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Comment by enriquto | Hacker News Reader

Why would you commit the outputs into git? That would be like committing compiled binary objects or pdfs. Of course the outputs are useful, but you just want to commit the sources.

The .ipynb stores inputs and outputs together in an unholy way. It is much cleaner to separate them. The inputs are python (or markdown) files that you can edit with a text editor and version control with git. The outputs are html, pdf, or whatever you want to nbconvert to and share.

The .ipynb file would only be useful if you want to share a stateful notebook, whose state cannot be easily reproduced by the people who you share it with. But that would be really bizarre and definitely in bad taste. Sharing the .ipynb is akin to sharing your .pyc files.

I love working with notebooks, but as a measure of hygiene I avoid .ipynb files altogether.

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"unholy" "cleaner" "stateful" "bizarre" "bad taste" "hygiene"

I'm not sure whether you're unaware or just feigning ignorance, but notebooks are frequently used to share partial results, often in the context of "research", however you may interpret it. Imagine a grad student or data scientist preparing some code and plots to show during a weekly meeting.

In this context, the only thing that matters is quick progress and advancing understanding of a problem. The highly loaded words you employ while blasting the idea of uploading Jupyter notebooks are not relevant here. Wasting time on these things is seen as a bad thing. It's clear why someone using notebooks this way would want the interaction with Git and GitHub to be as seamless as possible: uploading something to GitHub is a very easy way to share it, even if this isn't the platonic ideal.

It will probably cause you some pain, but I've known people to commit binary objects and PDFs to git to accomplish the same ends. ;-)

I agree entirely, outputs are great and I consider it best practice to provide notebooks with outputs provided.

As a concrete example, this one-liner of Python code is much more interesting to those who don't recognize it when it's presented with the associated output.

    4*sum([(random.random()**2 + random.random()**2)**.5 < 1 for _ in range(10**7)])/10**7

This is also useful, e.g. when viewing the read-only export of a notebook.

(The one-linear above is a monte-carlo simulation which approximates Pi. On one run, this result came to 3.1410416.)

As someone who uses notebooks for research, outputs don’t play well with git. You can end up with very large commits that GitHub or wherever your repo lives may not like if you have a lot of plots and animations.

Moreover, while research moves fast, reproducibility remains important. If your notebook is stateful, then when you share it I may not be able to recreate your result or you might have a bug due to something lingering in the notebook state. Having your outputs is convenient, but if I download the notebook, run it myself, and find that the code doesn’t run because there’s some variable that got defined earlier in your session but that code got deleted during iteration, that’s really not helpful. It’s the equivalent of handing someone your lab notebook but you kept erasing over early pages to make room for new content.

That’s one example of a bug. You could easily introduce more subtle bugs where the state leads to invalid results.

There are as many different "research workflows" as there are researchers.

I'm a researcher and don't use notebooks for all the reasons you outlined and more. I have my own approach to dealing with reproducibility which is low tech and works for me and my collaborators.

My comment is meant to point out that there are many researchers who view all of the problems you describe as unimportant and not worth spending time on.

Sometimes I work on software development, and this mindset («the only valuable asset is the code») makes total sense. But if I work on analytics / datascience projects, the analysis including outputs could be time consuming to run, validate, and visualize. In these cases, it might be required to version the outputs.

I’ve never used jupyter for taking notes in a lab setting, but with more and more instruments being computer/network connected, I imagine this would make total sense - put your data and notes with your analytics work.

Many jupyter users are not «software developers», they just use code to perform their work.

Precisely this. When your output is something like research data, or even just something that generally takes a long human time to complete (hours vs Microseconds) it makes a lot of sense to version and keep outputs, at least on major "versions".

But would you version it by storing it as output in an ipnyb file where it is overwritten if you rerun that cell? I would store the data in a versioned database or as separate data files in the repository (possibly stored in git-lfs). And I would store results of the analysis as data files / image files / whatever else, NOT as ephemereal outputs in an ipynb file. But I am pretty far down the “ipynb files are for local use only” path.

Yeah if your analysis to takes hours to run, you should really split up the number crunching code and result analysis / visualization. Not only does it make version control of the code easier, you can save the output in an organized labeled manner (time-stamped, etc.) and, if you lose power or the kernel crashes, you don’t need to rerun the lengthy analysis if you want to make a change further down the pipeline.

It was an extreme example to drive home the point that one is "human scale time" and one is "computer scale time", people are reading far far too much into my choice of hours specifically there.

Simply write the outputs to a formatted file and keep it separately?

However, I wouldn't then use version control software like Git for versioning analysis objects, as it is designed for text file source control and diffs.

(How one does a diff of a data object look like? If there is a natural text format to save it in, it still is usually quite messy, and Git doesn't really like Gb sized csvs.)

My preferred workflow is to version the source files in Git and store the associated data objects in a separate archive directory with meaningful name and the hash of commit of generating code as metadata attribute.

Now if you had a version control "IDE" software that would render changes in figures and other blobs nicely, then it would make sense to build a workflow around it.

What’s your solution for hosting the archive of the data if you want to share it? That’s a weakness in my workflow.

I use nbdime and have integrated it into my git diff so I see graphical diffs. Github renders notebooks with their output intact. They recently announced [rich notebook diffs](https://github.blog/changelog/2023-03-01-feature-preview-ric...) as well.

I think it depends a lot on what your git repository is.

If it's specifically source code for anything that's intended to run, then avoiding including the outputs is a smart move. But then, if that's the case, there's a good chance you'd just be committing a .py file.

I like notebooks because they include output alongisde input. For example, Peter Norvig's Pytudes are all brilliant, quick notebooks that solve a particular puzzle[0]. The code itself might not be that interesting to run (unless you really want to confirm his strategy for wordle checks out) but reading through the notebooks makes for a great experience of simultaneously understanding his thought process, and seeing the solution.

I do a bunch of generative art stuff and have recently been experimenting with using notebooks as quick sketches[1]. I really like the workflow and end up with something like a journal that isn't necessarily intended to be ran repeatedly, but read over, where I can see the visual output created, as well as the method for it.

[0] Norvig's extremely cool pytudes, wordle example: https://github.com/norvig/pytudes/blob/main/ipynb/Wordle.ipy... [1] My not anywhere near as cool as Norvig's pytudes example: https://github.com/benrutter/jupyter-sketches

You don't lose the outputs, they just aren't committed into Git. So for each new clone, you'd need to regenerate the outputs, but on a single clone, the outputs exist and are persistent in the .ipynb form of the notebook (which is not committed). You are correct that the .py version of the notebook is exactly what Git ends up tracking, with the .ipynb being essentially a build product.

(Note that the jupytext paradigm does assume that the outputs can always be recomputed as a function of the inputs. I consider that a best practice, but some might disagree.)

How do you capture things like charts or tables produced from long-running notebooks? Do you have a separate system to keep track of these? I prize notebooks with outputs in our data science repo since I can see the results of our analyses years later without having to re-run the notebook. In some cases, the notebooks don't even run anymore since our environment has moved on, but I can still see the graphs and read the text that was associated with that analysis.

Some people use the outputs like documentation, since github renders the notebook contents nicely in the browser. I agree it is not the best practice in many situations, I like using it on occasion though.

Another alternative if you want the outputs is to use nbconvert to convert the output to markdown, https://andrewpwheeler.com/2021/09/06/using-jupyter-notebook...

If you want to store (e.g. base64-encoded) outputs in Markdown, you're going to have to scroll past a lot of data to get to the next input cell in a notebook.

Jupyter notebooks store which Jupyter kernel they were run with to generate the outputs.

nbformat (.ipynb with inlined base64 outputs) isn't a sufficient package format: https://github.com/jupyter/enhancement-proposals/pull/103#is...

Papermill is one tool for running Jupyter notebooks as reports; with the date in the filename. https://papermill.readthedocs.io/en/latest/

Most of the time I agree, but if you want to e.g present a tutorial as a webpage, having an ipynb with both inputs and outputs becomes a feature. You can even bundle into docs e.g. https://moj-analytical-services.github.io/splink/demos/02_Ex...

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