Question

我有以下数据框，其中包含4列。让我们称之为df。

    ID  Start transfer  Finish transfer Ward
0   7685933 04/11/2015 12:07    05/11/2015 12:49    General surgery
1   7685933 05/11/2015 12:49    11/11/2015 14:42    Anestesiology
2   7685933 11/11/2015 14:42    11/11/2015 16:12    Anestesiology
3   7685933 11/11/2015 16:12    18/11/2015 21:24    General surgery
4   7685933 18/11/2015 21:24    02/01/2016 06:45    ICU
5   7690142 06/11/2015 17:24    30/11/2015 18:11    Internal Medicine
6   7690142 30/11/2015 18:11    02/12/2015 17:04    Internal Medicine
7   7690142 02/12/2015 17:04    03/12/2015 20:40    Internal Medicine
8   7690142 03/12/2015 20:40    11/01/2016 18:00    Internal Medicine
9   7691888 08/11/2015 16:28    16/11/2015 17:11    Internal Medicine
10  7691888 16/11/2015 17:11    20/11/2015 18:13    Internal Medicine
11  7691888 20/11/2015 18:13    04/01/2016 18:02    Internal Medicine
12  7691888 04/01/2016 18:02    04/01/2016 21:13    Internal Medicine

现在我想根据列'ID'对数据进行分组，然后查找类似的连续Wards，其中Ward的'Finish Transfer'与下一个连续的类似Ward名称的'Start Transfer'相同。一旦确定了这一点，我需要从最后一个连续病房行复制完成转移条目，并用该值更新该特定病房的第一个条目。例如，索引1和2处的row1和row2都具有相似的区域，如果查看row1（index1）的Finish Transfer条目，则类似于row2的start Transfer（index2）。沃德也一样。我想要的是只有一行连续数据，其中start transfer是row1中的一个，Finish是来自row2。

我希望以下输出（可能在新数据框中）：

    ID  Start transfer  Finish transfer Ward
0   7685933 04/11/2015 12:07    05/11/2015 12:49    General surgery
1   7685933 05/11/2015 12:49    11/11/2015 16:12    Anestesiology
2   7685933 11/11/2015 16:12    18/11/2015 21:24    General surgery
3   7685933 18/11/2015 21:24    02/01/2016 06:45    ICU
4   7690142 06/11/2015 17:24    11/01/2016 18:00    Internal Medicine
5   7691888 08/11/2015 16:28    04/01/2016 21:13    Internal Medicine

提前感谢您的帮助。

Answer 1

IIUC

df.groupby(['ID','Ward']).agg({'Start transfer':'first','Finish transfer':'last'}).reset_index()
Out[151]: 
        ID               Ward    Start transfer   Finish transfer
0  7685933      Anestesiology  05/11/2015 12:49  11/11/2015 16:12
1  7685933    General surgery  04/11/2015 12:07  18/11/2015 21:24
2  7685933                ICU  18/11/2015 21:24  02/01/2016 06:45
3  7690142  Internal Medicine  06/11/2015 17:24  11/01/2016 18:00
4  7691888  Internal Medicine  08/11/2015 16:28  04/01/2016 21:13

更新

df.assign(Key=(df.Ward.shift()!=df.Ward).cumsum()).groupby(['ID','Ward','Key']).agg({'Start transfer':'first','Finish transfer':'last'}).reset_index().sort_values('Key')
Out[181]: 
        ID               Ward  Key    Start transfer   Finish transfer
1  7685933    General surgery    1  04/11/2015 12:07  05/11/2015 12:49
0  7685933      Anestesiology    2  05/11/2015 12:49  11/11/2015 16:12
2  7685933    General surgery    3  11/11/2015 16:12  18/11/2015 21:24
3  7685933                ICU    4  18/11/2015 21:24  02/01/2016 06:45
4  7690142  Internal Medicine    5  06/11/2015 17:24  11/01/2016 18:00
5  7691888  Internal Medicine    5  08/11/2015 16:28  04/01/2016 21:13

根据条件更新行并在分组数据中删除几行

1 个答案: