This is especially true for a data scientist, where most code is throwaway. If you make it all spectacular, you aren't getting anything done. Data scientists' c

This is especially true for a data scientist, where most code is throwaway. If you make it all spectacular, you aren't getting anything done. Data scientists' code should be "eventually good," that is to say it gets refactored as it approaches a production environment. I talk about this in my last book, Agile Data Science 2.0 (Amazon 4.1 stars 7 years after publishing). https://www.amazon.com/Agile-Data-Science-2-0-Applications/dp/1491960116 https://www.amazon.com/Agile-Data-Science-2-0-Applications/d... I will say that after 20 years of working as a software engineer, data engineer, data scientist and ML engineer, I can write pretty clean Python all the time but this isn't common.