Python pandas change duplicate timestamp to unique -

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i have file containing duplicate timestamps, maximum 2 each timestamp, not duplicate, second timestamp needs add millisecond timestamp. example, having these in file,

.... 2011/1/4    9:14:00 2011/1/4    9:15:00 2011/1/4    9:15:01 2011/1/4    9:15:01 2011/1/4    9:15:02 2011/1/4    9:15:02 2011/1/4    9:15:03 2011/1/4    9:15:03 2011/1/4    9:15:04 ....

i change them into

2011/1/4    9:14:00 2011/1/4    9:15:00 2011/1/4    9:15:01 2011/1/4    9:15:01.500 2011/1/4    9:15:02 2011/1/4    9:15:02.500 2011/1/4    9:15:03 2011/1/4    9:15:03.500 2011/1/4    9:15:04 ....

what efficient way perform such task?

setup

in [69]: df = dataframe(dict(time = x))  in [70]: df out[70]:                   time 0 2013-01-01 09:01:00 1 2013-01-01 09:01:00 2 2013-01-01 09:01:01 3 2013-01-01 09:01:01 4 2013-01-01 09:01:02 5 2013-01-01 09:01:02 6 2013-01-01 09:01:03 7 2013-01-01 09:01:03 8 2013-01-01 09:01:04 9 2013-01-01 09:01:04

find locations difference in time previous row 0 seconds

in [71]: mask = (df.time-df.time.shift()) == np.timedelta64(0,'s')  in [72]: mask out[72]:  0    false 1     true 2    false 3     true 4    false 5     true 6    false 7     true 8    false 9     true name: time, dtype: bool

set theose locations use offset of 5 milliseconds (in question used 500 anything). requires numpy >= 1.7. (not syntax changing in 0.13 allow more direct df.loc[mask,'time'] += pd.offsets.milli(5)

in [73]: df.loc[mask,'time'] = df.time[mask].apply(lambda x: x+pd.offsets.milli(5))  in [74]: df out[74]:                          time 0        2013-01-01 09:01:00 1 2013-01-01 09:01:00.005000 2        2013-01-01 09:01:01 3 2013-01-01 09:01:01.005000 4        2013-01-01 09:01:02 5 2013-01-01 09:01:02.005000 6        2013-01-01 09:01:03 7 2013-01-01 09:01:03.005000 8        2013-01-01 09:01:04 9 2013-01-01 09:01:04.005000

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