JupyterLite 0.6 is now available!

Coming with exciting new features:

Interactive input() support (useful for teaching Python)
New REPL options
Better kernel status and logs
Easier browser data management
️ Improved multi-tab support and storage isolation

Check out the blog post and walkthrough video:

https://blog.jupyter.org/jupyterlite-0-6-0-is-released-b4bc69bfc8f4
https://www.youtube.com/watch?v=jkQ9ORG5qAM

Thanks @QuantStack, Bloomberg, CourseKata and all the contributors!

Oh dear, I'm still working on erosion. For quick experimentation, I've even broken out #Python, #Jupyter, #numpy and #matplotlib.

Here's a neat picture showing that something is working a little bit, although I think usually rivers are lower than the surrounding terrain, not higher.

Where will this end?

if #jupyter and #obsidian merged and I could seemlessly execute some math and function calls in obsidian's UI, that would be pretty cool...

Utilisateur longue date de #jupyter (pour mes cours, mes conf, ma recherche, etc...) je viens de découvrir #marimo et je dois dire que je suis bluffé.

- plein de protection pour ne pas pouvoir faire des notebook "perdus" qu'on ne peut plus faire tourner
- "pur python" juste un package python
- pousse à faire des fonctions (c'est bon ça !)
- notebook compatible avec le suivit de version
- basculer rapidement de l'édition à l'utilisation, faire des app standalone, ...
- la souplesse de l'environnement
- bonne doc / bon tutorial
- plein de widget bien pensés
- intégration de mes outils préférés
- fun (mais ça, ça s'estompe vite...)

bref je pense m'en servir de plus en plus...

( pub gratuite ! )

@marimo_io

After disrupting the notebook world, #marimo is expanding to markdown with a new #Quarto extension (WIP)!

https://marimo-team.github.io/quarto-marimo/

What's marimo? Reactive #jupyter -like notebooks that automatically run cells when code changes. marimo islands is making this tech portable everywhere

At the #AustralianPlantPhenomicsNetwork (#APPN), we're just concluding a project (Multiscalar Crop Characterisation Network) we've run with support from the #AustralianResearchDataCommons (#ARDC) to develop or adopt #Python tools and pipelines for simpler construction of geospatial data cubes from disparate sources (GeoTIFF, shapefiles, CSV data with point measurements, etc.). "Simpler" may only be relative, and others understand how to do this better, but I'm pleased with the results.

We're using STAC catalogues to drive an ODC-based engine for constructing xarray datacubes. The main tweaks have been in handling non-raster formats more smoothly.

As APPN goes forward, we expect to generate STAC metadata for pretty much any data objects that derive from observations with coordinates (UAV images, orthomosaics, point clouds, plot observations and measurements, etc.) and want to make it as easy as possible to plug and play with arbitrary sets of these and with relevant environment and climate data from other sources.

Three repositories:

stac-generator - configuration-driven generation of STAC catalogue records - https://github.com/aus-plant-phenomics-network/stac-generator

mccn-engine - loading and saving data cubes - https://github.com/aus-plant-phenomics-network/mccn-engine

mccn-case-studies - six #Jupyter notebooks that do semi-meaningful things with different data samples - https://github.com/aus-plant-phenomics-network/mccn-case-studies

All case studies also generate #RO-Crate packages, because we are heavily into adopting it (and #JSON-LD, schema.org, etc.) everywhere to contextualise our data to make it as #FAIR as possible.

GeoJupyter adding integration with QGIS to reduce analysis friction between tools.

https://geojupyter.org/blog/20250410-community-insight-gis-bounce/

I just killed our last #JupyterHub #GKE Project in my $DAYJOB. Our #Jupyter compute environments are now all fully on prem.

En algún momento tengo que dedicarle unas horas a utilizar #Marimo y probarlo en el blog estático que tengo en #codeberg. Parece mucho más sencillo y efectivo que con #Jupyter

https://www.youtube.com/watch?v=W8nTSmZnQBs

#Pikchr (pikchr.org) is a great little piece of software from the SQLite folks. It parses a little language for describing diagrams with boxes and lines and things, and puts out SVG.

#OrgMode (orgmode.org) has, among many other things, a way you can make code notebooks, #OrgBabel. Like #Jupyter, but less webby, and inside #Emacs, and supporting many languages - even multiple in the same document - thence its name.

Thanks to the ob-pikchr package by @SReyCoyrehourcq, Pikchr is one of the languages you can just write in the middle of your document this way.

Pikchr supports #darkmode, and I've just made a pull request that gets ob-pikchr in on the dark-mode game.

https://github.com/reyman/ob-pikchr/pull/1

Many thanks to Sebastien for the help ob-pikchr has provided in diagramming my thoughts! You go use it too!

I've created a Jupyter Lite version of the #GLAMWorkbench's 'getting started' notebook. It shows you how Jupyter notebooks work, with live examples that download data from the NMA collection API. https://glam-workbench.net/getting-started-jlite/lab/index.html?path=using_jupyter_notebooks.ipynb

Jupyter Lite runs in your browser, so there's no waiting for Binder or any cloud service to start up. #GLAM #jupyter

Pete Metcalfe shows you how to create a Julia graphical user interface, micro-web server, and charting apps that communicate with Raspberry Pi hardware
https://www.makerspace-online.com/julia-programming-language-on-a-raspberry-pi/?utm_source=mms
#Julia #languages #RaspberryPi #hardware #SBC #programming #DataScience #AI #Jupyter #Python #GUI

I am really looking forward to a time when scientific data analysis is less of a constant fuckaround and fight with technical bullshit. I'd *really* like

- #netCDF natively supporting complex numbers
- #Python #xarray and #pandas to natively support physical units (#pint is great on its own but the integrations leave a LOT to be desired)
- #Jupyter notebooks to suck less (crashes, glitches, widget plots not saved statically, an effing BUILTIN formatter, etc.)
- proper data pipeline systems
...

adding #hydra in #jupyter #notebooks

"#Geospatial #Python - Full Course for Beginners with #Geopandas"

https://www.youtube.com/watch?v=0mWgVVH_dos

#python is an interpreted language. The Python interpreter runs a program by executing one statement at a time. The standard interactive Python interpreter can be invoked on the command line with the python command

data analysis or scientific computing make use of IPython, an enhanced Python interpreter, or #jupyter notebooks, web-based code notebooks originally created within the IPython project.

Data analysis in Jupyter notebooks with... TypeScript?!
using `fetch` and other web standards
fast dataframes with nodejs-polars
easy charts with @observablehq
rich interactive UIs with JavaScript

Learn more in this detailed walkthrough
https://deno.com/blog/exploring-art-with-typescript-and-jupyter

Pierre-Antoine Bouttier & moi-même parlerons de #Jupyter au Café #Guix du jour.

https://hpc.guix.info/events/2024-2025/caf%C3%A9-guix/
aujourd’hui 25 mars, 13h