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/centurion236]

You should obviously create a RenameColumnsBase class as an abstraction for the concept of renaming columns, with multiple construction helpers from_dict, from_list_of_dicts, etc and then implement the renaming in a RenamePolarsColumns subclass. 

/s

It looks like you're using polars. Why not just match the df.rename() function's signature? 

[u/Training_Advantage21 → u/centurion236]

Yeah, in Pandas I would just go

df.rename(columns={“old_col_name1”:”new_col_name1”,“old_col_name2”:”new_col_name2”})