I've always liked the ggplot2 and the Grammar of Graphics approach to plotting so much so that I wrote my own DSL based on it - it is standalone, written in Rust, has WASM bindings (as you can see on the website) and more:
There's LSPs for both, LSP clients for VS Code, and even language diagnostics for standalone Monaco editors in the browser.
Of note is that the same language diagnostics are exposed via the WASM as via the LSP interface allowing for the same friendly red squiggles to look and work the same in both your browser with Monaco and your editor with the LSP!
Plotnine has been great in my usage, but I see violin plots on the front page. Just say no to violin plots.
In almost any situation you either want to talk about the actual distribution (in which case plotting the distribution on one side of the line arranged horizontally is significantly superior to plotting it vertically on both sides of the line for some reason as a violin plot does[1]) or you want to talk about the quartiles etc in which case a boxplot is better.
A violin plot tries to do both and as a result does them both badly.
[1] I remember in one meeting before I knew better, producing some violin plots and putting them on a slide and I knew I had gone wrong when that slide came up and everyone in the room had this confused expression on their faces and was leaning their head over to the side to try to see the distribution better. When your visualization produces obvious confusion like that, you can be completely certain it has failed.
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.
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.
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.
Like others mention, this is inspired by ggplot2, a Grammar of Graphics library.
The whole idea is graphics are composed by adding "layers", not like layers on a canvas, but like pouring paint into a pot, then the library understands the content and paints it to the canvas.
Layers might be pure data, geometry (lines, points, ...), annotations, styles, axis, etc.
When you get familiar with it, it's much more natural way of describing plots, better composition and easier exploration
And a side note, R actually includes a pipe operator (as in Unix `|`), and said if they'd know about it at the time, they would have never used the `+` operator
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.
https://williamcotton.github.io/algraf
It pairs well with a related data translation DSL:
https://williamcotton.github.io/pdl
And you can see the two working together here:
https://williamcotton.github.io/datafarm-studio
There's LSPs for both, LSP clients for VS Code, and even language diagnostics for standalone Monaco editors in the browser.
Of note is that the same language diagnostics are exposed via the WASM as via the LSP interface allowing for the same friendly red squiggles to look and work the same in both your browser with Monaco and your editor with the LSP!
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.
In almost any situation you either want to talk about the actual distribution (in which case plotting the distribution on one side of the line arranged horizontally is significantly superior to plotting it vertically on both sides of the line for some reason as a violin plot does[1]) or you want to talk about the quartiles etc in which case a boxplot is better.
A violin plot tries to do both and as a result does them both badly.
Extended anti-violin plot rant here https://www.youtube.com/watch?v=_0QMKFzW9fw
[1] I remember in one meeting before I knew better, producing some violin plots and putting them on a slide and I knew I had gone wrong when that slide came up and everyone in the room had this confused expression on their faces and was leaning their head over to the side to try to see the distribution better. When your visualization produces obvious confusion like that, you can be completely certain it has failed.
Semi related -- I made a little d3.js AI wrapper that works pretty well for making quick charts -- https://prompt2chart.com/share/e998a3f6-9482-4c18-931f-a4513...; https://prompt2chart.com/;
[1]: https://github.com/rstudio/cheatsheets/blob/main/plotnine.pd...
Disclaimer: I am the author.
2 things that would be awesome are interactive plots (hover + text box) and chlorpleth (tiled map) plots.
On closer look you have already nailed the latter!
Disclaimer: I am the author of plotnine.
https://lets-plot.org/python/
Like others mention, this is inspired by ggplot2, a Grammar of Graphics library. The whole idea is graphics are composed by adding "layers", not like layers on a canvas, but like pouring paint into a pot, then the library understands the content and paints it to the canvas.
Layers might be pure data, geometry (lines, points, ...), annotations, styles, axis, etc.
When you get familiar with it, it's much more natural way of describing plots, better composition and easier exploration
... I love the idea of a new python plotting library, but why is this anti-pattern so common with plotting libs?
Disclaimer: I made the plotnine homepage and cheatsheet.
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
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.