plot, what do the whiskers represent?

A The range of data excluding outliers

B The outliers

C The mean value

D The median value

Whiskers extend to the smallest and largest data points within 1.5 times the interquartile range, excluding outliers.

How are outliers shown in a box plot?

A As whiskers

B As individual points outside whiskers

C As part of the box

D They are not shown

Outliers appear as individual points outside the whiskers.

What does the line inside the box of a box plot indicate?

A Mean

B Mode

C Median

D Maximum value

The line inside the box shows the median of the data.

Explain how to create a box plot using matplotlib's plt.boxplot and what key parts of the data it shows.

Think about the parts of the box plot and the function name.

Describe what information a box plot gives about a data set and why it is useful.

Consider what you learn about data from the box and whiskers.

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