查找自上次事件 pandas 数据框以来的天数 [英] Find days since last event pandas dataframe

问题描述

我有一个熊猫数据框:

df12 = pd.DataFrame({'group_ids':[1,1,1,2,2,2],'dates':['2016-04-01','2016-04-20','2016-04-28','2016-04-05','2016-04-20','2016-04-29'],'event_today_in_group':[1,0,1,1,1,0]})


   group_ids      dates  event_today_in_group
0          1 2016-04-01                     1
1          1 2016-04-20                     0
2          1 2016-04-28                     1
3          2 2016-04-05                     1
4          2 2016-04-20                     1
5          2 2016-04-29                     0

我想计算一个额外的列，该列针对每个group_ids包含自上一次event_today_in_group为1以来的天数.

I would like to compute an additional column that contains, for each group_ids, the number of days since the last time event_today_in_group was 1.

 group_ids      dates  event_today_in_group  days_since_last_event
0          1 2016-04-01                     1                      0
1          1 2016-04-20                     0                     19
2          1 2016-04-28                     1                     27
3          2 2016-04-05                     1                      0
4          2 2016-04-20                     1                     15
5          2 2016-04-29                     0                      9

推荐答案

正如我前面提到的，这将使您获得每个组中日期之间的非累积差异:

As I mentioned earlier, this will get you the non-cumulative difference between dates within each group:

df['days_since_last_event'] = df.groupby('group_ids')['dates'].diff().apply(lambda x: x.days)

为了获得此差异的累积和，基于event_today_in_group的变化，我建议使用shift来获取上一行的值，然后生成一个累积和，如下所示:

In order to get a cumulative sum of this difference, based on whenever event_today_in_group changes, I propose using shift to get the value of the previous row, and then generating a cumulative sum, like so:

df['event_today_in_group'].shift().cumsum()

输出:

0    NaN
1    1.0
2    1.0
3    2.0
4    3.0
5    4.0

这为我们提供了获得累积总和所需的第二个分组值.您可以将上述值分配给新列，但是如果仅将它们用于计算，则可以将它们简单地包含在随后的groupby操作中，如下所示:

This gives us the second grouping value we need to get the cumulative sums. You could assign the above values to a new column, but if you're only using them for the calculation, then you can simply include them in the subsequent groupby operation like so:

df.loc[:, 'days_since_last_event'] = df.groupby(['group_ids', df['event_today_in_group'].shift().cumsum()])['days_since_last_event'].cumsum()

结果:

   group_ids      dates  event_today_in_group  days_since_last_event
0          1 2016-04-01                     1                    NaN
1          1 2016-04-20                     0                   19.0
2          1 2016-04-28                     1                   27.0
3          2 2016-04-05                     1                    NaN
4          2 2016-04-20                     1                   15.0
5          2 2016-04-29                     0                    9.0

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