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Top 23 Python Jupyter Projects
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BTW, my inspiration was https://github.com/tqdm/tqdm library for python and any contribution is welcome to add similar functionality.
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Project mention: Top 10 growing data visualization libraries in Python in 2023 | dev.to | 2023-10-11
Github: https://github.com/bokeh/bokeh
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ipython
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
If you’re already using ipython, this isn’t a problem because you’ll already need to download most of these dependencies anyway. But if you’re not using ipython… you’ll still need to download those dependencies.
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ydata-profiling
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Project mention: Coding Wonderland: Contribute to YData Profiling and YData Synthetic in this Advent of Code | dev.to | 2023-12-05Send us your North ⭐️: "On the first day of Christmas, my true contributor gave to me..." a star in my GitHub tree! 🎵 If you love these projects too, star ydata-profiling or ydata-synthetic and let your friends know why you love it so much!
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See https://github.com/jupyter/docker-stacks
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There is Papermill ... https://github.com/nteract/papermill
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InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
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Project mention: mercury VS solara - a user suggested alternative | libhunt.com/r/mercury | 2023-10-13
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Project mention: Ask HN: Fastest way to turn a Jupyter notebook into a website these days? | news.ycombinator.com | 2023-04-03
your task is very very broad
you mention you don't want to deal with AWS, if it's because of ad-hoc installation concerns and nothing else you can just run your notebooks in ready-made solutions like Google Colab, or Jupyter-book in Github ( https://github.com/executablebooks/jupyter-book ))
that would cover a lot of use cases right away without next to no learning curve
If you don't want to deal with AWS or similar, in that case:
- if it's a static notebook then you can obviously render it and serve the web content (might seem obvious but needs to be considered)
- if it's dynamic but has light hardware requirements, you can try jupyterlite which runs in the browser and should do a pyodine (webassembly CPython kernel) can do: https://jupyterlite.readthedocs.io/en/latest/try/lab/
- otherwise, you can try exposing a dockerised jupyter env ( as in https://github.com/MKAbuMattar/dockerized-jupyter-notebook/b... ) or even better a nixified one ( https://github.com/tweag/jupyenv )
there might be other approaches I'm missing, but I think that's pretty much it that doesn't entail some proprietary solution or an ad-hoc installation as you've been doing
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If you're not concerned about self-hosting, WandB is one of the more fully featured training monitoring tools (I've used it in the past without any issues but the lack of data and training privacy and lack of self-hosting possibilities makes it a hard no for anything that isn't scholastic). Polyaxon is an alternative but rewriting all your variable logging to conform to their requirements makes it very difficult to switch to it in the middle of a project so you have to commit to it from the get-go.
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Project mention: panel VS solara - a user suggested alternative | libhunt.com/r/holoviz/panel | 2023-10-13
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Project mention: Show HN: JupySQL – a SQL client for Jupyter (ipython-SQL successor) | news.ycombinator.com | 2023-12-06
- One-click sharing powered by Ploomber Cloud: https://ploomber.io
Documentation: https://jupysql.ploomber.io
Note that JupySQL is a fork of ipython-sql; which is no longer actively developed. Catherine, ipython-sql's creator, was kind enough to pass the project to us (check out ipython-sql's README).
We'd love to learn what you think and what features we can ship for JupySQL to be the best SQL client! Please let us know in the comments!
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geemap
A Python package for interactive geospaital analysis and visualization with Google Earth Engine.
Project mention: I'm a senior in my CS major and it's incredible I didn't hear about GIS projects until now. Glad to be here. | /r/gis | 2023-05-22Try out Google Earth Engine and browse through it's catalogue to get a feel for what's available. GEE allows you to work with global datasets and immediately see a preview of the results (there's also geemap if you prefer doing this from a Python notebook instead of the online JS editor)
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leafmap
A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment
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Project mention: Best open-source & local alternatives to GitHub Copilot for data science notebooks | /r/LocalLLaMA | 2023-10-29
- Jupyter AI
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Project mention: [P] Quick and easy assessment of table dataset predictability | /r/MachineLearning | 2023-03-30
I see you've posted a GitHub link to a Jupyter Notebook! GitHub doesn't render large Jupyter Notebooks, so just in case, here is an nbviewer link to the notebook:
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3. So you do want to do code-gen, does it make sense to do it in a chat interface, or can we do better?
As a Figma user, I'd answer these in the following way:
> Why is it necessary to generate code in the first place?
Because mockups aren't your production website, and your production website is written in code. But maybe this is just for now?
I'm sure some high-up PM at Figma has this as their goal - mockup the website in Figma, it generates the code for a website (you don't see this code!), and then you can click deploy _so easily_. Who wants to bet that hosting services like Vercel etc reach out to Figma once a week to try and pitch them...
In the meantime, while we have websites that don't fit neatly inside Figma constraints, while developers are easier to hire than good designers (in my experience), while no-code tools are continually thought of as limiting and a bad long-term solution -- Figma code export is good.
> Why is just writing the code by the hand not the best solution?
For the majority of us full-stack devs who have written >0 CSS but are less than masters, I'll leave this as self-evident.
> So you do want to do code-gen, does it make sense to do it in a chat interface, or can we do better?
In the case of Figma, if they were a new startup with no existing product and they were trying to "automation UI creation" -- v1 of their interface probably would be a "describe your website" and then we'll generate the code for it.
This would probably suck. What if you wanted to easily tweak the output? What if you had trouble describing what you wanted, but you could draw it (ok, OpenAI vision might help on this one)? What if you had experience with existing design tools you could use to augment the AI. A chat interface is not the best interface for design work.
ChatGPT-style code-generation is like v0.1. Github Copilot is an example of next step - it's not just a chat interface, it's something a bit more integrated into an environment that make sense in the context of the work you're doing. For design work, a canvas (literally! [2]) like Figma is well-suited as an environment for code-gen that can augment (and maybe one day replace) the programmers working on frontend. For tabular data work, we think a spreadsheet is the interface where users want to be, and the interface it makes sense to bring code-gen to.
Any thoughts appreciated!
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pygraphistry
PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer
We regular use pygraphistry to generate /import => viz 100k+ entity embeddings on mobile fine: https://github.com/graphistry/pygraphistry
More fun, in umap mode, by default, it also shows the top-n similarity edges between each entity, so you get an interactive graph you can recluster, vs just the 2d scatter plot
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Project mention: ChromeOS is Linux with Google’s desktop environment | news.ycombinator.com | 2023-10-05
For students, unless there are allocated server resources with network access, it SHOULD/MUST scale down to one local offline ARM64 node (because school districts haven't afforded containers on a managed k8s cloud for students at scale fwiu, though universities do with e.g. JupyterHub and BinderHub [4] and Colab).
For Chromebook sysadmins, Instructors, and Students learning about how {Linux*, ChromiumOS, Android, Git, Bash, ZSH, Python, and e.g. PyData Tools supported by NumFOCUS} are developed, for example;
When you git commit to a git branch, and then `git push` that branch to GitHub, and create a Pull Request, GitHub Actions runs the (container,command) tasks defined in the YAML files in the .github/workflows/ directory of the repo; so `git push` to a PR branch runs the CI job and the results are written back as cards in the Pull Request thread on the GitHub Project; saving to the server runs the (container,command) Actions with that revision of the git repo.
Somewhat-equivalent GitOps CI Continuous Integration workflows (without Bazel or Blaze or gtest or gn, or GitHub Enterprise or GitHub Free due to the kids' intererests) that might be supported at least in analogue by Education and Chromebooks: k8s with podman-desktop in a VM, Gitea Actions (nektos/act; like Github Actions), devpod
devpod: https://github.com/loft-sh/devpod :
> Codespaces but open-source, client-only and unopinionated: Works with any IDE and lets you use any cloud, kubernetes or just localhost docker. (with devcontainer.json, like Github Codespaces)
devcontainer.json is supported by a number of tools; e.g. VScode, IntelliJ,: https://containers.dev/supporting
repo2docker has buildpacks (like Heroku and Google AppEngine).
repo2docker buildpacks should probably work with devcontainer.json too?
repo2docker docs > Usage > "REES: Reproducible Execution Environment" describes what all repo2docker will build a container from: https://repo2docker.readthedocs.io/en/latest/usage.html
jupyterhub/repo2docker builds a Dockerfile (Containerfile) from git repo (or a Figshare/Zenodo DOI) that minimally has at least an /environment.yml and /example.py (and probably also at least a /README.md to start with), and installs a current, updated version of jupyter notebook along with whatever's in e.g. /environment.yml per the REES spec. [1][2][3]
[1] repo2docker/buildpacks/base.py: https://github.com/jupyterhub/repo2docker/blob/main/repo2doc...
[2] "Make base_image configurable" https://github.com/jupyterhub/repo2docker/commit/20b08152578...
[3] repo2docker/buildpacks/conda/environment.py-3.11.yml:
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Project mention: We wrote the OpenAI Wanderlust app in pure Python using Solara | /r/Python | 2023-11-11
We (the authors of the Solara web app framework) got inspired by the OpenAI keynote Wanderlust app they demoed.
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SaaSHub
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2023-12-09.
Python Jupyter related posts
- Show HN: JupySQL – a SQL client for Jupyter (ipython-SQL successor)
- We wrote the OpenAI Wanderlust app in pure Python using Solara
- We wrote the OpenAI Wanderlust app in pure Python using Solara
- Best open-source & local alternatives to GitHub Copilot for data science notebooks
- The GitHub Black Market That Helps Coders Cheat the Popularity Contest
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panel VS solara - a user suggested alternative
2 projects | 13 Oct 2023
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mercury VS solara - a user suggested alternative
2 projects | 13 Oct 2023
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A note from our sponsor - Onboard AI
getonboard.dev | 11 Dec 2023
Index
What are some of the best open-source Jupyter projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | tqdm | 26,436 |
| 2 | dash | 19,750 |
| 3 | bokeh | 18,381 |
| 4 | ipython | 16,020 |
| 5 | ydata-profiling | 11,539 |
| 6 | docker-stacks | 7,580 |
| 7 | papermill | 5,460 |
| 8 | voila | 5,005 |
| 9 | lux | 4,809 |
| 10 | mercury | 3,626 |
| 11 | jupyter-book | 3,571 |
| 12 | polyaxon | 3,420 |
| 13 | panel | 3,323 |
| 14 | ploomber | 3,271 |
| 15 | geemap | 3,003 |
| 16 | leafmap | 2,637 |
| 17 | jupyter-ai | 2,180 |
| 18 | nbviewer | 2,132 |
| 19 | mito | 2,075 |
| 20 | pygraphistry | 1,940 |
| 21 | repo2docker | 1,545 |
| 22 | zero-to-jupyterhub-k8s | 1,417 |
| 23 | Solara | 1,327 |
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