
Designing the Nteract Data Explorer - polm23
https://blog.nteract.io/designing-the-nteract-data-explorer-f4476d53f897?gi=4c224b89aca1
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watersb
From 2018.

I'm trying to do a bit more Python notebook stuff and it seems worthwhile,
easy to do. I can understand why scientists like it.

But as a programmer no longer in science or academia, I don't quite understand
the context of nteract versus Jupyter. Pandas data frames, sure.

Is this a precursor to Jupyter?

~~~
troelsSteegin
The project [0] looks more like a peer to ObservableHQ [1], in the sense that
both appear to be working toward combining editing source, REPL,
visualization, and collaboration into an broadly adopted platform. Jupyter-
next.

From my perspective, a Jupyter notebook is a kind of commented script. It
integrates rendering into python REPL. Code, commentary, and charts, all in
one interactive frame. Cells serve as functions. The overall navigation is
linear, but you can replay the script from different cells. So, rather than
REPL, read-eval-render-script.

As a pre-notebook era REPL plus IDE coder, to me notebooks offer the advantage
of integrated data viz and a new kind of delivery medium for code tutorials
and for analytic & data projects. They feel like a better way to script-n-
tell, not a better way to develop - as long as you have an easy means to viz
from the commandline.

My impression is that part of what ObservableHQ is doing is to create a
substrate below a cell as an explicit evaluation graph that a developer can
work with directly. And that leads to a better programming experience for
developing notebooks, and for better developing within notebooks. I hope that
does not mis-state the vision. But the more I work with notebooks, the more
interesting something like an Observable [0] or perhaps Nteract [1] looks.

[0] [https://nteract.io/](https://nteract.io/) [1]
[https://observablehq.com/](https://observablehq.com/)

