ipyflow
nbdev
ipyflow | nbdev | |
---|---|---|
20 | 45 | |
1,079 | 4,744 | |
1.0% | 0.6% | |
9.5 | 6.5 | |
4 days ago | 6 days ago | |
Python | Jupyter Notebook | |
BSD 3-clause "New" or "Revised" 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.
ipyflow
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Show HN: Marimo – an open-source reactive notebook for Python
You're probably referring to nbgather (https://github.com/microsoft/gather), which shipped with VSCode for a while.
nbgather used static slicing to get all the code necessary to reconstruct some cell. I actually worked with Andrew Head (original nbgather author) and Shreya Shankar to implement something similar in ipyflow (but with dynamic slicing and a not-as-nice interface): https://github.com/ipyflow/ipyflow?tab=readme-ov-file#state-...
I have no doubt something like this will make its way into marimo's roadmap at some point :)
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React Jam just started, making a game in 13 days with React
Np.
From https://news.ycombinator.com/context?id=35887168 re: ipyflow I learned about ReactiveX for Python (RxPY) https://rxpy.readthedocs.io/en/latest/ .
https://github.com/ipyflow/ipyflow :
> IPyflow is a next-generation Python kernel for Jupyter and other notebook interfaces that tracks dataflow relationships between symbols and cells during a given interactive session, thereby making it easier to reason about notebook state.
FWIU e.g. panda3d does not have a react or rxpy-like API, but probably does have a component tree model?
https://news.ycombinator.com/item?id=38527552 :
>> It actually looks like pygame-web (pygbag) supports panda3d and harfang in WASM
> Harfang and panda3d do 3D with WebGL, but FWIU not yet agents in SSBO/VBO/GPUBuffer
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The GitHub Black Market That Helps Coders Cheat the Popularity Contest
> Another giveaway is the ratio of stars to watchers / forks. I remember one project with thousands of stars but only 10 users "watching" it. They went on to raise a sizable seed round too.
Not necessarily indicative of foul play. I have two projects like this (https://github.com/smacke/ffsubsync and https://github.com/ipyflow/ipyflow) and I attribute it to not having great developer documentation.
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Python 3.12
It's not in the highlights, but one of the things that excites me most is this: https://docs.python.org/dev/whatsnew/3.12.html#pep-669-low-i...
> PEP 669 defines a new API for profilers, debuggers, and other tools to monitor events in CPython. It covers a wide range of events, including calls, returns, lines, exceptions, jumps, and more. This means that you only pay for what you use, providing support for near-zero overhead debuggers and coverage tools. See sys.monitoring for details.
Low-overhead instrumentation opens up a whole bunch of interesting interactive use cases (i.e. Jupyter etc.), and as the author of one library that relies heavily on instrumentation (https://github.com/ipyflow/ipyflow), I'm very keen to explore the possibilities here.
- Excel Labs, a Microsoft Garage Project
- GitHub - ipyflow/ipyflow: A reactive Python kernel for Jupyter notebooks
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IPython kernel alternatives
You’re looking for reactive kernels: https://github.com/ipyflow/ipyflow
- IPyflow: Reactive Python Notebooks in Jupyter(Lab)
nbdev
- The Jupyter+Git problem is now solved
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What is literate programming used for?
One example I've seen is ML/DL folks using jupyter notebooks to develop DL libraries in jupyter notebooks, see https://github.com/fastai/nbdev
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GitHub Accelerator: our first cohort and what's next
- https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow.
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Startups are in first batch of GitHub OS Accelerator
9. Nbdev: Boost developer productivity with an exploratory programming workflow - https://nbdev.fast.ai/
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Start learning python for a Statistician with SAS experience and little R experience
See if you like nbdev way of working with data through python and jupyter. nbdev is an optional part that will create python packages from jupyter notebooks. Also even the simple tutorials are opinionated and will guide you to unit test your code and write CICD pipelines.
- FastKafka - free open source python lib for building Kafka-based services
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isn't this just too much for a take home assignment?
You probably don’t have time for this for the purposes of your task, but I will also throw in the recommendation of nbdev especially if you’re a Python person. I haven’t had a project to use it on yet, but I’ve gone through the docs and the walkthrough and it seems like a great framework for starting potential projects with all the infrastructure needed for if/when they eventually get big and need all the packaging and stuff
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Any experience dealing with a non-technical manager?
nbdev: jupyter notebooks -> python package
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Resources to bridge the gap between jupyter notebooks and regular python development
Take a look at https://github.com/fastai/nbdev - haven't used it but supposedly the whole if fast.ai library was written that way. It sounds like a natural direction in your scenario - allowing your to keep working in a familiar environment and still producing production ready code (will, at least in paper 😅)
- Rant: Jupyter notebooks are trash.
What are some alternatives?
elyra - Elyra extends JupyterLab with an AI centric approach.
papermill - 📚 Parameterize, execute, and analyze notebooks
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
osxphotos - Python app to work with pictures and associated metadata from Apple Photos on macOS. Also includes a package to provide programmatic access to the Photos library, pictures, and metadata.
dbt - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. [Moved to: https://github.com/dbt-labs/dbt-core]
nopdb - NoPdb: Non-interactive Python Debugger
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
subtls - A proof-of-concept TypeScript TLS 1.3 client
rr - Record and Replay Framework
quarto-cli - Open-source scientific and technical publishing system built on Pandoc.
Jupyter-PowerShell - Jupyter Kernel for PowerShell