There's too many ways of doing the same thing (which I assume is already itself a relic of maintaining back-compatibility), there's inconsistencies within the API, there's "deprecated" stuff which isn't really deprecated, et cetera
dataframe.column
vs
dataframe['column']
as one example comes to mind but there is surely much more
I am of the philosophy of 'The Zen of Python'
There should be one-- and preferably only one --obvious way to do it.
Pandas is a powerful library, but when I have to use it in a workplace it usually gives me a feeling of dread, knowing I am soon to face various hacks and dataframes full of NaNs without them being handled properly, etc.
Which column format would you prefer? You need the latter to address random loaded data which may contain illegal identifiers. You need the former to stifle the rumblings about Pandas verbosity.
I would get rid of the .column accessor, but you will see a lot of pushback. Notably from the R camp.
I would also get rid of the .column accessor, it has the potential to collide with function members of the DataFrame so shouldn't have been added in the first place IMO
I am of the philosophy of 'The Zen of Python'
Pandas is a powerful library, but when I have to use it in a workplace it usually gives me a feeling of dread, knowing I am soon to face various hacks and dataframes full of NaNs without them being handled properly, etc.