⏱ Quick start
scatterplot() function of
seaborn also allows to build bubble charts. Indeed, it has a
size parameter that controls circle size according to a numeric variable of the dataset.🔥
# libraries import matplotlib.pyplot as plt import seaborn as sns from gapminder import gapminder # data set # data data = gapminder.loc[gapminder.year == 2007] # use the scatterplot function to build the bubble map sns.scatterplot(data=data, x="gdpPercap", y="lifeExp", size="pop", legend=False, sizes=(20, 2000)) # show the graph plt.show()
Bubble plot with
Seaborn is the best tool to quickly build a quality bubble chart. The example below are based on the famous
gapminder dataset that shows the relationship between gdp per capita, life expectancy and population of world countries.
The examples below start simple by calling the
scatterplot() function with the minimum set of parameters. It then show how to change bubble colors to represent a fourth variable, improve general styling, tweak the legend and more.
Bubble plot with
As for scatterplots,
Matplotlib will help us build a bubble plot thanks to the the
plt.scatter() function. This function provides a
s parameter allowing to pass a third variable that will be mapped to the markers size.
Note that it is a common practice to map a fourth variable to the markers colors thanks to the
c parameter. This way, you're now looking a 4 variables in the same time, on the same chart 🎉.
A very common task when it comes to bubble chart is to add a proper legend to explain what colors and sizes mean. The blogpost below is a deep-dive into matplotlib legend and should be of great help for this
From the web
The web is full of astonishing charts made by awesome bloggers, (often using R). The Python graph gallery tries to display (or translate from R) some of the best creations and explain how their source code works. If you want to display your work here, please drop me a word or even better, submit a Pull Request!