python - 将时间戳添加到分钟

Question

我有一个包含 200 行的 DataFrame df。前几行如下所示:

+--------------+----------+----------------+
|Customer name | Arrival  | Actual Arrival | 
+--------------+----------+----------------+
| Customer 21  |       20 |                |
| Customer 22  |       30 |                |  
| Customer 23  |       20 |                | 
| Customer 24  |       10 |                |  
| Customer 25  |       20 |                | 
+--------------+----------+----------------+

到达列显示从开始时间算起的到达时间分钟。因此，我想通过向到达列值添加固定时间戳(开始时间)来创建实际到达列。例如:如果开始时间是 07:00；

+--------------+----------+----------------+
|Customer name | Arrival  | Actual Arrival | 
+--------------+----------+----------------+
| Customer 21  |       20 |    07:20       |
| Customer 22  |       30 |    07:30       |  
| Customer 23  |       20 |    07:20       | 
| Customer 24  |       10 |    07:10       |  
| Customer 25  |       60 |    08:00       | 
+--------------+----------+----------------+

如何在 python 中执行此操作？

Answer 1

您可以尝试以下操作吗:

import datetime
actual_start_time = datetime.time(7, 0)
start_time = datetime.datetime.combine(datetime.datetime.today().date(), actual_start_time)
df['Actual Arrival'] = df['Arrival'].apply(lambda x: start_time + datetime.timedelta(minutes=x))

示例:

>>> df = pd.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]]).T
>>> df.columns = ['name', 'Arrival']
>>> df
   name  Arrival
0     1        5
1     2        6
2     3        7
3     4        8
>>> start_time = datetime.datetime.strptime('2019-05-17 7:0:0', '%Y-%m-%d %H:%M:%S')
>>> df['Actual Arrival'] = df['Arrival'].apply(lambda x: start_time + datetime.timedelta(minutes=x))
>>> df
   name  Arrival      Actual Arrival
0     1        5 2019-05-17 07:05:00
1     2        6 2019-05-17 07:06:00
2     3        7 2019-05-17 07:07:00
3     4        8 2019-05-17 07:08:00

如果您只想要时间，可以执行以下操作:

>>> df['Actual Arrival'] = df['Arrival'].apply(lambda x: (start_time + datetime.timedelta(minutes=x)).time())
>>> df
   name  Arrival Actual Arrival
0     1        5       07:05:00
1     2        6       07:06:00
2     3        7       07:07:00
3     4        8       07:08:00