Box plot with plt.boxplot in Matplotlib - Mini Project: Build & Apply

Box plot with plt.boxplot

📖 Scenario: You are a data analyst working with a small dataset of daily temperatures recorded over a week. You want to visualize the spread and identify any outliers in the temperature data using a box plot.

🎯 Goal: Create a box plot using plt.boxplot to visualize the temperature data.

📋 What You'll Learn

Create a list variable named temperatures with the exact values: 22, 21, 23, 24, 30, 20, 19

Create a variable named fig and ax by calling plt.subplots()

Use ax.boxplot to create a box plot of the temperatures list

Set the title of the plot to exactly 'Daily Temperatures Box Plot'

Print the box plot figure object fig to confirm creation

💡 Why This Matters

🌍 Real World

Box plots are used in data science to quickly see the distribution, spread, and outliers of data like temperatures, sales, or test scores.

💼 Career

Data analysts and scientists use box plots to summarize data visually and communicate insights clearly to teams and stakeholders.

1

Create the temperature data list

Create a list called temperatures with these exact values: 22, 21, 23, 24, 30, 20, 19.

Matplotlib

# Create the list temperatures with the given values
# Your code here

Hint

Use square brackets [] to create a list and separate values with commas.

2

Set up the plot figure and axes

Import matplotlib.pyplot as plt. Then create variables fig and ax by calling plt.subplots().

Matplotlib

temperatures = [22, 21, 23, 24, 30, 20, 19]
# Import matplotlib.pyplot as plt and create fig, ax using plt.subplots()
# Your code here

Hint

Use import matplotlib.pyplot as plt to import the plotting library.

Use fig, ax = plt.subplots() to create the figure and axes.

3

Create the box plot with plt.boxplot

Use ax.boxplot to create a box plot of the temperatures list. Then set the plot title to exactly 'Daily Temperatures Box Plot' using ax.set_title.

Matplotlib

import matplotlib.pyplot as plt

temperatures = [22, 21, 23, 24, 30, 20, 19]

fig, ax = plt.subplots()
# Create box plot of temperatures using ax.boxplot
# Set the title to 'Daily Temperatures Box Plot'
# Your code here

Hint

Call ax.boxplot(temperatures) to draw the box plot.

Use ax.set_title('Daily Temperatures Box Plot') to add the title.

4

Display the box plot figure

Print the figure object fig to confirm the box plot was created.

Matplotlib

import matplotlib.pyplot as plt

temperatures = [22, 21, 23, 24, 30, 20, 19]

fig, ax = plt.subplots()
ax.boxplot(temperatures)
ax.set_title('Daily Temperatures Box Plot')
# Print the figure object fig
# Your code here

Hint

Use print(fig) to display the figure object.

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