Microsecond difference between two datetime.time columns in python pandas?

Question

I have a python pandas data frame, which contains 2 columns: time1 and time2:

     time1             time2
13:00:07.294234    13:00:07.294234 
14:00:07.294234    14:00:07.394234 
15:00:07.294234    15:00:07.494234 
16:00:07.294234    16:00:07.694234 

How can I generate a third column which contains the microsecond difference between time1 and time2, in integer if possible?

Answer 1

If you prepend hese with an actual date you can convert them to datetime64 columns:

In [11]: '2014-03-19 ' + df
Out[11]: 
                        time1                       time2
0  2014-03-19 13:00:07.294234  2014-03-19 13:00:07.294234
1  2014-03-19 14:00:07.294234  2014-03-19 14:00:07.394234
2  2014-03-19 15:00:07.294234  2014-03-19 15:00:07.494234
3  2014-03-19 16:00:07.294234  2014-03-19 16:00:07.694234

[4 rows x 2 columns]

In [12]: df = ('2014-03-19 ' + df).astype('datetime64[ns]')
Out[12]: 
                       time1                      time2
0 2014-03-19 20:00:07.294234 2014-03-19 20:00:07.294234
1 2014-03-19 21:00:07.294234 2014-03-19 21:00:07.394234
2 2014-03-19 22:00:07.294234 2014-03-19 22:00:07.494234
3 2014-03-19 23:00:07.294234 2014-03-19 23:00:07.694234

Now you can subtract these columns:

In [13]: delta = df['time2'] - df['time1']

In [14]: delta
Out[14]: 
0          00:00:00
1   00:00:00.100000
2   00:00:00.200000
3   00:00:00.400000
dtype: timedelta64[ns]

To get the number of microseconds, just divide the underlying nanoseconds by 1000:

In [15]: t.astype(np.int64) / 10**3
Out[15]: 
0         0
1    100000
2    200000
3    400000
dtype: int64

As Jeff points out, on recent versions of numpy you can divide by 1 micro second:

In [16]: t / np.timedelta64(1,'us')
Out[16]: 
0         0
1    100000
2    200000
3    400000
dtype: float64