计算熊猫中2个日期的分钟差[重复]答案

【问题标题】：Calculate difference of 2 dates in minutes in pandas [duplicate]计算熊猫中2个日期的分钟差[重复]
【发布时间】：2018-12-31 16:03:20
【问题描述】：

我在数据框中有 2 列。我想在几分钟内计算 2 列的差异并将结果写入新列

Input

Planned Pickup date/time    Actual Pickup date/time      
07/05/2018 09:28:00         07/05/2018 09:33:15               
14/05/2018 17:00:00         15/05/2018 08:44:08               
15/05/2018 17:00:00         15/05/2018 10:52:50              
15/05/2018 17:00:00         15/05/2018 15:03:34             
15/05/2018 17:00:00         15/05/2018 15:03:34              
16/05/2018 17:00:00         16/05/2018 16:00:38

我想以分钟计算实际和计划取货的差异，并将结果写入数据框中名为data['time difference']的新列中

Expected Output

Planned Pickup date/time    Actual Pickup date/time      Time Difference
07/05/2018 09:28:00         07/05/2018 09:33:15               5
14/05/2018 17:00:00         15/05/2018 08:44:08               944
15/05/2018 17:00:00         15/05/2018 10:52:50              -368
15/05/2018 17:00:00         15/05/2018 15:03:34              -117
15/05/2018 17:00:00         15/05/2018 15:03:34              -117
16/05/2018 17:00:00         16/05/2018 16:00:38              -60

如何在 pandas 中做到这一点

【问题讨论】：

标签： python-3.x pandas numpy

【解决方案1】：

用途：

data['time difference'] = ((pd.to_datetime(data['Actual Pickup date/time']) - 
                            pd.to_datetime(data['Planned Pickup date/time']))
                                .dt.total_seconds() / 60)
print (data)

  Planned Pickup date/time Actual Pickup date/time  time difference
0      07/05/2018 09:28:00     07/05/2018 09:33:15         5.250000
1      14/05/2018 17:00:00     15/05/2018 08:44:08       944.133333
2      15/05/2018 17:00:00     15/05/2018 10:52:50      -367.166667
3      15/05/2018 17:00:00     15/05/2018 15:03:34      -116.433333
4      15/05/2018 17:00:00     15/05/2018 15:03:34      -116.433333
5      16/05/2018 17:00:00     16/05/2018 16:00:38       -59.366667

或者如果需要floor 值：

data['time difference'] = ((pd.to_datetime(data['Actual Pickup date/time']) - 
                            pd.to_datetime(data['Planned Pickup date/time']))
                                .astype('<m8[m]').astype(int))
print (data)


  Planned Pickup date/time Actual Pickup date/time  time difference
0      07/05/2018 09:28:00     07/05/2018 09:33:15                5
1      14/05/2018 17:00:00     15/05/2018 08:44:08              944
2      15/05/2018 17:00:00     15/05/2018 10:52:50             -368
3      15/05/2018 17:00:00     15/05/2018 15:03:34             -117
4      15/05/2018 17:00:00     15/05/2018 15:03:34             -117
5      16/05/2018 17:00:00     16/05/2018 16:00:38              -60

【讨论】：