What’s your usual workflow when you just start a new project and are doing the EDA/ data cleaning/ analysis (specifically for Python good habits)?

Do you put everything in functions or even classes?

I’m trying to go from a self-taught noob to someone who’s not completely clueless and wondering what the best practice is.

Found that, especially for larger projects, putting stuff in functions has been a game-changer because otherwise I’d have loads of random variables polluting the global namespace e.g. `df_clean`, `df_clean_na_drop`,`fig_value_count_XYZ`.

What’s the benefit of using classes instead of functions for this purpose? Any other pro tips?

analysis data data cleaning datascience eda good habits project python workflow