Pandas:带条件的时间窗口移动总和

Question

我有一个数据帧，其中包含特定机器的布尔故障(带有时间戳)。我想添加一个列，它为特定机器执行相对于时间戳的特定时间范围内所有故障的移动求和。例如，计算每台机器在生产线故障前8天到1天内发生了多少次故障。

这将创建一个初始数据帧的示例：

import pandas as pd
df1=pd.DataFrame({"Machine":["M0","M2","M3","M0","M2","M3"],"Failure":[0,0,1,1,1,1],"Date-time":["2014-02-20 11:00:19.0","2014-02-21 12:29:55.0","2014-02-20 11:00:21.0","2014-02-19 09:10:19.0","2014-02-18 12:19:47.0","2014-02-20 1:33:00.0"]})

这将创建一个输出数据帧示例：

df1=pd.DataFrame({"Machine":["M0","M2","M3","M0","M2","M3"],"Number of failures, d-8 to d-1":[1,1,0,0,0,0],"Failure":[0,0,1,1,1,1],"Date-time":["2014-02-20 11:00:19.0","2014-02-21 12:29:55.0","2014-02-20 11:00:21.0","2014-02-19 09:10:19.0","2014-02-18 12:19:47.0","2014-02-20 1:33:00.0"]})

Answer 1

我发现了一个类似的问题，在这里回答。

Pandas temporal cumulative sum by group

保留这两个线程可能是值得的，因为它们的表达方式非常不同。这可能有助于搜索。