When comparing Light Table vs Jupyter, the Slant community recommends Jupyter for most people. In the question“What are the best Python IDEs or editors?” Jupyter is ranked 8th while Light Table is ranked 25th. The most important reason people chose Jupyter is:
Because the editor is a web app (the Jupyter Notebook program is a web server that you run on the host machine), it is possible to use this on quite literally any machine. Morever, you can have Jupyter Notebook run on one machine (like a VM that you have provisioned in the cloud) and access the web page / do your editing from a different machine (like a Chromebook).
Specs
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Pros
Pro Inline evaluation
With LT's inline evaluation, you don't have to re-compile your whole source file. Each time you want to see an output, all you have to do is hover your cursor over the line you'd like to evaluate and press ctrl+enter
; LT will evaluate that line of code for you.
Pro Your code runs live as you write it
The "Watches" feature lets you see your code running live as you type it. This means that you can debug your code live while writing it, which leads to considerably less programming errors.
Pro Plugin manager available
LT has a plugin manager built directly inside of it. This plugin manager connects to LT's own registry of plugins, so whenever you want assistance while writing your HTML, JS, or even Python, just open up the plugin manager, search for it, and click the little install button beside it's name. Your plugin will then be installed.
Pro Web-based development allows for usage literally anywhere
Because the editor is a web app (the Jupyter Notebook program is a web server that you run on the host machine), it is possible to use this on quite literally any machine. Morever, you can have Jupyter Notebook run on one machine (like a VM that you have provisioned in the cloud) and access the web page / do your editing from a different machine (like a Chromebook).
Pro Interactive
Most IDEs require you to separately run Python to see the output of a particular piece of code. By contrast, Jupyter Notebook can evaluate Python statements inline, giving you the immediate feedback of interactive use of the interpreter while keeping your changes saved.
Pro Graphing , charting, and other math/numeric capabilities
The interactive editor is able to display complex equations, charts, graphs, etc. making this particular editor very well-regarded among data scientists.
Pro Open source
Because it is open source, you can review the source code and also propose extensions and fixes to it. It is also possible to fork the repository and make changes to it to customize it for your specific use case.
Pro Supports multiple different programming languages
Jupyter Notebook, formerly known as ipython, used to be specific to Python; however, in recent iterations, it has become capable of general purpose usage for any programming language. Thus it is possible to use this and have a consistent developer workflow, regardless of language.
Cons
Con Notebook-style makes reusing functions annoying
Con Interactive usage takes some getting used to
While the interactiveness is extremely, extremely powerful and useful, it does take a little bit of work getting to a point where it is "normal".
Con First time setup is more difficult than for other IDEs
Since Jupyter Notebook really requires two programs (the server and your browser) getting things setup in a way that works for you is a little more complex than for an ordinary IDE. For example, if you run the server and edit on the same machine, creating a little wrapper script that starts the server and then launches the browser pointing to it and gives an icon to this script is a small amount of setup but is more involved than a simple installer for other IDEs. Likewise, if you do remote development, creating a URL that will lazily spawn the Jupyter Notebook server and then turn it down when it is no longer in use is also a little bit of work to setup.
Con Non-trivial security configuration for remote access
By default, the editor is only accessible from localhost; however, if you want to run Jupyter on a VM in the cloud and do your editing through a web browser on a different computer (e.g. a Chromebook), there is some non-trivial security work to ensure that it is set up in a secure manner.
