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Re:Data Science at the Command Line by IanCal on Sunday January 28, 2018 @05:49AM (#16250749)
Notebooks however let you run code blocks in arbitrary orders, delete ones but keep their results in memory, change them without rerunning and change code with none of the downstream dependencies updating.

It's possible (actually very easy) to have code which works as you're making it but not if you run it from scratch.

Re:Data Science at the Command Line by minimaxir on Sunday January 28, 2018 @11:11AM (#16251816)
Which is why notebooks typically a) declare all imports/dependencies in the first block and b) the entire notebook is rerun from a fresh session before publishing (and notebooks have a keyboard command for doing that too).

In all my years of work with Notebooks, I've never had an issue with downstream dependencies.

Re:Data Science at the Command Line by achompas on Monday January 29, 2018 @12:30PM (#16259018)
Important distinction: the notebooks themselves don't do this. The notebook authors, by convention, are supposed to do this.

This is no different than engineers who are, by convention, supposed to write bug-free code. Even with this convention, however, devs still rely on testing to decrease the likelihood of bugs.

No similar tooling exists for notebooks, which is why I recommend moving re-used logic into a tested codebase.