Pandas datetime column operation

Dzakirin Research :

I have dataframe that look like this.

| Date_Time           | Execution_Time |  
|---------------------|----------------|  
| 2019-10-10 09:07:29 | 14.0           |
| 2019-09-21 19:47:01 | 14.3           |
| 2019-09-19 02:49:49 | 14.1           |
| 2019-09-27 23:19:16 | 21.9           |
| 2019-09-05 18:46:00 | 14.2           |

The execution is in seconds. How can I add Date_Time and Execution_Time ?

datatype for Date_Time: object, Execution_Time: float64

I have tried df['diff'] = df['Date_Time'] + df['Execution_Time'] and it returns following error:

TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('<U32') dtype('<U32') dtype('<U32')

jezrael :

First column convert to datetimes by to_datetime and second to timedeltas by to_timedelta:

df['diff'] = (pd.to_datetime(df['Date_Time']) + 
              pd.to_timedelta(df['Execution_Time'], unit='s'))
print (df)
             Date_Time  Execution_Time                    diff
0  2019-10-10 09:07:29            14.0 2019-10-10 09:07:43.000
1  2019-09-21 19:47:01            14.3 2019-09-21 19:47:15.300
2  2019-09-19 02:49:49            14.1 2019-09-19 02:50:03.100
3  2019-09-27 23:19:16            21.9 2019-09-27 23:19:37.900
4  2019-09-05 18:46:00            14.2 2019-09-05 18:46:14.200