Learn Stacked Bar Charts | Creating Commonly Used Plots

Stacked bar charts allow comparison of multiple categories within each x-axis group. For example, rather than showing only the total GDP of each country, they can illustrate the contribution of individual economic sectors to the total.


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import matplotlib.pyplot as plt
import numpy as np
countries = ['USA', 'China', 'Japan']
primary_sector = np.array([1.4, 4.8, 0.4])
secondary_sector = np.array([11.3, 6.2, 0.8])
tertiary_sector = np.array([14.2, 8.4, 3.2])
# Calling the bar() function multiple times for each category (sector)
plt.bar(countries, primary_sector)
plt.bar(countries, secondary_sector, bottom=primary_sector)
plt.bar(countries, tertiary_sector, bottom=primary_sector + secondary_sector)
plt.show()

To create stacked bars, the bar() function is called multiple times—once for each sector. In each call, the same countries list is used for the x-axis, and the bottom parameter ensures that each new segment is stacked on top of the previous one.

Study More

The bottom parameter specifies the y coordinate(s) of the bottom side(s) of the bars. Here is the bar() documentation.

Task

Swipe to start coding

Use the correct function for creating bar charts.
Plot the lower bars for yes_answers.
Plot the bars for no_answers on top of the bars for yes_answers with specifying the correct keyword argument.

Solution

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 5

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              1234567891011
            
import matplotlib.pyplot as plt
import numpy as np
countries = ['USA', 'China', 'Japan']
primary_sector = np.array([1.4, 4.8, 0.4])
secondary_sector = np.array([11.3, 6.2, 0.8])
tertiary_sector = np.array([14.2, 8.4, 3.2])
# Calling the bar() function multiple times for each category (sector)
plt.bar(countries, primary_sector)
plt.bar(countries, secondary_sector, bottom=primary_sector)
plt.bar(countries, tertiary_sector, bottom=primary_sector + secondary_sector)
plt.show()

Study More

The bottom parameter specifies the y coordinate(s) of the bottom side(s) of the bars. Here is the bar() documentation.

Task

Swipe to start coding

Use the correct function for creating bar charts.
Plot the lower bars for yes_answers.
Plot the bars for no_answers on top of the bars for yes_answers with specifying the correct keyword argument.

Solution

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 5

single

Swipe to show menu


              1234567891011
            
import matplotlib.pyplot as plt
import numpy as np
countries = ['USA', 'China', 'Japan']
primary_sector = np.array([1.4, 4.8, 0.4])
secondary_sector = np.array([11.3, 6.2, 0.8])
tertiary_sector = np.array([14.2, 8.4, 3.2])
# Calling the bar() function multiple times for each category (sector)
plt.bar(countries, primary_sector)
plt.bar(countries, secondary_sector, bottom=primary_sector)
plt.bar(countries, tertiary_sector, bottom=primary_sector + secondary_sector)
plt.show()

Study More

The bottom parameter specifies the y coordinate(s) of the bottom side(s) of the bars. Here is the bar() documentation.

Task

Swipe to start coding

Use the correct function for creating bar charts.
Plot the lower bars for yes_answers.
Plot the bars for no_answers on top of the bars for yes_answers with specifying the correct keyword argument.

Solution

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Stacked Bar Charts

Solution

Awesome!

Stacked Bar Charts

Solution

Awesome!

Stacked Bar Charts

Solution