argsort with nulls

import pandas, numpy, StringIO

txt = '''ID,RUN_START_DATE,PUSHUP_START_DATE,SITUP_START_DATE,PULLUP_START_DATE
1,2013-01-24,2013-01-02,,2013-02-03
2,2013-01-30,2013-01-21,2013-01-13,2013-01-06
3,2013-01-29,2013-01-28,2013-01-01,2013-01-29
4,2013-02-16,2013-02-12,2013-01-04,2013-02-11
5,2013-01-06,2013-02-07,2013-02-25,2013-02-12
6,2013-01-26,2013-01-28,2013-02-12,2013-01-10
7,2013-01-26,,2013-01-12,2013-01-30
8,2013-01-03,2013-01-24,2013-01-19,2013-01-02
9,2013-01-22,2013-01-13,2013-02-03,
10,2013-02-06,2013-01-16,2013-02-07,2013-01-11'''
df = pandas.read_csv(StringIO.StringIO(txt))
Bigdata_date_tofix = [c for c in df.columns if 'DATE' in c]
df[Bigdata_date_tofix] = df[Bigdata_date_tofix].apply(pandas.to_datetime)
as_indx = df[['RUN_START_DATE', 'PUSHUP_START_DATE', 'SITUP_START_DATE', 'PULLUP_START_DATE']].apply(numpy.argsort, axis=1)



############# RESULT #####################

'''

 RUN_START_DATE           PUSHUP_START_DATE            SITUP_START_DATE  \
0  1970-01-01 00:00:00         1970-01-01 00:00:00  2262-04-10 00:12:43.145224
1                    3                           2                           1
2                    2                           1                           0
3                    2                           3                           1
4                    0                           1                           3
5                    3                           0                           1
6  1970-01-01 00:00:00  2262-04-10 00:12:43.145224         1970-01-01 00:00:00
7                    3                           0                           2
8  1970-01-01 00:00:00         1970-01-01 00:00:00         1970-01-01 00:00:00
9                    3                           1                           0

            PULLUP_START_DATE
0         1970-01-01 00:00:00
1                           0
2                           3
3                           0
4                           2
5                           2
6         1970-01-01 00:00:00
7                           1
8  2262-04-10 00:12:43.145224
9                           2

'''