[FIXED] selecting values from different columns, checking for nan and forming new column based on conditions

Issue

I have a df

..	Doc2	DayType
..	NaN	0
..	PQ	Holiday
..	NaN	Holiday
..	PJ	0

I have to form a new column 'AssistanceFactor' such that its value is 1 if Doc2 is NaN and DayType is 'Holiday'

1 if Doc2 is not Nan and DayType is 'Holiday'

0.75 if Doc2 is not Nan and DayType is not 'Holiday'

1 if Doc2 is not NaN and DayType is not 'Holiday'

What would be the best method for this? I tried iterrows and conditions as well as np.select /where. But I am not getting the hang of it.

Thanks in advance

Solution

Because 3 conditions return 1 and one return 0.75 is possible test only 3rd condition:

df['AssistanceFactor'] = np.where(df.Doc2.notna() & df.DayType.ne('Holiday'), 0.75, 1)

Answered By - jezrael