ENH: Uniformised the boolean results in Pandas
Hello everybody,

I'm not completely sure this issue has not been submitted earlier, please accept my apologies if so. It is also possible that what I saw results from an incorrect use of the Pandas package.

It seems that boolean answers are not always rigourosly the same in Pandas, which is really disturbing, and can involve errors when developping scripts using Pandas.

Here are examples of the problem (realized with Python 3.8.5 and Pandas 1.0.5). Let's imagine we have a DataFrame named "data", with a column "col1". We are first using the all() function:

>>>data["col1"].all()
Out: True
>>>data["col1"].all() == True
Out: True
>>>data["col1"].all() is True
False

In this case, the checking of the value using the PEP recommended "is" return a wrong answer.

Same idea with the "empty" function :

>>>data["col1].empty
Out: False
>>>data["col1"].empty == False
Out: True
>>>data["col1"].empty is False
Out: True

This time, checking the value using the "is" statement works well.

From what I understand, some boolean answers are of different "types" (Numpy logical booleans, ...).

Maybe a uniformisation of the boolean answer would avoid these kinds of problems and could be an improvment for future Pandas versions ?
