JupyterLab is ready for users
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Re: JupyterLab is ready for users
#92Do I read this correctly as hinting that Jupyter Notebook is being replaced by an IDE?
Re: JupyterLab is ready for users
#93Mathematica is wonderful in terms of sheer computational power, but the notebook interface it presents is hopelessly outclassed nowadays by initiatives such as these. I keep hoping Wolfram will spring some impressive new interface on us that will enhance usability for power users (rather than their weird attempts at bringing ‘computation’ to random casual users), but... I'm giving up hope. This looks very impressive.
Last time I've tried Jupyter, about a year or two ago, the user experience wasn't even close to that of Mathematica. I guess I'll give it another chance, but can you tell me in what way Mathematicas notebook interface is outclassed, in your opinion? The only major beef I have with Mathematica are its default stylesheets. They suck, every single one of them. There is no good distinction between input and output fields…
Re: JupyterLab is ready for users
#94Re: JupyterLab is ready for users
#95Earlier quoted context omitted.
I haven't used it much, but I believe you can do this by using (or more likely slightly extending) the init_cell nbextension. https://jupyter-contrib-nbextensions.readthedocs.io/en/lates...
Oh, that's cool - it's just some javascript that's essentially clicking execute on the selected cells. That makes sense, and hacked up version of this could work fine! Thanks!
Re: JupyterLab is ready for users
#96At the same time, the community has faced challenges in using various software workflows with the notebook alone, such as running code from text files interactively. The classic Jupyter Notebook, built on web technologies from 2011, is also difficult to customize and extend. Do I read this correctly as hinting that Jupyter Notebook is being replaced by an IDE?
Guess so :-)
Re: JupyterLab is ready for users
#97Earlier quoted context omitted.
No, R and Python are fundamentally different. I do believe we will see a shift in what they are used for though. Python more for the "deep learning" stuff, and R for more statistical, non-deep-learning work.
Jupyter supports lots of languages (including R). RStudio is probably still nicer for working R, but I haven't done any serious analysis in R for a while now.