I see where you're coming from. From where you sit Jupyter is a language agnostic tool and so in. But the fact that there's dozens of solutions in this space is surely a problem?
I'd have thought there would be some things you could strongly encourage:
1. Come up with some standard format where the code and the data live in separate files.
2. Come up with some standard format where you can take load a regular .py script as a cell based notebook using metadata comments (and save it again).
If these came out of the box it would solve most of the issues.
I work with a bunch of 'data scientists' / 'strategists' and the like who love their notebooks but it's a pain to convert their code into an application!
In particular:
* Notebooks store code and data together, which is very messy if you want to look at [only] code history in git.
* It's hard to turn a notebook into an assertive test.
* Converting a notebook function into a python module basically involves cutting and pasting from the notebook into a .py file.
These must be common issues for anyone working in this area. Are there any guides on best practices for bridging from notebooks to applications?
Ideally I'd want to build a python application that's managed via git, but some modules / functions are lifted exactly from notebooks.