Jupyter Blog

The closest Python equivalent to RStudio is the JupyterLab Desktop app[1,2], which I highly recommend. I've entirely switched to using it for teaching, and it is a godsend, since it works the same way across platforms (win/mac/linux), installs its own Python interpreter independent of any system Python the student might have, and even comes with NumPy/SciPy/Pandas/Seaborn/statsmodels already installed, which makes it possible for me to skip the `pip ...` or `conda ...` instructions altogether.

Between the standalone desktop app, and the convenience of running JypyterLab in the cloud thanks to https://mybinder.org/ links, there is now a smooth path for beginners getting into stats/ML/data science: (1) read notebook on github or nbviewer, (2) run notebooks in the cloud via mybinder links, (3) install JupyterLab Desktop app, (4) learn to install Python+env-manager via command line. Previously, new learners were forced to jump straight to (4), but now there are logical steps along the way!

[1] https://github.com/jupyterlab/jupyterlab-desktop?tab=readme-ov-file#jupyterlab-desktop

[2] https://blog.jupyter.org/jupyterlab-desktop-app-now-available-b8b661b17e9a

We allow people to write pipeline tasks in .py files but open them as notebooks in Jupyter. So they keep the same workflow they're used to but under the hood, they're writing .py files - so they can do code reviews (jupytext handles the .py to .ipynb conversion). Also, when executing the pipeline, ploomber generates an output report for each script, so teams can use this to review any outputs generated by the code.

Finally, since the pipeline is modularized, it's easier to split the work. Some people may work in data cleaning, others in feature engineering, and they can all orchestrate the pipeline with "ploomber build".

You can read more about our approach in this guest blog post we published a few months ago on the Jupyter blog: https://blog.jupyter.org/ploomber-maintainable-and-collaborative-pipelines-in-jupyter-acb3ad2101a7

We'd love to hear from your experience! Please send me an email to eduardo@ploomber.io

You can do the VPS, and SSH thing.

As far as apps, I am not too sure what’s available, I know you can do swift playgrounds and that might have a C binding. (I checked there doesn’t seem to be a straightforward way to do C in swift playgrounds. Bonus content: https://github.com/uraimo/Awesome-Swift-Playgrounds )

What I do for most of my coding on iPad is use Google Colab. https://colab.research.google.com/

It’s a Jupyter Notebook, but you can get most things including working with C. The below link confirms C can be bound to and how.

https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b54227d92

As a matter of point, iPads aren’t really set up tool wise for programming. You can force something into this niche, but you probably want a computer for coding, because a significant portion of programming is massaging the system into doing what you need.

From the devs themselves: https://blog.jupyter.org/i-python-you-r-we-julia-baf064ca1fb6

> When we decided to rename part of the IPython project to Jupyter in 2014, we had many good reasons. Our goal was to make (Data)Science and Education better, by providing Free and Open-Source tools that can be used by everyone. The name “Jupyter” is a strong reference to Galileo, who detailed his discovery of the Moons of Jupiter in his astronomical notebooks. The name is also a play on the languages Julia, Python, and R, which are pillars of the modern scientific world. While we (Love Python), and use it for much of the architecture in Jupyter, we believe that all open-source languages have an important role in scientific and data analysis workflows. We have strived to make Jupyter a platform that treats all open-source languages as first-class citizens.

If you don’t actually care about this being a website, and you mainly care about the calculations, documentation and delivery, this really sounds like a job for a Jupyter[1] and Voila[2]. Write your calculations locally, and then present them on the web automatically.

I do understand this doesn’t meet your static site need. But if you’re in this for knowledge transfer and improving the state of the calculators you created perhaps opening up the code to those that could collaborate could be useful. Perhaps you could write the canonical library for those calculations. Keeping the presentation away from the business logic like this is a stated goal…

[1] https://jupyter.org/

[2] https://blog.jupyter.org/and-voilà-f6a2c08a4a93 has a pretty good demo that would be effectively what you need to create an interactive calculator.

1) There's Binder, which is the infrastructure for building computational environments around repos, and BinderHub, which hosts the executable code online. Binder's model, as I understand it, is to develop the infrastructure and then have universities/institutions/whatever host BinderHub: https://binderhub.readthedocs.io/en/latest/

2) One of my favorite contributions from the team is repo2docker, which turns requirements.txt or equivalent into Dockerfiles: https://repo2docker.readthedocs.io/en/latest/, integration into binder described here https://blog.jupyter.org/binder-2-0-a-tech-guide-2017-fd40515a3a84

3) Not just Jupyter notebooks, also R/Rstudio/Rshiny: https://github.com/binder-examples/r