Jupyter Blog

When we shipped Jupyter support in Deno, `nodejs-polars` was one of the cornerstone library for data science we supported. https://blog.jupyter.org/bringing-modern-javascript-to-the-jupyter-not… I'm not personally a Data Science guy, but considering how early the JS/Jupyter ecosystem is, it was surprisingly quick to get pola.rs-based analysis up and running in TypeScript. The JS bindings certainly need a bit of love, but hopefully now that it's more accessible we'll see some iteration on it.

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.

re: sql notebook, this came up a few months ago and worked great when I played around with it: https://blog.jupyter.org/an-sql-solution-for-jupyter-ef4a00a0d925. It's just a different kernel you can install to an existing jupyter instance.

Worth noting that Jupyter themselves announced[1] 'nbterm'[2] recently, which seems like an attempt at the same thing but it seems very very immature compared to Euporie. I don't understand why they announced nbterm at such an early stage tbh. When I tried it it was basically non-working. [1] https://blog.jupyter.org/nbterm-jupyter-notebooks-in-the-terminal-6a2b… [2] https://github.com/davidbrochart/nbterm Edit: cool to see another project supporting kitty terminal's graphics protocol. kitty is a great terminal and has a lot of good ideas. Also, nice to see Euporie has a section on related projects that mentions nbterm.

Yes, setup jupyter magics to execute SQL and it becomes MUCH more useful for data analysis in a notebook. There's a great extension that lets you just write and run SQL queries right in cells: https://blog.jupyter.org/a-jupyter-kernel-for-sqlite-9549c5dcf551 Hook it up to an in memory or on disk SQLite instance and you can kick pandas to the curb.

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

https://www.spyder-ide.org/ It's worth watching developments in Jupyter QtConsole [https://blog.jupyter.org/jupyter-qtconsole-4-4-81f2eaad5706].

I would recommend to start with C++ integration with Jupyter notebook https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b…

Please read the blogpost (http://blog.jupyter.org/2017/04/19/release-of-ipython-6-0/) we went to great length to make sure that `pip install ipython` just install the right IPython. `pip install ipython` will just install 5.x on Python 2. So you don't have to worry about which versions are incompatible.

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#ju… [2] https://blog.jupyter.org/jupyterlab-desktop-app-now-available-b8b661b1…

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-pipel… We'd love to hear from your experience! Please send me an email to [email protected]

Cool! Emscripten-forge also recently got a R distribution that runs natively on the browser: https://blog.jupyter.org/r-in-the-browser-announcing-our-webassembly-d…

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-fe9b… 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.

I just recently found out that there is Deno kernel for Jupyter notebook https://blog.jupyter.org/bringing-modern-javascript-to-the-jupyter-not…

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.

Jupyter in WASM is a thing too: https://blog.jupyter.org/jupyterlite-jupyter-%EF%B8%8F-webassembly-%EF…

It always amazes me how important and useful open source software is often supported by small team doing heroic efforts. I found it very interesting to learn about QuantStack and what they do to support Jupyter data science communities. I'm personally keeping a close eye on Jupyter Lite [1]. which uses WASM to run python and popular data science libraries (e.g. numpy, pandas) completely in the browser. [1] https://blog.jupyter.org/jupyterlite-jupyter-%EF%B8%8F-webassembly-%EF…

> It's too hard to get access to GIS software It took me a whole 5 seconds to google [1] up. As far as I can remember Anaconda can be installed without admin permissions on any PC and pulling the needed packages is a matter of minutes. Or a Tableau Online account with Tableau Creator desktop app having similar GIS functionality costs a whole 70 bucks a month, OMG. The public sector seems to excel in making up "blockers" to justify why shit doesn't get done. [1]: https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52…

nbterm [0] was recently released. You can also use Jupyter as a command line interface through Jupyter Console. [0]: https://blog.jupyter.org/nbterm-jupyter-notebooks-in-the-terminal-6a2b…

Plug that if you're a Jupyter user to chime in on the survey at https://www.surveymonkey.com/r/LCB7GBF. Background: https://blog.jupyter.org/survey-jupyterlab-and-beyond-88c7fbd27a79

https://twitter.com/jeremyphoward/status/1321626078985687042 https://blog.jupyter.org/jeremy-howard-8dace7b4a34a For people like myself needing backstory.

Yeah, I wonder how it would look like. https://root.cern.ch/cling https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b…

There is already a tool to make usual notebooks to standalone apps with all needed source data inside - Voilà https://blog.jupyter.org/a-gallery-of-voilà-examples-a2ce7ef99130

side note: I hope everyone is aware of Jupyter Lab, the successor of Jupyter Notebooks -- it's been basically version 1.0 for over a year: https://blog.jupyter.org/jupyterlab-is-ready-for-users-5a6f039b8906

They can base the kernel on top of these C++ libraries: - https://blog.jupyter.org/a-new-python-kernel-for-jupyter-fcdf211e30a8 - https://github.com/QuantStack/xeus One day, hopefully, language-independent part of Jupyter will be rewritten completely in Rust (backend) and WebAssembly (frontend), to achieve the portability, better safety guarantees, and speed.

https://www.moore.org/, More information available on this link https://blog.jupyter.org/mybinder-org-serves-two-million-launches-7543…

Too many are using Medium. This recent HN posted link for example: https://blog.jupyter.org/jupyterlab-is-ready-for-users-5a6f039b8906 My protest is to talk about this issue in this HN thread, and to just install the extension so that the sites start to "just work".

Check out our blog post on the C++ backend to Jupyter widgets: https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b…

I haven't heard of bpython before, but I believe Jupyter/IPython is quite popular, and now supports syntax highlighting too [1]. [1] http://blog.jupyter.org/2016/07/08/ipython-5-0-released/

Agree. I think the plain ipython shell can go a long way now. It will pop up a graphic, and you can access the surrounding shell. In theory you can just live in ipython; I've tried it but I stumbled too much. Next on the spectrum would be the QT version of ipython. And then of course ipython notebook/jupyter. What really looks promising is the Jupyter Lab, currently in alpha. http://blog.jupyter.org/2016/07/14/jupyter-lab-alpha/ https://pypi.python.org/pypi/jupyterlab