df = pd.DataFrame([[1601286,np.NaN,np.NaN],
[1601286,1135,2018-12-31],
[1601286,np.NaN,np.NaN],
[1601286,1135,2018-12-31],
[1601286,np.NaN,2018-12-31],
[1601286,1135,2018-12-31],
[1601286,1135,2018-12-31],
[1601286,1135,2018-12-31],
[1601286,np.NaN,np.NaN]], columns=['col1','col2','col3'])
df['count_notnull']=df.count(axis=1) # Will give a count of non-NULLs.
df['bool'] = df['count_notnull'].map(lambda x:x==1) # Since we need only 1 non-Null,
# so we test the condition here.
df
col1 col2 col3 count_notnull bool
0 1601286 NaN NaN 1 True
1 1601286 1135.0 1975.0 3 False
2 1601286 NaN NaN 1 True
3 1601286 1135.0 1975.0 3 False
4 1601286 NaN 1975.0 2 False
5 1601286 1135.0 1975.0 3 False
6 1601286 1135.0 1975.0 3 False
7 1601286 1135.0 1975.0 3 False
8 1601286 NaN NaN 1 True