Conteúdo do Curso
Ultimate Visualization with Python
1. Matplotlib Introduction
2. Creating Commonly Used Plots
5. Plotting with Seaborn
Ultimate Visualization with Python
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):
Code Description
plt.bar(countries, primary_sector)
Plotting the lower bars for
primary_sector
(blue bars).
plt.bar(countries, secondary_sector, bottom=primary_sector)
Plotting the middle bars for
secondary_sector
(orange bars) on top of the lower bars for primary_sector
.
plt.bar(countries, tertiary_sector, bottom=primary_sector + secondary_sector)
Plotting the upper bars for
tertiary_sector
(green bars) on top of the middle bars.
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.
Tarefa
- 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 foryes_answers
with specifying the correct keyword argument.
Tudo estava claro?
Conteúdo do Curso
Ultimate Visualization with Python
1. Matplotlib Introduction
2. Creating Commonly Used Plots
5. Plotting with Seaborn
Ultimate Visualization with Python
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):
Code Description
plt.bar(countries, primary_sector)
Plotting the lower bars for
primary_sector
(blue bars).
plt.bar(countries, secondary_sector, bottom=primary_sector)
Plotting the middle bars for
secondary_sector
(orange bars) on top of the lower bars for primary_sector
.
plt.bar(countries, tertiary_sector, bottom=primary_sector + secondary_sector)
Plotting the upper bars for
tertiary_sector
(green bars) on top of the middle bars.
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.
Tarefa
- 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 foryes_answers
with specifying the correct keyword argument.
Tudo estava claro?