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If I have a date time index like this: 

 DatetimeIndex(['2018-01-22 21:39:00', '2018-01-22 21:39:01',
                '2018-01-22 21:39:03', '2018-01-22 21:39:06',
                '2018-01-22 21:39:07', '2018-01-22 21:39:08',
                '2018-01-22 21:39:09', '2018-01-22 21:39:10',
                '2018-01-22 21:39:11', '2018-01-22 21:39:12'], dtype='datetime64[ns]', freq=None)

How do I subtract a half second from each value such that I get a data time index like this:

 DatetimeIndex(['2018-01-22 21:38:59.50', '2018-01-22 21:39:00.50',
                '2018-01-22 21:39:02.50', '2018-01-22 21:39:05.50',
                '2018-01-22 21:39:06.50', '2018-01-22 21:39:07.50',
                '2018-01-22 21:39:08.50', '2018-01-22 21:39:09.50',
                '2018-01-22 21:39:10.50', '2018-01-22 21:39:11.50'], dtype='datetime64[ns]', freq=None)
CypherX
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Zmann3000
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2 Answers2

3

Solution

You can subtract the 0.5 seconds from the DateTimeIndex objects as a datetime.timedelta object.

Short Answer

import datetime

dt = datetime.timedelta(seconds=0.5)
pd.DatetimeIndex(datetime_data) - dt

Output: 

0   2018-01-22 21:38:59.500
1   2018-01-22 21:39:00.500
2   2018-01-22 21:39:02.500
3   2018-01-22 21:39:05.500
4   2018-01-22 21:39:06.500
5   2018-01-22 21:39:07.500
6   2018-01-22 21:39:08.500
7   2018-01-22 21:39:09.500
8   2018-01-22 21:39:10.500
9   2018-01-22 21:39:11.500
Name: Timestamp, dtype: datetime64[ns]

Solution in Detail

1. Make Data

import numpy as np
import pandas as pd

datetime_data = ['2018-01-22 21:39:00', '2018-01-22 21:39:01',
                '2018-01-22 21:39:03', '2018-01-22 21:39:06',
                '2018-01-22 21:39:07', '2018-01-22 21:39:08',
                '2018-01-22 21:39:09', '2018-01-22 21:39:10',
                '2018-01-22 21:39:11', '2018-01-22 21:39:12']

dti = pd.DatetimeIndex(datetime_data)
dti

Output: 

DatetimeIndex(['2018-01-22 21:39:00', '2018-01-22 21:39:01',
               '2018-01-22 21:39:03', '2018-01-22 21:39:06',
               '2018-01-22 21:39:07', '2018-01-22 21:39:08',
               '2018-01-22 21:39:09', '2018-01-22 21:39:10',
               '2018-01-22 21:39:11', '2018-01-22 21:39:12'],
              dtype='datetime64[ns]', freq=None)

2. Subtract 0.5 Second

import datetime

df = pd.DataFrame(dti, columns=['Timestamp'])
dt = datetime.timedelta(seconds=0.5)
df.Timestamp - dt

Output: 

0   2018-01-22 21:38:59.500
1   2018-01-22 21:39:00.500
2   2018-01-22 21:39:02.500
3   2018-01-22 21:39:05.500
4   2018-01-22 21:39:06.500
5   2018-01-22 21:39:07.500
6   2018-01-22 21:39:08.500
7   2018-01-22 21:39:09.500
8   2018-01-22 21:39:10.500
9   2018-01-22 21:39:11.500
Name: Timestamp, dtype: datetime64[ns]
CypherX
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0

you can use pd.DateOffset to substract 0.5 seconds from each value : 

df.index - pd.DateOffset(seconds=0.5)
print(df)
DatetimeIndex(['2018-01-22 21:38:59.500000', '2018-01-22 21:39:00.500000',
           '2018-01-22 21:39:02.500000', '2018-01-22 21:39:05.500000',
           '2018-01-22 21:39:06.500000', '2018-01-22 21:39:07.500000',
           '2018-01-22 21:39:08.500000', '2018-01-22 21:39:09.500000',
           '2018-01-22 21:39:10.500000', '2018-01-22 21:39:11.500000'],
          dtype='datetime64[ns]', name=0, freq=None)
Umar.H
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