panel VS jupyterlite

panel – data exploration & web app framework for Python

https://panel.holoviz.org/ It's a web app framework for Python similar to what Dash does for plotly. It plays nicely with bokeh visuals and I think the front-end is built using bokeh css elements.

I'm following the Panel FastAPI example here: https://github.com/holoviz/panel/blob/main/examples/apps/fastApi/main.py

If you want to build Python dashboards, look at the solara (react-style lib, https://solara.dev/) and panel (https://panel.holoviz.org/).

panel

My suggestion is https://panel.holoviz.org/

Fully open sourced, makes it easy to make reactive apps with small changes, can even configured as a graphical REPL.

I am doing something like this in a [panel](https://panel.holoviz.org/) dashboard, which I am currently converting to nicegui. Maybe I can provide an example in some days.

Ill have to check it out and see how it compares to voilà and holoviz panel. What I like about Holoviz panel is you can create a data web app from code that resides in a notebook or create a completely standalone app from just plain py scripts, and it supports many different visualization backends. I have found it to be the more flexible and generalizable data web app framework among the others I have come across (like Voilà, Dash, Plotly, and Streamlit).

Like the previous three alternatives, Panel is an open-source Python library for creating interactive dashboard web apps. Panel is extremely flexible, allowing you to use any plotting library you like. Like Gradio but unlike Streamlit, you can use Panel in Jupyter notebooks. Panel dashboards can also be deployed as standalone web apps, but like Plotly Dash, you'll need to set up a server to deploy it yourself.

The JupyterLite Python-compiled-to-WASM build has NumPy, SciPy, matplotlib, and SymPy installed; so you can do computer algebra with SymPy in a browser tab.

https://JupyterLite.rtfd.io/

https://github.com/jupyterlite/jupyterlite/tree/main/py/jupy... :

> Initial support for interactive visualization libraries such as: altair, bqplot, ipywidgets, matplotlib, and plotly

https://jupyterlite.readthedocs.io/en/latest/howto/extension... :

> We recommend checking out how to create a server extension first

From https://github.com/jupyterlite/jupyterlite/issues/237#issuec... re: 'micropip':

  %pip install $@

https://github.com/emscripten-forge/recipes/tree/main/recipe...

Re: emscripten fs implementations: https://github.com/emscripten-core/emscripten/issues/15041#i... https://github.com/jupyterlite/jupyterlite/issues/315

Agreed that would be a killer feature. Unzip this package and get a functional Python + Jupyter + scientific (numpy, pandas, scipy, matplotlib) environment.

I have been on-and-off teaching some people Python and the initial setup on-ramp is horrible. Ok, so install Python, now ignore-this-for-now-complications: create a "virtualenv", use this thing called "pip", install these half-dozen things to get a basic notebook (Jupyter + scipy things), install these other half-dozen quality of life things, you should probably also have "conda" for the future, etc. That's a lot of nonsense for someone I am trying to show an alternative to Excel.

My shortcut, "You want to try Python?" approach has been to start with JupyterLite[0] where I can immediately get people coding and delay that pain.

[0] https://github.com/jupyterlite/jupyterlite

JupyterLite: a JupyterLab distribution that runs in the browser\ (45 comments)