So have you been looking for something like RStudio, but for Python?
It’s been out for some time, but a recently updated release of Rodeo gives an increasingly workable RStudio-like environment for Python users.
The layout resembles the RStudio layout – file editor top left, interactive console bottom left, variable inspector and history top right, charts, directory view and plugins bottom right. (For plugins, read: packages).
The preferences panel lets you set the initial working directory as well as the path the required python executable.
Code selected in the file editor can be run in the console. Charts can be generated using matplotlib and are displayed in the chart view area bottom right.
As with RStudio, you can write reproducible research documents that blend markdown and code and render the result as HTML or PDF.
As you might expect, charts can be embedded as outputs in the document too.
Whilst the first version of Rodeo was a flask app viewable via a browser, and installable via pip, the latest version is an electron app, like RStudio. I found the ability to run Rodeo directly in the browser really useful, but the RStudio folks appear to have found a way of running RStudio via a browser using their RStudio server, so I’m hoping there’ll also be an open source version of Rodeo server available too?
One thing I’m wondering is whether Rodeo is a front end that can run against other Jupyter kernels? I notice that there is already a branch on the Rodeo github repo called r-backend, for example…?
Another thing I haven’t really clarified for myself are the differences between authoring (and teaching/learning) using the “Rmd/knitr” RStudio/Rodeo style workflow, and authoring in Jupyter notebooks. Notebook extensions are available that can suppress cell output etc to provide some level of control over what get rendered from a notebook used as an authoring environment. I guess what I’d like for Jupyter notebooks is a simple dropdown that lets me specify the equivalent of knitr text result options that control how code cells are rendered in an output document.
And if you do prefer the notebook route, here are Seven Ways of Running IPython / Jupyter Notebooks.