Data Visualization with PlotNine
Ch. 1 Preface
Last update: Fri Nov 6 12:52:07 2020 -0600 (a19ad66)
plotnine is a data visualisation package for Python based on the grammar of graphics, created by Hassan Kibirige. Its API is similar to ggplot2, a widely successful R package by Hadley Wickham and others.1
I’m a staunch proponent of ggplot2. The underlying grammar of graphics is accompanied by a consistent API that allows you to quickly and iteratively create different types of beautiful data visualisations while rarely having to consult the documentation. A welcoming set of properties when doing exploratory data analysis.
I must admit that I haven’t tried every data visualisation package there is for Python, but when it comes to the most popular ones, I personally find them either convenient but limited (pandas), flexible but complicated (matplotlib), or beautiful but inconsistent (seaborn). Your mileage may vary. plotnine, on the other hand, shows a lot of promise. I estimate it currently has a 95% coverage of ggplot2’s functionality, and it’s still actively being developed. All in all, as someone who uses both R and Python, I’m very pleased to be able to transfer my ggplot2 knowledge to the Python ecosystem.
I figured that plotnine could use a good tutorial so that perhaps more Pythonistas would give this package a shot. Instead of writing one from scratch, I turned to the, in my opinion, best free tutorial for ggplot2: R for Data Science by Hadley Wickham and Garrett Grolemund, published by O’Reilly Media in 2016.
All I had to do was translate2 the visualization chapters (chapter 3 and 28) from R and ggplot2 to Python and plotnine. I would like to thank Hadley, Garrett, and O’Reilly Media, for granting me permission to do so. Translating an existing text is quicker than writing a new one, and has the benefit that it becomes possible to compare both the syntax and coverage of plotnine to ggplot2.
However, while quicker, translating is not always straightforward.
I have tried to change as little as possible to the original text while making sure that the text and the code are still in sync.
In case any errors or falsehoods have been introduced due to translation, then I’m the one to blame.
For example, to the best of my knowledge, neither authors have made any claims about
If you find such an error and think it is fixable, then it would be greatly appreciated if you’d let me know by creating an issue on Github.
The section numbers in this tutorial link back to the corresponding section of the original text, in case you want to compare them.3
Only this preface and the few footnotes scattered among the text are entirely mine.
This tutorial is also available as a Jupyter notebook and an R notebook in case you want to follow along.
If you clone the Github repository then you can find the notebooks in the
The README contains instructions on how to run the notebooks.
The Jupyter notebook is also available on Binder, but keep in mind that the interactive version may take a while to launch.
Without further ado, let’s start learning about plotnine!
It’s important to note that this tutorial is not meant to compare Python and R. The never-ending flame wars between these two languages are boring and unproductive.↩︎