starboard-notebook
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starboard-notebook | panel | |
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10 | 39 | |
1,175 | 4,160 | |
- | 6.3% | |
3.8 | 9.9 | |
about 1 month ago | 3 days ago | |
TypeScript | Python | |
Mozilla Public License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
starboard-notebook
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JupyterLite is a JupyterLab distribution that runs in the browser
The format is only partially invented, it follows Jupytext [0], but adds support for cell metadata. There is no obvious way to get that in fenced codeblocks, especially with the ability to spread it over multiple lines so it plays well with version control.
One more consideration is that it's not "Markdown with code blocks interspersed", one might as well use plaintext or AsciiDoc.
Of course there are tradeoffs.. I wish I had more time to work on it.
[0]: https://github.com/gzuidhof/starboard-notebook/blob/master/d...
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A fast SQLite PWA notebook for CSV files
This is really wonderful! The discussion about lay people's knowledge of sql reminded me that the Pandas API is often useful for non-sql folk. Likewise there are some projects similar to dirtylittlesql to bring Python data manipulation to the browser.
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Turns Jupyter notebooks into standalone web applications and dashboards
You could consider an in browser notebook to get your cost down to near nothing - it depends a bit on what kind of tasks your students do whether they fit in the browser (one wouldn't train a large neural network in one for instance)
There's Starboard (which I'm building, it's built specifically for the browser and can integrate into a larger app deeply) and JupyterLite (the closest you will get to JupyterLab in the browser), either can be a good choice depending on your requirements. Both use Pyodide for the Python runtime.
[1]: https://github.com/gzuidhof/starboard-notebook, demo: https://starboard.gg
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Enabling COOP/COEP without touching the server
A few examples of web-applications that have this problem are in-browser video converters using ffmpeg.wasm, a web-based notebook that supports Python and multithreaded Emscripten applications.
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I want to learn D3. I don’t want to learn Observable. Is that ok? (2019-2021)
As someone building an in-browser notebook I have a lot of opinions on notebook environments. Notebooks serve different purposes, sometimes the notebook itself is the end-goal because the author is creating an interactive tutorial or explaining a complex concept with a bunch of visualizations. Observable is a fantastic tool for that, and the kind-of-Javascript reactive programming system it is built on is a great fit for that.
Outside of that use-case, I think notebooks are great for the first 20% of the effort that gets 80% of the work done. If it turns out one also needs to do the other 80% of the effort to get the last 20%, it is time to "graduate" away from a notebook. For instance if I am participating in a Kaggle machine learning competition I may train my first models in a Jupyter notebook for quick iteration on ideas, but when I settle onto a more rigid pipeline and infra, I will move to plain Python files that I can test and collaborate on.
This "graduation" from notebook to the "production/serious" environment should be straightforward, which means there shouldn't be too much magic in the notebook without me opting into it. Documentation in my eyes is not so different, I should be able to copy the examples easily into my JS project without knowing specifics of Observable and adapt it to my problem. Saying "don't be lazy and just learn Observable", or "you must learn D3 itself properly to be able to use it anyway" is not helpful. Observable being a closed, walled garden doesn't help: not being able to author notebooks without using their closed source editor is a liability that I can totally understand makes it a non-starter for some companies and individuals.
I think it's ok to plug my own project: It's called Starboard [1] and is truly open source [2]. It's built on different principles: it's hackable, extendable, embeddable, shareable, and easy to check into git (i.e. I try to take what makes the web so great and put that in a notebook environment). You write vanilla JS/ES/Python/HTML/CSS, but you can also import your own more advanced cell types. Here's an example which actually introduces an Observable cell type [3] which is built upon the Observable runtime (which is open source) and an unofficial compiler package [4]. I would be happy for the D3 examples to be expressed in these really-close-to-vanilla JS notebooks, but I can convince the maintainers to do so.
[1]: https://starboard.gg
[2]: https://github.com/gzuidhof/starboard-notebook
[3]: https://starboard.gg/gz/open-source-observablehq-nfwK2VA
[4]: https://github.com/asg017/unofficial-observablehq-compiler
- Show HN: A simple JavaScript notebook in one file
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Pyodide: Python for the Browser
If you want to play with Pyodide in a web notebook you can try Starboard [1][2].
A sibling comment introduces JupyterLite and Brython, which are Jupyer-but-in-the-browser, whereas with Starboard I'm trying to create what Jupyter would have been if it were designed for the browser first.
As it's all static and in-browser, you can embed a notebook (or multiple) in a blog post for instance to power interactive examples. The bundle size is a lot smaller than JupyerLite for the initial load - it's more geared towards fitting into existing websites than being a complete IDE like JupyerLab.
- Brython: Python in the Browser
- Ask HN: What personal tools are you the most proud of making?
panel
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This Week In Python
panel – data exploration & web app framework for Python
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panel VS solara - a user suggested alternative
2 projects | 13 Oct 2023
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What python library you are using for interactive visualisation?(other than plotly)
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.
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FastAPI, Panel and Bokeh
I'm following the Panel FastAPI example here: https://github.com/holoviz/panel/blob/main/examples/apps/fastApi/main.py
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How to approach GIS and which language to use
If you want to build Python dashboards, look at the solara (react-style lib, https://solara.dev/) and panel (https://panel.holoviz.org/).
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Panel - A high-level app and dashboarding solution for Python
panel
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Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
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.
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Updating a page with MQTT
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.
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Mercury – Turn Python Notebooks to Web Apps
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).
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4 Streamlit Alternatives for Building Python Data Apps
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.
What are some alternatives?
jupyterlite - Wasm powered Jupyter running in the browser 💡
streamlit - Streamlit — A faster way to build and share data apps.
TiddlyWiki - A self-contained JavaScript wiki for the browser, Node.js, AWS Lambda etc.
dash - Data Apps & Dashboards for Python. No JavaScript Required.
unofficial-observablehq-compiler - An unofficial compiler for Observable notebook syntax
gradio - Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
userscript-github-repository-categories - Categorize GitHub repositories by matching repository names with regular expressions
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
hal9ai - Hal9 — Data apps powered by code and LLMs [Moved to: https://github.com/hal9ai/hal9]
appsmith - Platform to build admin panels, internal tools, and dashboards. Integrates with 25+ databases and any API.
dev - Development repository for the CodeMirror editor project