python - 如何根据多列的值使用python pandas将多行合并为一行？

Question

我需要将多行合并为一行，原始数据框如下所示:

IndividualID    DayID    TripID    JourSequence   TripPurpose
200100000001    1        1         1              3
200100000001    1        2         2              31
200100000001    1        3         3              23
200100000001    1        4         4              5
200100000009    1        55        1              3
200100000009    1        56        2              12
200100000009    1        57        3              4
200100000009    1        58        4              6
200100000009    1        59        5              19
200100000009    1        60        6              2

我试图建立某种“旅行链”，所以基本上一个人在一天中的所有旅行顺序和旅行目的都应该在同一行中......

理想情况下，我试图将表格转换为如下形式:

IndividualID    DayID     Seq1   TripPurp1     Seq2   TripPur2     Seq3   TripPurp3     Seq4   TripPur4
200100000001    1         1      3             2      31           3       23           4      5
200100000009    1         1      3             2      12           3        4           4      6

如果不行，那么下面的模式也行:

IndividualID    DayID      TripPurposes
200100000001    1          3, 31, 23, 5
200100000009    1          3, 12, 4, 6

有没有可能的解决方案？我在考虑 for loop/while 语句，但也许这不是一个好主意。提前致谢!

Answer 1

你可以试试:

df_out = df.set_index(['IndividualID','DayID',df.groupby(['IndividualID','DayID']).cumcount()+1]).unstack().sort_index(level=1, axis=1)
df_out.columns = df_out.columns.map('{0[0]}_{0[1]}'.format)
df_out.reset_index()

输出:

   IndividualID  DayID  JourSequence_1  TripID_1  TripPurpose_1  \
0  200100000001      1             1.0       1.0            3.0   
1  200100000009      1             1.0      55.0            3.0   

   JourSequence_2  TripID_2  TripPurpose_2  JourSequence_3  TripID_3  \
0             2.0       2.0           31.0             3.0       3.0   
1             2.0      56.0           12.0             3.0      57.0   

   TripPurpose_3  JourSequence_4  TripID_4  TripPurpose_4  JourSequence_5  \
0           23.0             4.0       4.0            5.0             NaN   
1            4.0             4.0      58.0            6.0             5.0   

   TripID_5  TripPurpose_5  JourSequence_6  TripID_6  TripPurpose_6  
0       NaN            NaN             NaN       NaN            NaN  
1      59.0           19.0             6.0      60.0            2.0