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Comment by cpsempek | Hacker News Reader

This couldn't have come at a more convenient time for me. After years of telling myself I'd learn D3 I have finally started to as well as use it at work. I was using an extension in Jupyter but it was behaving unpredictably, and not being a frontend engineer by trade I found the process of sublime + browser foreign and cumbersome. As a big fan of jupyter and zeppelin I am stoked to see notebooks entering the js data viz domain.

Curious as to know how developers view the notebook paradigm? I feel like there is a sense of pride in working from the command line or vi, so wonder how these UI heavy approaches are received by the js/python/scala community.

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I learned D3 a year back and am reasonably fluent at it now and I'm not sure I would ever pick this platform to learn D3. The easiest way to learn is simply to have a blank HTML file with your D3 code in it. The reason you want to do this is because you want to take advantage of powerful debugging tools that exist within modern browsers (Chrome dev tools, or equivalent from Firefox). ObservableHQ will simply add a layer in between that will make everything more annoying to debug and slow down experimentation.

I think it is really cool, and I have mountains of respect for Mike for his contributions to the field of data visualization/javascript, but my feeling is that this is more suited for sharing "notebooks" that are interactive and also to potentially allow for collaborative notebook building/demos. In essence, this is a giant evolution over his previous work on bl.ocks.org (https://bl.ocks.org/mbostock)

I know many developers who've integrated Jupyter Notebooks in their workflow too. For Python-specific things, it's a great way to replicate bugs, try out a few initial solution paths, and share them with collaborators who can then interact with.

Regarding D3, I haven't tried that out yet, although I've used the `folium` Python package, which generates leaflet.js code you can put on a website. Otherwise, maybe check out [Jupyter Dashboards](https://github.com/jupyter/dashboards), whose interactive widgets seem to be in the same vein as OP.

The BeakerX extension to Jupyter has good support for D3 visualization of data prepared in Python and other languages. http://BeakerX.com/

Here's a live demo on MyBinder:

https://mybinder.org/v2/gh/twosigma/beakerx/0.12.0?filepath=...

To me, a netbook like this is similar to a REPL and works well for visualization tasks. You can figure it out in the netbook/REPL and then move it to "real code" once you've got it figured out.

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