Why no Mathematica?
It's not even free software.
Developers need to pay bills somehow.
What’s wrong with computational notebooks?
181–190 of 223 posts
Re: What’s wrong with computational notebooks?
#182Earlier quoted context omitted.
Superb talk! It's worth noting that a lot of the issues he brings up, ultimately stem from the format in which Jupyter notebooks are stored. R notebooks, with their plain-text stored format as well as code-chunk parameters, solve some, but not all, of these problems.
https://github.com/mwouts/jupytext doesn’t solve the state management testing or tooling issues though, but commits are slightly less awful.
Yea, when I first found that extension, I was pretty excited about it. But it ultimately is not a first class citizen in the way it is for R notebooks, so I simply don't feel as comfortable using this as I might otherwise have been.
Re: What’s wrong with computational notebooks?
#183I want a notebook where causality can only flow forward through the cells. I hate notebook time-loops where a variable from a deleted cell can still be in scope. 1. Checkpoint the interpreter state after every cell execution. 2. If I edit a cell, roll back to the previous checkpoint and let execution follow from there. I can't tell you how many times I've seen accidental persistence of dead state waste hours of peopl…
My problem with notebooks is that I feel like the natural mental model for them is a spreadsheet mental model, not a REPL mental model. Under that assumption, changing a calculation in the middle means that all of the cells that depend on that calculation would be updated, but instead you need to go and manually re-run the cells after it that depend on that calculation (or re-run the entire notebook) to see the effec…
This falls apart when computation is a factor, though. You can't recompute the whole notebook on every commit when there are 30 cells that each take 2-8 seconds to complete.
Re: What’s wrong with computational notebooks?
#184Co-author of the study here. Let me know if you have any questions or how you overcome some of the problems we identified!
I just wanted to say thank you. Many of the points in your study strikes a nerve. Part of my responsibility at my last job was to introduce good software engineering practices. What happens? The data scientists go rogue and start running notebooks left and right. How do they productionize their work? Well, they don't. They were academics. All they know is that the models ran fine in their notebooks on their laptops.…
>to introduce good software engineering practices. What happens? The data scientists go rogue and start running notebooks left and right. How do they productionize their work? Well, they don't. They were academics.
My background is programming (instead of data analysis & modeling) so I'm sympathetic to your idealistic "software engineering" view... but I'm also sympathetic to the academics' side as explained by Yihui Xie's blog post:
https://yihui.org/en/2018/09/notebook-war/
He's convinced me that criticizing non-programmers for using (or over-using) computational notebooks when it should be a "proper" programming language and deployment is like criticizing financial analysts over-using Excel to learn how to program VB or Python and re-write their spreadsheets into a "proper database" like Oracle or MySQL. That's just not reality. This divide between "end user tools" and "proper programmer tools" will always exist because there is no perfect tool in existence that serves the needs of both skill sets. Therefore, the programmers will always be able to say the data scientists or financial analysts are "doing it wrong".
Re: What’s wrong with computational notebooks?
#185I work at https://www.deepnote.com/ , we are trying to tackle some of the pains mentioned in the article (setup, collaboration, IDE features like auto-complete or linting). We are still early access, but if you are interested in an invite just let me know. My email is filip at deepnote dot com.
Deepnote seems quite interesting, but as a cheapskate grad student, I'm compelled to ask. If this information isn't private, what sort of business model do you use? I take it you'll have a SaaS subscription model? I see it's free to use now, but how does your company plan to make money (especially taking into account the cost of the cloud hosting Deepnote requires)?
Hey, thanks for the question.
Our goal right now is to build the most amazing data science notebook. We need a lot of feedback to get there, that's why we are keeping it free. But since the servers also cost us something, we haven't opened up Deepnote to the public just yet.
Once in GA, we know we can support students on a free tier almost indefinitely (it doesn't really cost that much) while offering more advanced features on a subscription model for teams and enterprises.
Re: What’s wrong with computational notebooks?
#186I think that all the pain points of the article are a result of not using notebooks for their purpose. In my opinion, notebooks are good for:
1. POC/MVP: Showing that what you want to do will work before making a full structure. 2. Creating PDF/HTML documents with code and output. 3. Exploratory data analysis and visualization.
I think many of the data scientists in the article go well beyond what a notebook is. A notebook is where you start, but should never be a production tool.
Re: What’s wrong with computational notebooks?
#187I used Mathematica’s notebook interface quite heavily 15-20 years ago; Jupyter’s interface is a clone of that in many ways. At the time, my workflow was to use two different notebooks for everything: foo.nb and foo-scratch.nb. I’d get things working a piece at a time in foo-scratch.nb, not caring at all how it looked, not having to worry about leaving extra output or dead ends of explorations lying around; then the r…
I make a "scratch pad" section of my notebook and work on ideas there. Then once I've pieced together a function line by line and tested it a bit I move it up to where it should be in the chronological order of the notebook. Kind of like your two notebook system but makes copying easier in Jupyter.
Re: What’s wrong with computational notebooks?
#188Why not teach data scientists how to write software effectively? Those are smart people, it’s not like using version control, writing unit tests and extracting common code into libraries is rocket science.
Re: What’s wrong with computational notebooks?
#189One idea for a pain point not mentioned: better variable persistence. If I declare a variable, then delete the cell I declared it in, the variable persists. I've had this cause issues because if I use the deleted variable by accident, it will work fine right up until a kernel restart.
Re: What’s wrong with computational notebooks?
#190Earlier quoted context omitted.
The key factor is iteration speed. If step A takes 5 minutes (and 5 minutes is a very short time) and I want to experiment on step B, then I don't want to rerun step A each time while I'm writing and running code that helps me understand what step B is going to be; I'd want that to be interactive and immediate, not have each rerun take 5 minutes. Storing/loading to disk is not a good option because all the data that…
Ah, I think I have a hugely different approach to data processing: For my work I often have a very good idea what the output should look like, and what transformations are required on the input to get there. E.g. when processing log files to generate an overview page, or (as I'm doing right now) adding a target to binutils (assembler, linker,...). (Obviously I'm not a data scientist ;-) With what you describe, intuit…
Sure, if you know what the output should look like and if it's possible to e.g. write tests to verify that it's correct, then jupyter notebooks would not be the proper tool to use.
The intended usecase of these notebooks is in scenarios where the main output is not the code and not a particular set transformed data, but knowledge gained during a 'computational exploration' of that data. With that knowledge in hand, you can then build 'productionized' code with different methodologies (possibly but not necessarily using or adapting large parts of the code in your notebook), if that's needed - and in such data analysis scenarios it often happens that it's not ever needed.
Sampling a subset of the data sometimes works. Sometimes it would alter the results substantionally and drive the exploration in a wrong direction; questioning and verifying assumptions is important, and it can be a big difference if all A's are also B or only 99% of them are.