How To Add Extra Time Into The Time Column With Data Using Python

Here I have a dataset with date, time and one input column. So here my time column is not good. So I want to give time range into that time column. So here first I did I just conve

Solution 1:

Use:

data['date']= pd.to_datetime(data['date'] + " " + data['time'],
                    format='%d/%m/%Y %H:%M:%S', dayfirst=True)

Subtract firat value of date column and convert to minutes:

data['time1'] = data['date'].sub(data.loc[0, 'date']).dt.total_seconds()/60
print (data)
                  date      time  X3  time1
0  2018-03-10 06:15:00   6:15:00   7    0.0
1  2018-03-10 06:45:00   6:45:00   5   30.0
2  2018-03-10 07:45:00   7:45:00   7   90.0
3  2018-03-10 09:00:00   9:00:00   7  165.0
4  2018-03-10 09:25:00   9:25:00   7  190.0
5  2018-03-10 09:30:00   9:30:00   5  195.0
6  2018-03-10 11:00:00  11:00:00   7  285.0
7  2018-03-10 11:30:00  11:30:00   7  315.0
8  2018-03-10 13:30:00  13:30:00   7  435.0
9  2018-03-10 13:50:00  13:50:00   5  455.0
10 2018-03-10 15:00:00  15:00:00   7  525.0
11 2018-03-10 15:25:00  15:25:00   7  550.0
12 2018-03-10 16:25:00  16:25:00   7  610.0
13 2018-03-10 18:00:00  18:00:00   7  705.0
14 2018-03-10 19:00:00  19:00:00   5  765.0

Create new 60 range values:

arr = np.arange(0, int(data['time1'].max()), 60)
print (arr)
[  0  60 120 180 240 300 360 420 480 540 600 660 720]

Join together with minute column:

union = np.union1d(data['time1'], arr)
print (union)
[  0.  30.  60.  90. 120. 165. 180. 190. 195. 240. 285. 300. 315. 360.
 420. 435. 455. 480. 525. 540. 550. 600. 610. 660. 705. 720. 765.]

Create index by time1 column and DataFrame.reindex - added new values from arr:

data = data.set_index('time1')['X3'].reindex(union, fill_value=0).reset_index()
print (data)
    time1  X3
0     0.0   7
1    30.0   5
2    60.0   0
3    90.0   7
4   120.0   0
5   165.0   7
6   180.0   0
7   190.0   7
8   195.0   5
9   240.0   0
10  285.0   7
11  300.0   0
12  315.0   7
13  360.0   0
14  420.0   0
15  435.0   7
16  455.0   5
17  480.0   0
18  525.0   7
19  540.0   0
20  550.0   7
21  600.0   0
22  610.0   7
23  660.0   0
24  705.0   7
25  720.0   0
26  765.0   5