So I only do data engineering, backend APIs, Data Science and Machine Learning. And so many times i learned something about OOP, i go excited I implemented it and i ended up with a solution that was really complex and I re-implemented it using functions and procedural code.
However, i do love a library like pandas with their data frames and series etc. which are two really cool and powerful abstractions / classes.
I also noticed this trend in Python/ Data Developers (including myself),that they start writing mostly procedural code and at some point they feel like they "have to" move to classes to be more sophisticated.
Maybe I am also just bad at finding good and easy to understand abstractions that you can re-use like a data frame. But maybe when it comes to data this is just quite hard? Would love to hear about other data-peoples perspectives about OOP.
Also the thing with data engineering the state lives in a DB or a file and not really in my class (because its soo large).
However, i do love a library like pandas with their data frames and series etc. which are two really cool and powerful abstractions / classes.
I also noticed this trend in Python/ Data Developers (including myself),that they start writing mostly procedural code and at some point they feel like they "have to" move to classes to be more sophisticated.
Maybe I am also just bad at finding good and easy to understand abstractions that you can re-use like a data frame. But maybe when it comes to data this is just quite hard? Would love to hear about other data-peoples perspectives about OOP.
Also the thing with data engineering the state lives in a DB or a file and not really in my class (because its soo large).