What’s wrong with computational notebooks?
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What’s wrong with computational notebooks?
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Re: What’s wrong with computational notebooks?
#2slides: https://docs.google.com/presentation/d/1n2RlMdmv1p25Xy5thJUh...
Re: What’s wrong with computational notebooks?
#3Re: What’s wrong with computational notebooks?
#4Re: What’s wrong with computational notebooks?
#51. Checkpoint the interpreter state after every cell execution.
2. If I edit a cell, roll back to the previous checkpoint and let execution follow from there.
I can't tell you how many times I've seen accidental persistence of dead state waste hours of people's time.
Re: What’s wrong with computational notebooks?
#6The main question is: what are the alternatives, for data exploration (and sharing its results). Similarly, for data science tool demos, Notebooks shine.
IMHO the problem is not in the notebooks, but in how they are being used (i.e. the workflow). By writing scripts in py files, and using notebooks only to show their results (processed data, charts, etc) we get the best of both worlds.
The only build-in problem with Jupyter Notebooks is JSON, mixing input and output (and making it pain to work with version control). But here RMarkdown (and a few other alternatives) work well.
Re: What’s wrong with computational notebooks?
#7Re: What’s wrong with computational notebooks?
#8One category left out of the list: testing of data pipelines (c.f. great expectations).
Re: What’s wrong with computational notebooks?
#9No mention of https://observablehq.com notebooks? They’re the best I’ve found in the “Share and collaborate” and “As products” category. JupyterLab is still pretty great for exploratory stuff, but visualization possibilities in observable are incredible.