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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.
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Develop notebook-based pipelines
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Automatically convert ipynb files to py when saving them on JupyterLab
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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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