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Top 23 Python Jupyter Projects
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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ipython
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
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ydata-profiling
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
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SaaSHub
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geemap
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
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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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pygraphistry
PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer
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yeah my code needs to use multiprocessing, which does not play nice with tqdm. thanks for the tip about positions though, that helped me search more effectively and came up with two promising comments. unmerged / require some workarounds, but might just work:
https://github.com/tqdm/tqdm/issues/1000#issuecomment-184208...
Bokeh - Interactive Web Plotting for Python.
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.
See https://github.com/jupyter/docker-stacks
Project mention: Spreadsheet errors can have disastrous consequences – yet we keep making them | news.ycombinator.com | 2024-01-25Pandas docs > Comparison with spreadsheets: https://pandas.pydata.org/docs/getting_started/comparison/co...
Pandas docs > I/O > Excel files: https://pandas.pydata.org/docs/user_guide/io.html#excel-file...
nteract/papermill: https://github.com/nteract/papermill :
> papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. [...]
> This opens up new opportunities for how notebooks can be used. For example:
> - Perhaps you have a financial report that you wish to run with different values on the first or last day of a month or at the beginning or end of the year, using parameters makes this task easier.
"The World Excel Championship is being broadcast on ESPN" (2022) https://news.ycombinator.com/item?id=32420925 :
> Computational notebook speedrun ideas:
panel – data exploration & web app framework for Python
Project mention: Ask HN: What's the best charting library for customer-facing dashboards? | news.ycombinator.com | 2024-04-29I'm build dashboards in Jupyter Lab. My plotting libraries are Altair, matplotlib, seaborn, Plotly - all work well in notebook.
My favorite is Altair. It provides interactivity for charts, so you can move/zoom your plots and have tooltips. It is much lighter than Plotly after saving the notebook to ipynb file. Altair charts looks much better than in matplotlib. One drawback, that exporting to PDF doesn't work. To serve notebook as dashboard with code hidden, I use Mercury framework, you can check example https://runmercury.com/tutorials/vega-altair-dashboard/
disclaimer: I'm author of Mercury framework https://github.com/mljar/mercury
Project mention: I don't always use LaTeX, but when I do, I compile to HTML (2013) | news.ycombinator.com | 2024-01-25Sphinx supports ReStructuredText and Markdown.
MyST-Markdown supports MathJaX and Sphinx roles and directives. https://myst-parser.readthedocs.io/en/latest/
jupyter-book supports ReStructuredText, Jupyter Notebooks, and MyST-Markdown documents:
You can build Sphinx and Jupyter-Book projects with the ReadTheDocs container, which already has LaTeX installed: https://github.com/executablebooks/jupyter-book/issues/991
myst-templates/plain_latex_book:
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!
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)
This notebook is dedicated to a (not so) short jupyterlab/jupyter-ai unboxing so anyone can enjoy this kind of magic (and much much more):
Project mention: The Design Philosophy of Great Tables (Software Package) | news.ycombinator.com | 2024-04-042. The report you're sending out for display is _expected_ in an Excel format. The two main reasons for this are just organizational momentum, or that you want to let the receiver conduct additional ad-hoc analysis (Excel is best for this in almost every org).
The way we've sliced this problem space is by improving the interfaces that users can use to export formatting to Excel. You can see some of our (open-core) code here [2]. TL;DR: Mito gives you an interface in Jupyter that looks like a spreadsheet, where you can apply formatting like Excel (number formatting, conditional formatting, color formatting) - and then Mito automatically generates code that exports this formatting to an Excel. This is one of our more compelling enterprise features, for decision makers that work with non-expert Python programmers - getting formatting into Excel is a big hassle.
[1] https://trymito.io
[2] https://github.com/mito-ds/mito/blob/dev/mitosheet/mitosheet...
Extra fun: We find most enterprise/gov graph analytics work only requires 1-2 attributes to go along with the graph index, and those attributes often are already numeric (time, $, ...) or can be dictionary-encoded as discussed here (categorical, ID, ...)... so even 'tough' billion scale graphs are fine on 1 gpu.
Early, but that's been the basic thinking into our new GFQL system: slice into the columns you want, and then do all the in-GPU traversals you want. In our V1, we keep things dataframe-native include the in-GPU data representation, and are already working on the first extensions to support switching to more graph-native indexing for steps as needed.
Ex: https://github.com/graphistry/pygraphistry/blob/master/demos...
I really like this idea of using Python to create both the frontend and backend. Another lib doing this is https://solara.dev/ . Something I particularly like about Solara is that you can interactively build your app in a Jupyter Notebook, since behind the scenes it's using ipywidgets.
Has anyone compared Solara and Reflex and can comment on pros/cons? Are there other options in this space? Maybe https://shiny.posit.co/py/ ?
Project mention: ChromeOS is Linux with Google’s desktop environment | news.ycombinator.com | 2023-10-05For 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:
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).
Python Jupyter related posts
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PyPy v7.3.16 Release
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The Design Philosophy of Great Tables (Software Package)
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SymPy: Symbolic Mathematics in Python
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🪄 Put magic in your Notebook w/ Jupyter-AI
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Show HN: JupySQL – a SQL client for Jupyter (ipython-SQL successor)
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We wrote the OpenAI Wanderlust app in pure Python using Solara
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We wrote the OpenAI Wanderlust app in pure Python using Solara
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A note from our sponsor - SaaSHub
www.saashub.com | 10 May 2024
Index
What are some of the best open-source Jupyter projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | tqdm | 27,492 |
| 2 | dash | 20,544 |
| 3 | bokeh | 18,867 |
| 4 | ipython | 16,143 |
| 5 | ydata-profiling | 12,070 |
| 6 | docker-stacks | 7,762 |
| 7 | papermill | 5,636 |
| 8 | voila | 5,226 |
| 9 | lux | 4,921 |
| 10 | panel | 4,260 |
| 11 | mercury | 3,789 |
| 12 | jupyter-book | 3,694 |
| 13 | polyaxon | 3,486 |
| 14 | ploomber | 3,387 |
| 15 | geemap | 3,214 |
| 16 | leafmap | 2,905 |
| 17 | jupyter-ai | 2,875 |
| 18 | mito | 2,223 |
| 19 | nbviewer | 2,163 |
| 20 | pygraphistry | 2,062 |
| 21 | Solara | 1,593 |
| 22 | repo2docker | 1,581 |
| 23 | zero-to-jupyterhub-k8s | 1,475 |
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