jupyenv
datapane
jupyenv | datapane | |
---|---|---|
6 | 30 | |
599 | 1,349 | |
1.8% | 0.5% | |
3.1 | 7.3 | |
8 days ago | 7 months ago | |
Nix | Python | |
MIT License | Apache License 2.0 |
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.
jupyenv
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JupyterLab 4.0
> There aren't good boundaries between Jupyter's own Python environment, and that of your notebooks— if you have a dependency which conflicts with one of Jupyter's dependencies, then good luck.
I believe that you can use https://github.com/tweag/jupyenv for this.
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Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
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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Need help Integrating Hasktorch into my Haskell Jupyter environment using Nix
I'm new to Nix and I'm trying to set up a Jupyter notebook environment for Haskell that includes the Hasktorch package. I'm using the jupyenv project from Tweag as the foundation, and I've been able to get it working with some basic Haskell packages. However, I'm running into issues when I try to add Hasktorch to the mix.
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is nix datasci
i looked at https://nixos.org/manual/nixpkgs/stable/#python https://nix-tutorial.gitlabpages.inria.fr/nix-tutorial/index.html https://github.com/tweag/jupyterWith and i can setup jupyterwith + math-nix + flake to make torch + jupyter
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How to use Matplotlib for Haskell in IHaskell
You could look into jupyterWith. With that you can list the packages you want to use in a shell.nix file; based on this file an environment is created in which Jupyter is run. I've also had issues with using packages with regular IHaskell in the past, but jupyterWith works pretty well for me.
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Why isn't NixOS more popular
(Hopefully the Flakes version of Nix gets released soon, and JupyterWith gets the flake treatment!)
datapane
- Datapane: Build and share data reports in 100% Python
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Polars: Company Formation Announcement
If you're looking for an easy way to build an HTML report using Python, you might find Datapane (https://github.com/datapane/datapane) helpful. I'm one of the people building it! We don't support polars (yet, on the roadmap) but we do support pandas so you can convert to a pandas DataFrame and include your data and any plots, etc.
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JupyterLab 4.0
If you're interested in an easier way to create reports using Python and Plotly/Pandas, you should check out our open-source library, Datapane: https://github.com/datapane/datapane - you can create a standalone, redistributable HTML file in a few lines of Python.
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Evidence – Business Intelligence as Code
You might be interested in what we're hacking on at Datapane (I'm one of the founders): https://github.com/datapane/datapane.
You can create standalone HTML data reports from Python/Jupyter in ~3 lines of code: https://docs.datapane.com/reports/overview/
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Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
You can build web apps from Jupyter using Datapane [0]. I'm one of the founders, so let me know if I can help at all.
You can either export a static site [1] (and host on GH pages or S3), or, if you need backend logic, you can add Python functions [2] and serve on your favourite host (we use Fly).
We have specific Jupyter integration to automatically convert your notebook into an app [3].
[0] https://github.com/datapane/datapane
[1] https://docs.datapane.com/reference/reports/#datapane.proces...
[2] https://docs.datapane.com/apps/overview/
[3] https://docs.datapane.com/reports/jupyter-integration/#conve...
- Datapane – Build full-stack data apps in 100% Python
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Datapane - Build full-stack data apps in 100% Python
Our GitHub is https://github.com/datapane/datapane and you can get started here: https://docs.datapane.com/quickstart/
- Datapane: Build internal analytics products in minutes using Python
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Datapane - Build internal data products in 100% Python
Thanks a lot! Yes, absolutely, a few people have brought this up and working working on removing the header right now. If I can help at all, feel free to reach us on GH Discussions: https://github.com/datapane/datapane/discussions
- Datapane/datapane: Build full-stack data analytics apps in Python
What are some alternatives?
nixos-manager - Manage your NixOS packages and configuration via a simple, intuitive UI
streamlit - Streamlit — A faster way to build and share data apps.
ihaskell - A Haskell kernel for the Jupyter project.
dash - Data Apps & Dashboards for Python. No JavaScript Required.
nbformat - Reference implementation of the Jupyter Notebook format
jupyter-dash - OBSOLETE - Dash v2.11+ has Jupyter support built in!
fastpages - An easy to use blogging platform, with enhanced support for Jupyter Notebooks.
perspective - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
jupyter-collaboration - A Jupyter Server Extension Providing Support for Y Documents
superset - Apache Superset is a Data Visualization and Data Exploration Platform
nb_conda_kernels - Package for managing conda environment-based kernels inside of Jupyter
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!