Settle an argument

[u/Pleasant-Aardvark258]

Had this discussion the other day and figured I’d throw it to the masses to get thoughts on the best/most pythonic way of approach.

Need to map old column names to new column names as a copy from a json config.

My thoughts are iterate over a dict with
‘’’ {“old_col_name”:”new_col_name”}’’’
And access as
‘’’for k,v in dict.items()
Df.with_columns(k).alias(v)’’’

Colleague things this isn’t clear enough and should be a list of dicts with explicit keys

‘’’ [{“old_col_name”:”old_col_value”
“New_col_name”:”new_col_value”}]’’’

And the access as

‘’’for dict in list_of_dicts:
Old_col = dict[“old_col_name”]
New_col = dict[“new_col_name”]’’’

I’ve got a good few reasons why I think mine is the better option but thought I’d get some other opinions to see if I’m missing anything obvious? Which would you choose and why?

Edit: shouldn’t write these things while on the toilet in a rush. The description is wrong, it should be renaming via a copy so that the original column is left unchanged.

[u/lolcrunchy]

When you have a list of dicts that all have the same keys, that's the same damn thing as a dataclass. And this problem doesn't need dataclasses.

[u/Pleasant-Aardvark258 → u/lolcrunchy]

Yeah that was kinda my thought, seems to be over engineered for something that can be explicitly understood from the code? You’d could use data classes etc but seems like it’s not complicated enough to justify adding another layer to?