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I don't know what this document is meant to do but you will have to take my Jupyter-lab instance from my dead cold hands. I love notebooks, I work fast, line b
by teekert 7y ago
I don't know what this document is meant to do but you will have to take my Jupyter-lab instance from my dead cold hands.
I love notebooks, I work fast, line by line I execute commands and I immediately see the output (dataframes or graphs). For complex code I have an editor open (in jupyter-lab or vscode) for some functions and classes. But the main developing is done in the notebook, anything that ends in a module start in my notebooks.
As a biologist that learned to program after 30 I just don't understand how you can develop data processing code without such a close handle on dataframes and without checking in graphs/visualizations if your code does what you expect. I don't see how I would do that in pure vscode of other IDEs.
I also don't understand this sentence: "Once the data is loaded, it then has to be cleaned, which participants complained is a repetitive and time consuming task that involves copying and pasting code from their personal "library" of commonly used functions." What is the alternative? Not cleaning the code? And why copy and paste when you can perfectly fine have your own shareable module on the side? I guess most notebook users do some kind of hybrid development.
- grenoire 7y agoGood point on the last one; I think we have to 'educate' researchers on the fact that they can also write their own libraries and frameworks, and they should. Even basic data manipulation utilities can be made into Python modules and distributed at ease. If something is tedious, there definitely is a way to make it less so.