I share your opinion about the superiority of org-mode as a means to assemble a document out of various pieces. It's not hard to show nicely formatted code, the code's output, the math behind the code and perhaps a graph of the results all in a LaTeX/PDF document and or HTML. All of this can be done with a flat text file that is easy to save as a part of a project under source control.
What I wonder is why are Jupyter notebooks so popular? I've tried them, but perhaps I don't understand the workflow in the way people use them. Are they intended to be works in progress where one explores ideas and looks at results? This is how I've usually used them and Mathematica's notebooks as well.
For me though, my Jupyter or Mathematica notebooks end up with lots of false steps and dead-ends. To prepare a presentable form of the results I have to go back and edit the notebook to such a degree that it doesn't feel like working in a notebook as much as it is working in a complex document with a really weak set of editing tools. To be fair, I haven't used Jupyter or Mathematica for a couple of years and things may have changed. Maybe HN readers can help be understand how to more effectively use these tools.
> What I wonder is why are Jupyter notebooks so popular?
How easy is it to share a Jupyter notebook with someone else, versus sharing an org-mode notebook? Think about the recipients rather than how hard it is for you. Assume the person you share with needs to be able to re-run your notebook. Assume the person you share with isn't using the same operating system that you are. Be honest and think about all the steps involved.
With Jupyter, I send a single install link to Anaconda. With Emacs + org mode + python + numpy + scipy + matplotlib, I might not even know where to begin if the recipient is using Windows or Mac. The recipient needs to install emacs, and they need to know how to use Emacs. They have to know how to install python packages (and possibly a package manager), and ideally be able to use virtualenv too.
If I need different libraries than what Anaconda comes with, I can put the commands in the notebook, and I won't mess up the recipient's system version of Python. With org-mode, they'd need to figure it out on their own.
If I choose to use a different language than Python in my org-node notebook, the recipient will have to install that programming language and environment themselves before they can use your notebook.
It's the simplicity and bundling of dependencies that make Jupyter popular. All the flexibility and power you get with org mode comes at a high price.
If the notebook is something you can share publicly, then https://mybinder.org/ makes sharing even easier. You send a link, and the only thing the recipient needs is a modern browser.
If they want to actually work on the notebook and keep their changes, they'll probably need to install it (or switch to a platform like Cocalc). But Binder is enough to let them view, run it and make experimental changes, without installing anything or creating any new account.
> How easy is it to share a Jupyter notebook with someone else, versus sharing an org-mode notebook?
Well, now you're talking about a whole environment. Use docker, or VMs or whatever. There's plenty of options for doing this (we actually use Docker for Jupyter, to make sure we work off a consistent install, so I guess sharing can be non-trivial if you walk a bit off the beaten path, anyway)
> If I choose to use a different language than Python in my org-node notebook, the recipient will have to install that programming language and environment themselves before they can use your notebook.
Sure, and if you need to use another language from your Jupyter notebook, you can't. I don't get your point. Yes, if you need to use a tool, you need to install it. It requires a minimum of computer literacy.
Yes, Anaconda + Jupyter is an environment and org mode is not. That’s a big reason Jupyter is so popular.
> I don’t get your point.
My point was Jupyter inflicts far less technical burden and debt on other people. There’s a huge downside to custom environments. If that’s not obvious, that explains why it’s hard to understand jupyter’s popularity.
> it requires a minimum of computer literacy
I’m sure you didn’t mean it that way, but this borders on white tower programmer centric dismissiveness.
Personally I know how to install all the things an org mode notebook might require, but I don’t want to. It takes too much of my time and energy. Expecting other people to adopt your choice of tools and environment by arguing that your tools are basic literacy is presumptuous. Use org mode, I’m wildly in favor of people using tools that they like, and you like it. Just don’t claim it’s objectively better on all axes when it’s not.
> Yes, Anaconda + Jupyter is an environment and org mode is not. That’s a big reason Jupyter is so popular.
You're again comparing apples to oranges. If you want to use Jupyter, you need to install a whole lot of stuff (easier to do if you're using Anaconda, agreed). It's "not simple" enough that there are public docker images for it. My point is that when tooling is an issue, you can do the exact same thing with an org-based setup.
>My point was Jupyter inflicts far less technical burden and debt on other people. There’s a huge downside to custom environments. If that’s not obvious, that explains why it’s hard to understand jupyter’s popularity.
I agree that there's difficulty in achieving reproducible environments, and that's why there's tools that handle that for you. I think that much of Jupyter's popularity comes from the fact that it's easily available, and based on Python, which let's non-programmers get started moderately quickly. I just don't think that it's the best choice. This is not to shit on Jupyter, I've mentioned a bunch of advantages of using org (which by the way, you haven't really addressed, other than focusing on the setup aspects)
> I’m sure you didn’t mean it that way, but this borders on white tower programmer centric dismissiveness.
It's not, really. My point is that when you're doing presumably advanced analysis on presumably complex data, you (should!) certainly have the capacity to deal with tooling.
I think of a Jupyter notebook as a REPL with superpowers. I can try code out in real-time and incrementally like a REPL, but when something breaks, the full history is right in front of me, so I can tweak something 10 lines ago and run the entire thing again like with an ordinary source file. It combines the best of both methods of programming.
Seen that way, sharability is just a nice bonus. Yeah, I'd never try to turn anything directly from a jupyter notebook into a serious way of sharing research results---but for a quick and dirty "here's what I did" on a blog or email or something, or to show students, it does the job.
You should give some of the emacs modes a try. Could do this sort of stuff for wire a while now. And I'm really just talking of interior buffers. Not org.
Kind of odd, but repl on steroids is really just how dim the modern view of the old lisp machines is. Most of our neat repl tricks were just taken for granted on some of those machines.
Yeah, I like the idea of that stuff, and org-babel too, actually. But the setup just seems like a bit of a bear. I use spacemacs, and the python layer for that[1] can spawn a repl and send code, but in order to do so incrementally, I have to manually select the code to send to the repl. The great thing about the cell construction in jupyter is that it supplies a natural way to organize chunks of code, you don't have to define the chunks of code before executing them.
Maybe you should try ob-ipython. It'll spawn a Jupyter server with a python kernel, and send your source blocks as cells, and you also get to keep all the other org goodies.
> What I wonder is why are Jupyter notebooks so popular?
I often wonder the same. I'm now kinda forced to use them, since they're the tool of choice in my team, but it's a major pain. I think that they're "easy" (in the way Rich Hickey describes "easy" vs "simple") to get started with (just as python, btw), but their lack of flexibility comes back to bite you sooner or later.
For me, I've resorted to using a mix of EIN and ob-ipython, and then copy/pasting as needed to keep the stuff in synch with Jupyter. I'm now considering writing a simple ox-jupyter exporter, and maybe some import functionality, to avoid dealing with Jupyter directly.
John Kitchin has written a basic exporter (http://kitchingroup.cheme.cmu.edu/blog/2017/01/21/Exporting-...), but it doesn't support inline images (it should be simple to fix this), and it doesn't really adhere to the ox- style of exports, so I think I'll probably do my own instead of adapting his (it'll be a good learning experience, for sure).
What I wonder is why are Jupyter notebooks so popular? I've tried them, but perhaps I don't understand the workflow in the way people use them. Are they intended to be works in progress where one explores ideas and looks at results? This is how I've usually used them and Mathematica's notebooks as well.
For me though, my Jupyter or Mathematica notebooks end up with lots of false steps and dead-ends. To prepare a presentable form of the results I have to go back and edit the notebook to such a degree that it doesn't feel like working in a notebook as much as it is working in a complex document with a really weak set of editing tools. To be fair, I haven't used Jupyter or Mathematica for a couple of years and things may have changed. Maybe HN readers can help be understand how to more effectively use these tools.