panel VS more-itertools

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.

I'd bet that reading through more-itertools would be a good exercise.

check out the code examples in itertools and the source code of more-itertools if you want to see cases where next and iter get used by themselves.

from itertools import islice # Copied from the more-itertools library (MIT license) # https://github.com/more-itertools/more-itertools def batched(iterable, n): "Batch data into lists of length n. The last batch may be shorter." # batched('ABCDEFG', 3) --> ABC DEF G if n < 1: raise ValueError('n must be at least one') it = iter(iterable) while (batch := list(islice(it, n))): yield batch

more_itertools.chunked

I've had a tab open to more-itertools on github for weeks; maybe I should go read it...

The popular more-itertools library implements (among many others) a chunked method which yields lists of size n from an iterator. There is also the ichunked method which yields iterators of size n instead of lists.

There's a function called consecutive_groups in the more-itertools library that will do this for you efficiently.

Here are some that might answer your question: - algorithms is a library which contains many of the most useful algorithms for sorting, searching, working with trees, math algorithms like factorials, prime finders and many more - data classes to save you the trouble of writing everytime special methods in a class like init, repr, set, get - box allows the use of dot on dictionaries to access the keys - more-itertools for more routines to operate on iterables than those itertools provide.