python - Pandas 滚动时间总和

Question

我正在尝试在数据框中获取时间值的滚动总和，如下所示:

    RunTime
0  00:51:25
1       NaT
2  00:42:16
3       NaT
4  00:40:15
5       NaT
6  00:50:13
7  00:53:28
8       NaT
9  00:37:32
10      NaT
11 01:53:22
12 01:08:22
13 00:59:57
14 00:12:22

预期输出:

     RunTime  RunTime_MS
0   00:51:25    
1   NaT         
2   00:42:16    
3   NaT         
4   00:40:15    
5   NaT         
6   00:50:13    3:04:09
7   00:53:28    3:06:12
8   NaT         3:06:12
9   00:37:32    3:01:28
10  NaT         3:01:28
11  01:53:22    4:14:35
12  01:08:22    5:22:57
13  00:59:57    5:32:41
14  00:12:22    4:51:35

对于我正在使用的数据框中的其他列(包含 float )

dfExt['Distance_MS'] = dfExt['Distance'].fillna(value=0).rolling(window=7).sum()

运行得很好。如果我尝试在时间列上执行此操作，则会收到错误

ops for Rolling for this dtype timedelta64[ns] are not implemented

尽管the documentation似乎表明 .sum() 是您可以在 timedelta 上执行的操作。

这是示例代码:

import pandas as pd
from datetime import datetime, timedelta

RunTimeValues = ['00:51:25','','00:42:16','','00:40:15','','00:50:13','00:53:28','','00:37:32','','01:53:22','01:08:22','00:59:57','00:12:22']
for i in range(len(RunTimeValues)):
    if RunTimeValues[i] != '':
        #RunTimeValues[i] = datetime.strptime(RunTimeValues[i], "%H:%M:%S")
        t = datetime.strptime(RunTimeValues[i],"%H:%M:%S")
        RunTimeValues[i] = timedelta(hours=t.hour, minutes=t.minute, seconds=t.second)
dfExt = pd.DataFrame({'RunTime': RunTimeValues})
dfExt['RunTime_MS'] = dfExt['RunTime'].fillna(value=0).rolling(window=7).sum()
print(dfExt)

我知道我可以将时间增量转换为浮点形式的小时，然后进行滚动求和，但该结果并不完全是我想要的。有什么建议么？

Answer 1

这样就可以了:

dfExt['RunTime_MS'] = pd.to_timedelta(dfExt['RunTime'].fillna(0).dt.total_seconds().rolling(window=7).sum(), unit='s')
print(dfExt)
    RunTime RunTime_MS
0  00:51:25        NaT
1       NaT        NaT
2  00:42:16        NaT
3       NaT        NaT
4  00:40:15        NaT
5       NaT        NaT
6  00:50:13   03:04:09
7  00:53:28   03:06:12
8       NaT   03:06:12
9  00:37:32   03:01:28
10      NaT   03:01:28
11 01:53:22   04:14:35
12 01:08:22   05:22:57
13 00:59:57   05:32:41
14 00:12:22   04:51:35