I don't write Python code for my work. Last weekend I came across an interesting Jupiter notebook and figured I'd give it a try on my work laptop. "It's probably as easy as brew install pip and then use that to load the other dependencies," I assumed.<p>Over an hour later I had to give up. There was initially some kind of Python version conflict on my Mac. Eventually some version of JupyterLab was installed somewhere, but it couldn't find any dependencies for the notebook. As a complete newbie to the Python ecosystem, I googled for instructions and found various environment managers, which then failed possibly because something is incompatible with Big Sur. More googling revealed instructions with complex CFLAGS environment setups to fix it, which didn't — and at that point it was very far from the supposed convenience of scripting languages anyway.<p>I don't think it's Python's fault. Probably all the programming toolchains are this hard to a newbie! But it was a humbling experience after 34 years of programming, not being able to load a piece of sample code in a Sunday afternoon.
Ok this may be kind of stupid, but my primary reason for not switching over to jupyter lab (from notebooks) was the right-click menu wouldn't let me copy images (plotting outputs typically) from the in-line output. However I found out today that it has always been available if you hold shift...so if that was anyone's issue this is a great time to give JupyterLab another shot!
I'm one of those extension developers that working on getting my extension working on JupyterLab 3.0 :-)<p>I've been developing Mito (<a href="https://trymito.io" rel="nofollow">https://trymito.io</a>), a spreadsheet extension to JupyterLab that allows you to edit a spreadsheet in a notebook as if you're editing Excel. You edit the spreadsheet, and Python code gets generated that corresponds to your edits.<p>Feedback on the above greatly appreciated. And congrats on the release :)
Just to say a big thank you to all those who have worked on JupyterLab. It's by far my favourite notebook interface and I think gets the balance between a clean interface and powerful features just about right.<p>I just wish that the cloud providers would adopt it as the basis for their products.
I was really excited to see the visual debugger there. I tried it myself, it was really easy to get it going. Unfortunately, using it briefly in the notebook I was using I found it pretty buggy, lots of glitches and big slowdowns in the UI. I would also really love to see the option to drop into an interpreter while debugging.<p>Still, can't complain too much about an open source project, thanks to the team for all their hard work.
I don't understand why Jupyter notebooks are still in use as a writable format when there are editors like VSCode that can treat ordinary python files as notebooks. The Python extension can submit code blocks to the kernel and import and export Jupyter notebooks, but VSCode is also a real editor on top of that (debugging, git, vim mode, hover info, etc.). For distribution, as a read-only format (like PDF), notebooks are great. But why do people continue to do their work in one?
I skimmed the page and didn't see any mention of sharing or collaborative use. That's the biggest obstacle I'm seeing with getting buy-in at work. I need to be able to let some users see the notebook in read-only mode, others should be able to run it but not edit it, others should have full access.<p>Maybe there's a non-hacky way to do this and I'm missing it?
Interesting that they include mamba install instructions before conda.<p>Is mamba taking off with the recent (and upcoming) license changes that anaconda made? I’m not familiar with mamba though I’ve had my eyes open for an alternative to anaconda (also seen poetry mentioned a few times here).
Great news! I'd only just updated the Purple Please theme[1] for Jupyter Lab 2.0, will have to look at doing the same for Jupyter Lab 3.0. It looks like they've made some improvements to extension development too which is good to see.<p>[1] <a href="https://datacrayon.com/posts/tools/jupyter/theme-purple-please-for-jupyter-lab/" rel="nofollow">https://datacrayon.com/posts/tools/jupyter/theme-purple-plea...</a>
Hmm, what is mamba? I thought I was at least sort of up to date with the various python dependency managers, but that one is new to me.<p>Relatedly, I have a grand vision of having as part of my development environment a jupyter notebook always at hand, in which to explore data as necessary, whip up scripts, accumulate little helper functions, etc. Basically, any time I have that "hm, I wonder..." itch, I'd like to be able to quickly whip up a solution in my notebook. I'm a skilled developer in terms of larger systems, but have a weakness when it comes to very early stage "throwaway" scripts to answer ad-hoc questions.<p>I spent a few days trying to set up jupyterlab in an "ideal" way, so that I could have easy access to python libraries within my notebook, and also a reproducible environment since I plan to push my working directory to GitHub and would like to sync across different machines. I got confused by virtualenv vs venv, and tried conda to install libraries, but ran into various problems. Oh, and part of it was trying to have nbdev (from the fastai folks) as part of this toolkit.<p>Anyone have a setup like this that they use and want to share? Part of me wants to just throw in the towel and learn R and RStudio, since I've heard its ggplot is the best plotting library anyway.
I've seen a few projects that have used the JupyterLab UI for other projects as a simple interface - it looks really neat and slick.<p>I just wondered if anyone has any ideas how you go about this as I've been drawing a blank.