I don't get the people here saying don't use Jupyter notebooks, or they are bad software engineering.

So much Python development is trying snippets of code in a REPL as you introspect live objects, then once they're right pasting them into the IDE.

All my Jupyter notebooks are like that, where my code starts as cells of a line or two, as I check each output. Then I coalesce them into a function (which avoid the problem of execution order that some people here mention). Then I may move those functions into a normal Python module and build up complex classes, add unit tests. Or if the goal is communicating with other people the results of running that code, I'll add explanatory text and mathematical formulas, break it up into sections, include references to papers and web links.

I think what people miss here is many software development tasks, especially in data science and machine learning, are essentially exploratory data analysis. And the outputs are graphs, tables of data etc, designed to be read by humans. Jupyter's ease of use for iteration, and the ability to interleave documentation, formulas, graphs and tables with the code is a big win.

In my daily work, I have my IDE and Jupyter open side by side. The Jupyter notebooks are version controlled like my .py modules.

Over time, I expect editors like VSCode to edit Jupyter notebooks natively. And also more code editing to move from IDEs into the Jupyter (via integrating the Monaco editor). We will all benefit from that crosspollination, whether we are principally software engineers or data scientists.

Jupyter code is draft code. Draft code rarely makes into clean code, because it requires extra tedious effort. Most of us are too overworked to have the time or energy to perform it.

Alternatively one can write exploratory code as unit tests and run them inside an IDE. When the exploration is done, three quarters of the tedious cleanup effort is already done. There are already unit tests. There are already APIs exercised by the unit tests. The code is in much better shape.

The missing two bits:

* No charting support. To the best of my knowledge, neither PyCharm nor VSCode have an output console with HTML support.

* Caching. Some operations take a lot of time. At the extreme, fast.ai style development, where some cells train a model for hours/days. Re-running such code from scratch on every test run is impractical. An opportunity for a library with explicit save/load/cache/checkpoint support.

Vscode has decent Jupyter support with its python plugin. What I really missed my jupyter notebooks was a world class editor with keyboard shortcuts and code completion. So I went the other route, running jupyter notebooks in vscode.

I made a really simple python cli module that would convert from jupyter notebook to python files with runnable cells in vscode, and vice versa.

https://github.com/nojvek/vscode-ipynb-py-converter/blob/mas...

Really upped my game while doing the udacity courses.

Vscode does indeed have a html output console.

Wow! This is insanely cool! You should package this up as a VS Code plugin so more people can use it. At a minimum, please post a license.txt file in your repo so folks can actually use it (MIT or similar is pretty easy and common for something like this, without a license.txt file it's not clear whether others can actually use it and many places that means they can't).