向特定时间段内的日期添加一个小时 Pandas

Question

I have a data frame with two columns, one column is "Date", the other is "Depth(m)" like that

    Date    Depth(m)

0   2015-07-09 13:00:00 2.624

1   2015-07-09 13:15:00 2.686

2   2015-07-09 13:30:00 2.747

3   2015-07-09 13:45:00 2.78

4   2015-07-09 14:00:00 2.826

5   2015-07-09 14:15:00 2.879

6   2015-07-09 14:30:00 2.938

7   2015-07-09 14:45:00 3.005

8   2015-07-09 15:00:00 3.056

9   2015-07-09 15:15:00 3.106

10  2015-07-09 15:30:00 3.173

11  2015-07-09 15:45:00 3.262

12  2015-07-09 16:00:00 3.332

13  2015-07-09 18:15:00 3.35

14  2015-07-09 18:30:00 3.324

15  2015-07-09 18:45:00 3.306

16  2015-07-09 19:00:00 3.299

I want to add an hour to days between 2015-07-09 14:00:00 and 2015-07-09 16:00:00

Output like that

    Date    Depth(m)

0   2015-07-09 13:00:00 2.624

1   2015-07-09 13:15:00 2.686

2   2015-07-09 13:30:00 2.747

3   2015-07-09 13:45:00 2.78

4   2015-07-09 15:00:00 2.826

5   2015-07-09 15:15:00 2.879

6   2015-07-09 15:30:00 2.938

7   2015-07-09 15:45:00 3.005

8   2015-07-09 16:00:00 3.056

9   2015-07-09 16:15:00 3.106

10  2015-07-09 16:30:00 3.173

11  2015-07-09 16:45:00 3.262

12  2015-07-09 17:00:00 3.332

13  2015-07-09 18:15:00 3.35

14  2015-07-09 18:30:00 3.324

15  2015-07-09 18:45:00 3.306

16  2015-07-09 19:00:00 3.299

Thank in advance.

Answer 1

Convert Date column to pandas datetime if it isn't already, and convert start and stop date values also to pandas datetime, then use np.where and assign Date incremented by an hour using pd.to_timedelta for those which fall in the given range.

df['Date'] = pd.to_datetime(df['Date'])
start = pd.to_datetime('2015-07-09 14:00:00')
stop = pd.to_datetime('2015-07-09 16:00:00')

df['Date'] = np.where((df['Date'].ge(start))&(df['Date'].le(stop)),
                       df['Date']+pd.to_timedelta(1, 'H'),
                       df['Date'])

OUTPUT:

                  Date  Depth(m)
0  2015-07-09 13:00:00     2.624
1  2015-07-09 13:15:00     2.686
2  2015-07-09 13:30:00     2.747
3  2015-07-09 13:45:00     2.780
4  2015-07-09 15:00:00     2.826
5  2015-07-09 15:15:00     2.879
6  2015-07-09 15:30:00     2.938
7  2015-07-09 15:45:00     3.005
8  2015-07-09 16:00:00     3.056
9  2015-07-09 16:15:00     3.106
10 2015-07-09 16:30:00     3.173
11 2015-07-09 16:45:00     3.262
12 2015-07-09 17:00:00     3.332
13 2015-07-09 18:15:00     3.350
14 2015-07-09 18:30:00     3.324
15 2015-07-09 18:45:00     3.306
16 2015-07-09 19:00:00     3.299