I have used neither in quite a while now but there is an alternative from jetbrains that i started using because it shares the same ergonomics and had better (?) documentation.
Sorry for the confusion. Though, it is a mango tree in a mango garden! The continued development and maintenance of plotnine is supported by Posit, PBC, the same company behind the Tidyverse.
For another grammar-of-graphics-based visualization library (flexibly compose charts rather than simply pick a template), check out Altair https://altair-viz.github.io.
While it’s generally considered to be bad practice to import everything into the global namespace, I think it’s fine to do this in an ad-hoc environment such as a notebook as it makes using the many functions plotnine provides more convenient. An additional advantage is that the resulting code more closely resembles the original ggplot2 code. Alternatively, it’s quite common to `import plotnine as p9` and prefix every function with `p9`.
Disclaimer: I made the plotnine homepage and cheatsheet.
Because most of the time this will be used is not part of a software development project but rather producing publication plots in a script or plots in a notebook. Not what you would want to do when incorporating it into a web app.
Back when I did a lot of data stuff I used ggplot in R because it seemed to be popular, but I was just copy/pasting examples. Then one day I finally started to "get it" and actually read the manual. Learning the grammar of graphics was like a super power. I got to the point I could open pretty much anything people sent me and visualise it in a matter of seconds.
Although I've used Python professionally a lot more than R, I still felt like R was better at this. Somehow opening files in Python always feels a bit more "heavy". I don't really know why, though.
If you already use plotnine, or if this has piqued your interest, the next release (v0.16.0) will bring nice capabilities.
You can get a sneak peek by installing the pre-release:
pip install --pre plotnine
Details here: https://github.com/has2k1/plotnine/issues/1031
Disclaimer: I'm the author.
I have used neither in quite a while now but there is an alternative from jetbrains that i started using because it shares the same ergonomics and had better (?) documentation.
https://lets-plot.org/python/
And it comes with tidyverse-like cheatsheet[1] that I confused with ggplot2 when first discovered plotnine
[1]: https://github.com/rstudio/cheatsheets/blob/main/plotnine.pd...
Sorry for the confusion. Though, it is a mango tree in a mango garden! The continued development and maintenance of plotnine is supported by Posit, PBC, the same company behind the Tidyverse.
Disclaimer: I am the author.
After plotnine, with a solid & performant (more than the R versions) Python version of Purrr and Dplyr I might never reach for R again!
For another grammar-of-graphics-based visualization library (flexibly compose charts rather than simply pick a template), check out Altair https://altair-viz.github.io.
Using operator overloading of "+" to configure the plot is... a choice.
Plotnine is heavily inspired by the ggplot2 library, which uses the + operator in the same way: https://ggplot2.tidyverse.org/#usage
`from plotnine import *`
... I love the idea of a new python plotting library, but why is this anti-pattern so common with plotting libs?
While it’s generally considered to be bad practice to import everything into the global namespace, I think it’s fine to do this in an ad-hoc environment such as a notebook as it makes using the many functions plotnine provides more convenient. An additional advantage is that the resulting code more closely resembles the original ggplot2 code. Alternatively, it’s quite common to `import plotnine as p9` and prefix every function with `p9`.
Disclaimer: I made the plotnine homepage and cheatsheet.
> a new python plotting library
Whilst it's still not yet at 1.0.0, it's not that new: the first (0.1.0) release was in 2017: https://pypi.org/project/plotnine/#history
matplotlib's first release was in 2003, making it more than twice as old.
Because most of the time this will be used is not part of a software development project but rather producing publication plots in a script or plots in a notebook. Not what you would want to do when incorporating it into a web app.
Because it's aimed at data scientists who would rather be using R...
Back when I did a lot of data stuff I used ggplot in R because it seemed to be popular, but I was just copy/pasting examples. Then one day I finally started to "get it" and actually read the manual. Learning the grammar of graphics was like a super power. I got to the point I could open pretty much anything people sent me and visualise it in a matter of seconds.
Although I've used Python professionally a lot more than R, I still felt like R was better at this. Somehow opening files in Python always feels a bit more "heavy". I don't really know why, though.