Rant: Jupyter notebooks are trash.

This page summarizes the projects mentioned and recommended in the original post on /r/datascience

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  1. ipyflow

    A reactive Python kernel for Jupyter notebooks.

    I've been trying to make it easier to iterate on notebooks and go from notebook -> finished product with https://github.com/ipyflow/ipyflow. On the iteration side, it supports things like execution suggestions and reactivity to keep your execution state in sync with the code in your cells. On the "productionization" side, there's a code function which can be used to retrieve all the code necessary for computing some symbol.

  2. Judoscale

    Save 47% on cloud hosting with autoscaling that just works. Judoscale integrates with Django, FastAPI, Celery, and RQ to make autoscaling easy and reliable. Save big, and say goodbye to request timeouts and backed-up task queues.

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  3. nbdev

    Create delightful software with Jupyter Notebooks

  4. ploomber

    The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️

    Develop notebook-based pipelines

  5. jupytext

    Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts

    Automatically convert ipynb files to py when saving them on JupyterLab

  6. lineapy

    Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code.

    There are a few projects that can help close this gap between notebook prototype -> production. One of them is ipyflow (https://github.com/ipyflow/ipyflow), another is lineapy (https://github.com/linealabs/lineapy).

  7. CodeRabbit

    CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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