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Why Jupyter is data scientists’ computational notebook of choice

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Re: Why Jupyter is data scientists’ computational notebook of choice

#201

Post by heavenlyblue »

atomic77 wrote: post #115
agibsonccc wrote: post #24

The only thing that stops me from being able to use notebooks full time is their intellisense compared to IDEs is horrible. I like being able to use them for demos/presentations, but I can't imagine trying to code within one primarily. Especially when it comes to tracking results. How do people cope with this? Do you supplement it with other tools? I spend a lot of my time in an IDE and then just paste some of the co…

I find this odd because I am the opposite - one of my primary use cases for Jupyter/ipython in general is the ease with which I can get 'live' code introspection and intellisense. It's often my prototyping sandbox for python code that I then move into my IDE once it's close to being ready. I also notice that developing in this way encourages me to create smaller, more testable functions that i can easily work with in…

Doesn't PyCharm provide IntelliSense?

Re: Why Jupyter is data scientists’ computational notebook of choice

#202

Post by jonnycomputer »

joshuamorton wrote: post #165
jonnycomputer wrote: post #93

Earlier quoted context omitted.

If you ever have used an R Notebook written in R-Markdown, then its pretty easy to see why Jupyter Notebooks putting everything in JSON is just... infuriatingly wrong-headed. In an R Notebook, I can see my code, I can see my text, everything is exceedingly simple to understand, and I can edit it in any of the fantastic text editors out there (Jupyter's editor is not among them)

As a result of the serialize-to-json approach, jupyter supports R, python, scala, go, lua, bash, julia, and haskell, among others. Its accessible to a much wider range of programmers, at the cost of version control being a bit weirder.

It might be the case that serializing to json facilitates support for multiple languages, though I wonder how.

With the reticulate package in R Markdown you can run python chunks, by putting, e.g.

```{python}

for i in range(1:10):

    print("{}:{}".format(i, i*i))
# etc

```

And in emacs org-mode you can:

#+begin_src python

for i in range(1:10):

    print("{}:{}".format(i, i*i))
#+end_src

Language support in org-mode is pretty comprehensive, afaik.

I do not know the details of the implementations behind these, but my own source code is plain and simple unserialized text, and that means a lot to me.

Re: Why Jupyter is data scientists’ computational notebook of choice

#203

Post by jonnycomputer »

mlevental wrote: post #188
celrod wrote: post #132

Earlier quoted context omitted.

RStudio is also my favorite editor. All my work is data science / stats related, where I like the workflow of writing/modifying code in a .R (or .py) file, and being able to quickly experiment by running chunks in a REPL with Ctrl + Enter. R and Python are supported. No Julia, unfortunately. VS Code and Atom support similar workflows with Julia. However, the Julia Language server in VS Code is extremely unstable and…

r studio supports python? does it completion and stuff like that?

You can use R Studio to execute the code, and you're preferred editor to do the editing, if you like.

Re: Why Jupyter is data scientists’ computational notebook of choice

#204

Post by zmmmmm »

As with so many things python related (including python itself), I am perplexed by how willing people seem to be to fall in love with solutions that have so many limitations and problems. I find Jupyter just barely usable. I constantly have issues with editing in the cells, diagrams not sizing correctly, cells accidentally displaying huge amounts of data and freezing my browser, complete failure of autocompletion in many languages, a very awkward security model involving manual cutting and pasting of auth tokens around, nearly impossible to get a reasonable rendering of the notebook into something reasonable like PDF (yes there attempts at solutions, they are full of problems). Many limitations derive directly from the architecture where the kernels are limited in what they can do because specific parts have to be interpreted in the browser that are language specific.

From my perspective, it's a dumpster fire - in 2018 there should be something so much better than this. RStudio is a thousand times better but only does R. I used to like Beaker Notebook but it gave up due to Jupyter's popularity and converted itself into a bunch of Jupyter extensions which now have all of Jupyter's limitations.

Yet despite all this I can see that there's this enormous community that loves this and keeps developing and contributing to it.

Re: Why Jupyter is data scientists’ computational notebook of choice

#205

Post by stult »

zmmmmm wrote: post #204

As with so many things python related (including python itself), I am perplexed by how willing people seem to be to fall in love with solutions that have so many limitations and problems. I find Jupyter just barely usable. I constantly have issues with editing in the cells, diagrams not sizing correctly, cells accidentally displaying huge amounts of data and freezing my browser, complete failure of autocompletion in…

I love everything about RStudio, except for all the R stuff.

Re: Why Jupyter is data scientists’ computational notebook of choice

#206

Post by brootstrap »

j2kun wrote: post #198
makmanalp wrote: post #51

The majority of the complaints I hear about notebooks I think come from a misunderstanding of what they're supposed to be. It's a mashup between a scientific paper and a repl. So it's useful for a bit of both: a) Just like with a paper, you can present scientific or mathematical ideas with accompanying visualizations or simulations. From the REPL side, as a bonus, you get interactivity, and the reader can pause and e…

My complaint is that people use notebooks as production systems.

I think we can all agree some notebooks are shit storms and should not be relied upon at ALL for production. AT my job we started using notebooks as an 'in-repo', 'interactive' documentation of sorts. Showcase various modules and give simple usage examples of them. It was pretty awesome. I love using notebooks as a more advanced scratch pad. For the times when ipython shell isnt enough, and you want something extra. Also i had to install the vim bindings ASAP, gotta have that vim

Re: Why Jupyter is data scientists’ computational notebook of choice

#208

Post by aw3c2 »

reilly3000 wrote: post #127

Does anybody know of a good hosted solution of JupyterHub? I made a neat notebook that I needed to share with my non-technical team, it was using iPyWidgets to do some interactive modeling, but they each needed to be able to use it independently. It has private data so I couldn't use Binder. I've been following Zepl.com for a long time, but couldn't use them here because Zepplin doesn't support iPyWidgets. Pretty soo…

https://the-littlest-jupyterhub.readthedocs.io/ is really easy to deploy on your own server!

Re: Why Jupyter is data scientists’ computational notebook of choice

#209

Post by gaius »

zmmmmm wrote: post #204

As with so many things python related (including python itself), I am perplexed by how willing people seem to be to fall in love with solutions that have so many limitations and problems. I find Jupyter just barely usable. I constantly have issues with editing in the cells, diagrams not sizing correctly, cells accidentally displaying huge amounts of data and freezing my browser, complete failure of autocompletion in…

it's a dumpster fire - in 2018 there should be something so much better than this

In 1998 I was using a tool called MathCAD that provided a notebook interface running as a plugin to MS Word. In 2018, Jupyter is still not as good as that. Some things are just not meant to be webpages.

Re: Why Jupyter is data scientists’ computational notebook of choice

#210

Post by newen »

zmmmmm wrote: post #204

As with so many things python related (including python itself), I am perplexed by how willing people seem to be to fall in love with solutions that have so many limitations and problems. I find Jupyter just barely usable. I constantly have issues with editing in the cells, diagrams not sizing correctly, cells accidentally displaying huge amounts of data and freezing my browser, complete failure of autocompletion in…

I like jupyter and use it all the time mostly because I can log what I do for future reference. But I have to work around it so much stuff it can get really annoying sometimes. Yeah, compared to the Matlab IDE and how easy it is to use, it's not even close. But it's an open source project, so people tend to find it awkward to criticize it a lot (I mean, that sentiment is justified since it's mostly volunteer work but sometimes it can get to be too much).
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