Stacked Bar Charts

Stacked bar charts are useful when we want to compare several categories (two or more) for each value on the x-axis. For example, instead of only looking at the GDP of different countries we may want to look at the amount of contribution of each economic sector to the GDP of a particular country (the data is not real):


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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()

Similarly to line plots and scatter plots, we called the bar() function three times to create three bars for each value on the x-axis (country names in our example). In every call countries are specified as x-axis values in order to create stacked bars. Pay extra attention to the bottom parameter.

Note

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

Task

Swipe to show code editor

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.

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

Stacked Bar Charts


              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()

Note

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

Task

Swipe to show code editor

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.

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

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