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Ultimate Visualization with Python
course content

Course Content

Ultimate Visualization with Python

Ultimate Visualization with Python

1. Matplotlib Introduction
Plotting ApplicationsWhy Use Matplotlib?Creating a CanvasBasic Plotting
2. Creating Commonly Used Plots
Line PlotMultiple Line PlotsScatter PlotBar ChartStacked Bar ChartsGrouped Bar Charts
3. Plots Customization
Adding Title to the PlotAdding LegendAxes CustomizationColors and Transparency Customizing GridSubplots
4. More Statistical Plots
HistogramBox PlotPie Chart
5. Plotting with Seaborn
Seaborn IntroductionCountplotPlots DecorationKDE PlotJoint PlotPair PlotHeatmap

bookStacked 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):


              
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()
copy

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

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

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Section 2. Chapter 5
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bookStacked 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):


              
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()
copy

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

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

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 5
toggle bottom row

bookStacked 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):


              
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()
copy

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

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

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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):


              
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()
copy

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

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

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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