Rant: Jupyter notebooks are trash.

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

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  • 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.

  • nbdev

    Create delightful software with Jupyter Notebooks

  • 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.

  • ploomber

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

    Develop notebook-based pipelines

  • jupytext

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

    Automatically convert ipynb files to py when saving them on JupyterLab

  • 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).

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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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