Why Jupyter is data scientists’ computational notebook of choice

Re:Why Jupyter is data scientists’ computational notebook of... by TeMPOraL on Tuesday October 30, 2018 @03:21PM (#18340404)

The closest thing I've seen to what you described would be... Emacs. It actually uses the "metadata in file-specific comments" paradigm. You can put file-local values for Emacs variables in comments at the top or bottom of your file, like described in [0].

Your example could be rewritten as:

  # -*- notebook-lang: python -*-

  // -*- notebook-lang: scala -*-

Still, the usual way of using Emacs for "interactive notebooks" is via org-mode, which is a better Markdown with support for (among other things) executing code blocks straight in the org document you're writing. This way, Emacs support all your points 1 to 5, and is generally more powerful than Jupyter or other similar things, but it also means you can kiss any kind of collaboration goodbye.

For some weird reason, the more powerful a tool, the less likely it is other people will be using it.

[0] - https://www.gnu.org/software/emacs/manual/html_node/emacs/Sp...

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Re:Why Jupyter is data scientists’ computational notebook of... by nemoniac on Saturday November 03, 2018 @09:42AM (#18370119)

Well, the reason is not so weird. Generally speaking, the more powerful the tool, the higher the bar. More time and effort is required to learn it and become proficient with it. When it comes to the very powerful tools, few will have the aptitude or be prepared to put in the effort to learn them.

For those who don't, less powerful tools take their place and proliferate.

As you say, emacs checks all the boxes but the majority is not prepared to learn it and prefers to program throught their browser.

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