starboard-notebook
sqliteviz
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starboard-notebook | sqliteviz | |
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10 | 10 | |
1,175 | 1,856 | |
- | - | |
3.8 | 7.7 | |
about 2 months ago | 4 months ago | |
TypeScript | JavaScript | |
Mozilla Public License 2.0 | Apache License 2.0 |
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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...
[1]: https://github.com/mwouts/jupytext
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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.
https://github.com/jtpio/jupyterlite
https://github.com/gzuidhof/starboard-notebook
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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
[2]: https://jupyterlite.readthedocs.io/en/latest/
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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.
[1] https://github.com/gzuidhof/starboard-notebook
[2] https://starboard.gg
- Brython: Python in the Browser
- Ask HN: What personal tools are you the most proud of making?
sqliteviz
- SQLite Db Admin in the Browser
- Sqlite3 Utility on the Browser
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Best apps for transitioning from Spreadsheets to SQLite?
Regarding visualization tools, sqliteviz has proven to be the best I've found so far. Their web app runs locally but has some trackers, so I run it locally via a simple, static HTTP server. Falcon and Redash seem like overkill for my needs.
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Ask HN: What do you use for basic data analysis, visuals, and graphing?
If I'm not trying to build a very specific graph or chart, and just exploring data I usually use either Rawgraphs or Sqliteviz. Rawgraphs is nice if you just want to swap visualizations out with smaller data as is, sqliteviz seems to handle much larger datasets and let's you use SQL if you want to change the resultset. Both seem to keep data local too and I know sqliteviz works offline, rawgraphs might too.
https://www.rawgraphs.io/
https://sqliteviz.com/
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Awesome SQLite
sqliteviz - Instant offline SQL-powered data visualisation in your browser
- A fast SQLite PWA notebook for CSV files
- A stab at pivot/BI visualization in SQL-based offline-first PWA
- Sqliteviz is a single-page offline-first web app for fully client-side visualisation of SQLite databases or CSV files
- Offline-First PWA for Plotly Visualization of CSV via SQLite
What are some alternatives?
jupyterlite - Wasm powered Jupyter running in the browser 💡
dtale - Visualizer for pandas data structures
TiddlyWiki - A self-contained JavaScript wiki for the browser, Node.js, AWS Lambda etc.
sqlite-utils - Python CLI utility and library for manipulating SQLite databases
unofficial-observablehq-compiler - An unofficial compiler for Observable notebook syntax
Tablesaw - Java dataframe and visualization library
userscript-github-repository-categories - Categorize GitHub repositories by matching repository names with regular expressions
falcon - Free, open-source SQL client for Windows and Mac 🦅
hal9ai - Hal9 — Data apps powered by code and LLMs [Moved to: https://github.com/hal9ai/hal9]
sqlite-plus - The ultimate set of SQLite extensions
dev - Development repository for the CodeMirror editor project
chorus - Clone Hero-friendly Organized Repository of User-provided Songs