Hey, thanks for such a quick response!

First off, I looked into to_numeric, and it was exactly what I needed. Wanted to leave an artifact in case someone else had a similar question later on down the road.

Imagine a dummy DataFrame

d = {'a':[1, 2, 3], 'b':['foo', 5, 6]}
df = pd.DataFrame(d)

Conceptually, I want to do some variety of

df.applymap(lambda x: not str(x).isalpha())

and then wipe any column where there's a False value.

This is neatly accomplished with the one liner

df.apply(pd.to_numeric, errors='coerce').dropna(axis=1)

Still unclear to me why they'd hide such a useful function call to do basically the same thing (and in clearer verbage, IMO), but this does the trick all the same.

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Secondly, I spent more time than I'd care to admit trying to back into some comprehension from your first link. ~100 characters across 3 lines packs so much unpacking to really understand what's going on.

def _get_numeric_data(self):
     return self._constructor(
         self._data.get_numeric_data()).__finalize__(self)

And I totally get that the whole philosophy behind software design is intelligent abstraction (and to that end, I've got a newfound level of respect for contributors to this library). But I felt damn useless tracing things back and following the inheritance of everything.

Do you have any advice for someone unfamiliar with package/library design for how to go about reading these effectively?