python - 如何根据多列的值将多行合并为一行? [英] How to combine multiple rows into a single row with python pandas based on the values of multiple columns?

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问题描述

我需要将多行合并为一行,原始数据帧如下所示:

I need to combine multiple rows into a single row, and the original dataframes looks like:

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",所以基本上一个人一天的所有旅行顺序和旅行目的都应该在同一行...

I was trying to build some sort of 'trip chain', so basically all the journey sequences and trip purposes of one individual on a single day should be in the same row...

理想情况下,我试图将表格转换为这样的:

Ideally I was trying to convert the table to something like this:

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

如果这是不可能的,那么以下模式也可以:

If this is not possible, then the following mode would also be fine:

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!

Is there any possible solutions? I was thinking on for loop/ while statement, but maybe that was not really a good idea. Thanks in advance!

推荐答案

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  

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