Need to add a new DateTime column to an already existing dataframe using its columns

Question

I have a data frame in which I have the distance and speed out of which I calculated the time. Now I want to add a column in which I can give its a fixed DateTime value for the first row and it will automatically add the time and make it to the next value to the dateTime column

Currently the dataframe looks like:

x_coordinate    y_coordinate    z_coordinate    speed   Distance    Time1
-22                -2.28           -0.1         300     1           0.2
-21                -2.28           -0.1         300     1           0.2
-20                -2.28           -0.1         300     1           0.2
-19                -2.28           -0.1         300     1           0.2
-18                -2.28           -0.1         300     1           0.2
-17                -2.28           -0.1         300     1           0.2
-16                -2.28           -0.1         300     1           0.2
-15                -2.28           -0.1         300     1           0.2
-14                -2.28           -0.1         300     1           0.2
-13.2674           -2.601          -0.1         300 0.7998398339667759  0.15996796679335518
-13.039            -3.5743         -0.1         300 0.9997396911196436  0.1999479382239287
-12.7392           -4.5281         -0.1         300 0.9998072214182092  0.19996144428364185
-12.3697           -5.4571         -0.1         300 0.9997856020167519  0.1999571204033504

The time is in seconds like the 0.2 seconds that I calculated with speed 300 mm/sec and distance = 1 mm. Now I want to add a column named as DateTime which has the first Dattime value hardcoded and the consecutive value calulated by the time columns like

    Datetime
    2019-02-21 03:50:39.000 --> this is hardcoded
    2019-02-21 03:50:39.200 --> this to be calculated by adding 0.2 seconds from row 1.
     and so on

Quang Hoang · Accepted Answer · 2019-06-06 17:38:09Z

1

Try:

df['new_time'] = (pd.to_datetime('2019-02-21 03:50:39.000') + 
                  pd.to_timedelta(df.Time1.shift().cumsum(), unit='s')
                 )

Output (df['new_time']):

0    2019-02-21 03:50:39.000000000
1    2019-02-21 03:50:39.200000000
2    2019-02-21 03:50:39.400000000
3    2019-02-21 03:50:39.600000000
4    2019-02-21 03:50:39.800000000
5    2019-02-21 03:50:40.000000000
6    2019-02-21 03:50:40.200000000
7    2019-02-21 03:50:40.400000000
8    2019-02-21 03:50:40.600000000
9    2019-02-21 03:50:40.800000000
10   2019-02-21 03:50:40.959967967
11   2019-02-21 03:50:41.159915905
12   2019-02-21 03:50:41.359877349
Name: new_time, dtype: datetime64[ns]

answered Jun 6, 2019 at 17:38

Quang Hoang

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