Python 如何有条件地将相同的随机 ID 分配给Pandas 中的相关行

我还是个新手，我正在努力解决将一些随机ID分配给"相关"行的问题 其中关系简单地是他们在按用户分组的连续几天中的接近程度(在14天内).在那个例子中，我 Select 了uuid，没有任何特别的意图.它可以是唯一标识概念上相关行的任何其他随机ID.

        import pandas as pd
        import uuid
        import numpy as np

以下是一个虚拟数据帧:

        dummy_df = pd.DataFrame({"transactionid": [1, 2, 3, 4, 5, 6, 7, 8],
                                "user": ["michael", 
                                                    "michael", 
                                                    "michael", 
                                                    "tom", 
                                                    "tom", 
                                                    "tom",
                                                    "tom", 
                                                    "tom"],
                                "transactiontime": pd.to_datetime(["2022-01-01", 
                                                                "2022-01-02", 
                                                                "2022-01-03", 
                                                                "2022-09-01", 
                                                                "2022-09-13",
                                                                "2022-10-17",
                                                                "2022-10-20",
                                                                "2022-11-17"])})
        dummy_df.head(10)
        
        transactionid   user    transactiontime
                0   1   michael 2022-01-01
                1   2   michael 2022-01-02
                2   3   michael 2022-01-03
                3   4   tom     2022-09-01
                4   5   tom     2022-09-13
                5   6   tom     2022-10-17
                6   7   tom     2022-10-20
                7   8   tom     2022-11-17

我在这里对交易进行排序，并计算它们之间的差额(以天为单位):

        dummy_df = dummy_df.assign(
            timediff = dummy_df
            .sort_values('transactiontime')
            .groupby(["user"])['transactiontime'].diff() / np.timedelta64(1, 'D')
            ).fillna(0)
        
        dummy_df.head(10)
        
        
        transactionid   user    transactiontime timediff
                0   1   michael 2022-01-01  0.0
                1   2   michael 2022-01-02  1.0
                2   3   michael 2022-01-03  1.0
                3   4   tom     2022-09-01  0.0
                4   5   tom     2022-09-13  12.0
                5   6   tom     2022-10-17  34.0
                6   7   tom     2022-10-20  3.0
                7   8   tom     2022-11-17  28.0

在这里，我 for each 相关事务创建了一个具有随机ID的新列--尽管它并不像预期的那样工作:

        dummy_df.assign(related_transaction = np.where((dummy_df.timediff >= 0) & (dummy_df.timediff < 15), uuid.uuid4(), dummy_df.transactionid))
        
        
        transactionid   user    transactiontime timediff    related_transaction
                0   1   michael 2022-01-01  0.0  fd630f07-6564-4773-aff9-44ecb1e4211d
                1   2   michael 2022-01-02  1.0  fd630f07-6564-4773-aff9-44ecb1e4211d
                2   3   michael 2022-01-03  1.0  fd630f07-6564-4773-aff9-44ecb1e4211d
                3   4   tom     2022-09-01  0.0  fd630f07-6564-4773-aff9-44ecb1e4211d
                4   5   tom     2022-09-13  12.0 fd630f07-6564-4773-aff9-44ecb1e4211d
                5   6   tom     2022-10-17  34.0    6
                6   7   tom     2022-10-20  3.0  fd630f07-6564-4773-aff9-44ecb1e4211d
                7   8   tom     2022-11-17  28.0    8

假设交易之间的用户组差异在14天内，我希望是这样的:

        transactionid   user    transactiontime timediff    related_transaction
                0   1   michael 2022-01-01  0.0  ad2a8f23-05a5-49b1-b45e-cbf3f0ba23ff
                1   2   michael 2022-01-02  1.0  ad2a8f23-05a5-49b1-b45e-cbf3f0ba23ff
                2   3   michael 2022-01-03  1.0  ad2a8f23-05a5-49b1-b45e-cbf3f0ba23ff
                3   4   tom     2022-09-01  0.0  b1da2251-7770-4756-8863-c82f90657542
                4   5   tom     2022-09-13  12.0 b1da2251-7770-4756-8863-c82f90657542
                5   6   tom     2022-10-17  34.0 485a8d97-80d1-4184-8fc8-99523f471527
                6   7   tom     2022-10-20  3.0  485a8d97-80d1-4184-8fc8-99523f471527
                7   8   tom     2022-11-17  28.0    8