如果pandas DataFrame中的语句，则由可能包含nan的其他三个列计算得出的布尔列

Question

I am determining a boolean to put in column A , given the values of column B , C , and D .

Columns B , C , D contain booleans, but also possibly nan .

I need this statement to result in the following

Column A should be...

True if [B,C,D] = [False, False, False]
False if [B,C,D] contains True, regardless of other values
nan if columns [B,C,D] contain only False or nan

Unfortunately, nan values are returning False , so the following code does not work.

 df['A'] = ~(df['B'] | df['C'] | df['D'])

Thank you.

Edit to clarify wording:

The above three situations were considered to follow one another. The only condition which returns True is FFF. Any true in the set should return false. And any set that contains some combo of FF and nan should return nan. In the third situation, I can no longer say for sure that this row is triple negative, and so I return nan. Any row with a True in B|C|D is definitely not triple negative.

Answer 1

As pointed out in the comments, there is an ambiguity in the wording, as an all- False row could have two possible return values. Assuming that the all- False case should give a True , the following should do what you are looking for:

In [41]: df
Out[41]: 
       B      C      D
0  False  False  False
1   True  False    NaN
2    NaN  False  False

In [42]: df['A'] = df.apply(lambda row: True if not any(row) else False if True in list(row) else np.nan, axis=1)

In [43]: df
Out[43]: 
       B      C      D      A
0  False  False  False   True
1   True  False    NaN  False
2    NaN  False  False    NaN