I am trying to take the rowwise max (and min) of two columns containing dates

from datetime import date
import pandas as pd
import numpy as np    df = pd.DataFrame({'date_a' : [date(2015, 1, 1), date(2012, 6, 1),date(2013, 1, 1), date(2016, 6, 1)],'date_b' : [date(2012, 7, 1), date(2013, 1, 1), date(2014, 3, 1), date(2013, 4, 1)]})df[['date_a', 'date_b']].max(axis=1)
Out[46]: 
0    2015-01-01
1    2013-01-01
2    2014-03-01
3    2016-06-01

as expected. However, if the dataframe contains a single NaN value, the whole operation fails

df_nan = pd.DataFrame({'date_a' : [date(2015, 1, 1), date(2012, 6, 1),np.NaN, date(2016, 6, 1)],'date_b' : [date(2012, 7, 1), date(2013, 1, 1), date(2014, 3, 1), date(2013, 4, 1)]})df_nan[['date_a', 'date_b']].max(axis=1)
Out[49]: 
0   NaN 
1   NaN
2   NaN
3   NaN
dtype: float64

What is going on here? I was expecting this result

0    2015-01-01
1    2013-01-01
2    NaN
3    2016-06-01

How can this be achieved?

I would say the best solution is to use the appropriate dtype. Pandas provides a very well integrated datetime dtype. So note, you are using object dtypes...

>>> dfdate_a      date_b
0  2015-01-01  2012-07-01
1  2012-06-01  2013-01-01
2         NaN  2014-03-01
3  2016-06-01  2013-04-01
>>> df.dtypes
date_a    object
date_b    object
dtype: object

But note, the problem disappears when you use

>>> df2 = df.apply(pd.to_datetime)
>>> df2date_a     date_b
0 2015-01-01 2012-07-01
1 2012-06-01 2013-01-01
2        NaT 2014-03-01
3 2016-06-01 2013-04-01
>>> df2.min(axis=1)
0   2012-07-01
1   2012-06-01
2   2014-03-01
3   2013-04-01
dtype: datetime64[ns]