每次列中字符串更改时的布尔标记，按组

Question

我有以下数据框：

data = {'unit': {59: 'unit1',
  662: 'unit1',
  680: 'unit1',
  725: 'unit1',
  709: 'unit1',
  703: 'unit1',
  653: 'unit1',
  807: 'unit4',
  825: 'unit4',
  778: 'unit4',
  816: 'unit4',
  822: 'unit4',
  849: 'unit4',
  820: 'unit4',
  754: 'unit4',
  1031: 'unit3',
  1094: 'unit2',
  1008: 'unit2',
  1089: 'unit2',
  1044: 'unit5'},
 'Date_job': {59: datetime.date(2021, 6, 7),
  662: datetime.date(2021, 6, 14),
  680: datetime.date(2021, 7, 5),
  725: datetime.date(2021, 7, 26),
  709: datetime.date(2021, 8, 30),
  703: datetime.date(2021, 10, 11),
  653: datetime.date(2021, 10, 18),
  807: datetime.date(2021, 7, 19),
  825: datetime.date(2021, 7, 26),
  778: datetime.date(2021, 8, 23),
  816: datetime.date(2021, 8, 30),
  822: datetime.date(2021, 9, 6),
  849: datetime.date(2021, 9, 27),
  820: datetime.date(2021, 10, 4),
  754: datetime.date(2021, 10, 18),
  1031: datetime.date(2021, 9, 6),
  1094: datetime.date(2021, 7, 26),
  1008: datetime.date(2021, 8, 9),
  1089: datetime.date(2021, 10, 4),
  1044: datetime.date(2021, 6, 14)},
 'Vector': {59: ['A|14:1/9.0',
   'A|15:1/11.0',
   'A|16:1/12.0',
   'B|11:1/4.0',
   'B|2:1/3.0',
   'B|3:1/12.0',
   'B|4:1/12.0',
   'B|5:1/9.0',
   'B|6:1/5.0',
   'B|7:1/5.0'],
  662: ['A|14:1/9.0',
   'A|15:1/11.0',
   'A|16:1/12.0',
   'B|11:1/4.0',
   'B|3:1/12.0',
   'B|4:1/12.0',
   'B|5:1/9.0',
   'B|5:1/8.0',
   'B|6:1/5.0',
   'B|7:1/5.0'],
  680: ['A|14:1/9.0',
   'A|14:1/4.0',
   'A|15:1/11.0',
   'A|16:1/12.0',
   'B|11:1/4.0',
   'B|3:1/12.0',
   'B|4:1/12.0',
   'B|5:1/9.0',
   'B|6:1/5.0',
   'B|7:1/5.0'],
  725: ['A|14:1/9.0',
   'A|15:1/11.0',
   'A|16:1/12.0',
   'B|11:1/4.0',
   'B|2:1/3.0',
   'B|3:1/12.0',
   'B|4:1/12.0',
   'B|5:1/9.0',
   'B|6:1/5.0',
   'B|7:1/5.0'],
  709: ['A|14:1/9.0',
   'A|15:1/11.0',
   'A|16:1/12.0',
   'B|11:1/4.0',
   'B|2:1/3.0',
   'B|3:1/12.0',
   'B|4:1/12.0',
   'B|5:1/9.0',
   'B|6:1/5.0',
   'B|7:1/5.0'],
  703: ['A|14:1/9.0',
   'A|15:1/11.0',
   'A|16:1/12.0',
   'B|11:1/4.0',
   'B|2:1/4.0',
   'B|3:1/12.0',
   'B|4:1/12.0',
   'B|5:1/9.0',
   'B|6:1/6.0',
   'B|7:1/5.0'],
  653: ['A|14:1/9.0',
   'A|15:1/11.0',
   'A|16:1/12.0',
   'B|11:1/4.0',
   'B|2:1/4.0',
   'B|3:1/12.0',
   'B|4:1/12.0',
   'B|5:1/9.0',
   'B|6:1/6.0',
   'B|7:1/5.0'],
  807: ['A|10:1/13.0',
   'A|10:1/13.0',
   'A|3:1/6.0',
   'A|3:1/6.0',
   'A|4:1/2.0',
   'A|5:1/2.0',
   'A|6:1/5.0',
   'A|6:1/5.0',
   'A|7:1/10.0',
   'A|7:1/10.0'],
  825: ['A|10:1/13.0',
   'A|10:1/13.0',
   'A|3:1/6.0',
   'A|3:1/6.0',
   'A|5:1/2.0',
   'A|5:1/2.0',
   'A|6:1/5.0',
   'A|6:1/5.0',
   'A|7:1/10.0',
   'A|7:1/10.0'],
  778: ['A|10:1/13.0',
   'A|10:1/13.0',
   'A|3:1/6.0',
   'A|3:1/6.0',
   'A|5:1/2.0',
   'A|6:1/5.0',
   'A|6:1/5.0',
   'A|7:1/10.0',
   'A|7:1/10.0',
   'A|8:1/7.0'],
  816: ['A|10:1/13.0',
   'A|10:1/13.0',
   'A|3:1/6.0',
   'A|3:1/6.0',
   'A|5:1/2.0',
   'A|6:1/5.0',
   'A|6:1/4.0',
   'A|7:1/10.0',
   'A|7:1/10.0',
   'A|8:1/7.0'],
  822: ['A|10:1/13.0',
   'A|10:1/13.0',
   'A|3:1/6.0',
   'A|3:1/6.0',
   'A|5:1/2.0',
   'A|5:1/2.0',
   'A|6:1/5.0',
   'A|6:1/4.0',
   'A|7:1/10.0',
   'A|7:1/10.0'],
  849: ['A|10:1/13.0',
   'A|10:1/13.0',
   'A|3:1/6.0',
   'A|3:1/6.0',
   'A|5:1/3.0',
   'A|5:1/2.0',
   'A|6:1/5.0',
   'A|6:1/5.0',
   'A|7:1/10.0',
   'A|7:1/10.0'],
  820: ['A|10:1/13.0',
   'A|10:1/13.0',
   'A|3:1/6.0',
   'A|3:1/6.0',
   'A|5:1/5.0',
   'A|5:1/2.0',
   'A|6:1/5.0',
   'A|6:1/5.0',
   'A|7:1/10.0',
   'A|7:1/10.0'],
  754: ['A|10:1/13.0',
   'A|10:1/13.0',
   'A|3:1/6.0',
   'A|3:1/6.0',
   'A|5:1/3.0',
   'A|5:1/2.0',
   'A|6:1/5.0',
   'A|6:1/5.0',
   'A|7:1/10.0',
   'A|7:1/10.0'],
  1031: ['A|10:1/7.0',
   'A|12:1/2.0',
   'A|5:1/10.0',
   'A|5:1/2.0',
   'A|6:1/12.0',
   'A|6:1/11.0',
   'A|6:1/4.0',
   'A|7:1/9.0',
   'A|7:1/6.0',
   'A|9:1/2.0'],
  1094: ['A|10:1/7.0',
   'A|12:1/2.0',
   'A|5:1/9.0',
   'A|6:1/11.0',
   'A|6:1/4.0',
   'A|7:1/9.0',
   'A|7:1/4.0',
   'A|8:1/4.0',
   'A|8:1/3.0',
   'A|9:1/2.0'],
  1008: ['A|10:1/7.0',
   'A|12:1/2.0',
   'A|5:1/9.0',
   'A|5:1/4.0',
   'A|6:1/11.0',
   'A|6:1/4.0',
   'A|7:1/9.0',
   'A|7:1/9.0',
   'A|8:1/4.0',
   'A|9:1/2.0'],
  1089: ['A|10:1/7.0',
   'A|12:1/2.0',
   'A|5:1/9.0',
   'A|5:1/2.0',
   'A|6:1/11.0',
   'A|6:1/6.0',
   'A|7:1/9.0',
   'A|7:1/3.0',
   'A|8:1/4.0',
   'A|9:1/2.0'],
  1044: ['A|10:1/6.0',
   'A|10:1/6.0',
   'A|5:1/4.0',
   'A|5:1/4.0',
   'A|6:1/10.0',
   'A|6:1/9.0',
   'A|6:1/9.0',
   'A|7:1/8.0',
   'A|7:1/8.0',
   'A|8:1/3.0']}}

看起来像这样：

  unit    Date_job                                                                                                                   Vector
59    unit1  2021-06-07  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/3.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0] 
662   unit1  2021-06-14  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|5:1/8.0, B|6:1/5.0, B|7:1/5.0] 
680   unit1  2021-07-05  [A|14:1/9.0, A|14:1/4.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0]
725   unit1  2021-07-26  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/3.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0] 
709   unit1  2021-08-30  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/3.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0] 
703   unit1  2021-10-11  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/6.0, B|7:1/5.0] 
653   unit1  2021-10-18  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/6.0, B|7:1/5.0] 
807   unit4  2021-07-19  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|4:1/2.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]   
825   unit4  2021-07-26  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]   
778   unit4  2021-08-23  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0, A|8:1/7.0]   
816   unit4  2021-08-30  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|6:1/5.0, A|6:1/4.0, A|7:1/10.0, A|7:1/10.0, A|8:1/7.0]   
822   unit4  2021-09-06  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|5:1/2.0, A|6:1/5.0, A|6:1/4.0, A|7:1/10.0, A|7:1/10.0]   
849   unit4  2021-09-27  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/3.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]   
820   unit4  2021-10-04  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/5.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]   
754   unit4  2021-10-18  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/3.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]   
1031  unit3  2021-09-06  [A|10:1/7.0, A|12:1/2.0, A|5:1/10.0, A|5:1/2.0, A|6:1/12.0, A|6:1/11.0, A|6:1/4.0, A|7:1/9.0, A|7:1/6.0, A|9:1/2.0]    
1094  unit2  2021-07-26  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|6:1/11.0, A|6:1/4.0, A|7:1/9.0, A|7:1/4.0, A|8:1/4.0, A|8:1/3.0, A|9:1/2.0]      
1008  unit2  2021-08-09  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|5:1/4.0, A|6:1/11.0, A|6:1/4.0, A|7:1/9.0, A|7:1/9.0, A|8:1/4.0, A|9:1/2.0]      
1089  unit2  2021-10-04  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|5:1/2.0, A|6:1/11.0, A|6:1/6.0, A|7:1/9.0, A|7:1/3.0, A|8:1/4.0, A|9:1/2.0]      
1044  unit5  2021-06-14  [A|10:1/6.0, A|10:1/6.0, A|5:1/4.0, A|5:1/4.0, A|6:1/10.0, A|6:1/9.0, A|6:1/9.0, A|7:1/8.0, A|7:1/8.0, A|8:1/3.0]

最后一列是向量，这里写成列表。如您所见，对于同一单位，此向量会随时间变化。我希望能够标记每个单元行之间的变化。我知道如何为整个数据框执行此操作，但我无法将 groupby 应用于此。

以下是我知道的方法：

df2["VectorChanged"] = df2["Vector"].shift(1, fill_value=df2["Vector"].head(1)) != df2["Vector"]

但是，正如你所理解的，当单位改变时它不会改变向量（这是正确的但不适用，因为我们不是在看同一个单位）：

unit    Date_job                                                                                                                   Vector  VectorChanged
59    unit1  2021-06-07  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/3.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0]   False        
662   unit1  2021-06-14  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|5:1/8.0, B|6:1/5.0, B|7:1/5.0]   True         
680   unit1  2021-07-05  [A|14:1/9.0, A|14:1/4.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0]  True         
725   unit1  2021-07-26  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/3.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0]   True         
709   unit1  2021-08-30  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/3.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0]   False        
703   unit1  2021-10-11  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/6.0, B|7:1/5.0]   True         
653   unit1  2021-10-18  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/6.0, B|7:1/5.0]   False        
807   unit4  2021-07-19  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|4:1/2.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]     True         
825   unit4  2021-07-26  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]     True         
778   unit4  2021-08-23  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0, A|8:1/7.0]     True         
816   unit4  2021-08-30  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|6:1/5.0, A|6:1/4.0, A|7:1/10.0, A|7:1/10.0, A|8:1/7.0]     True         
822   unit4  2021-09-06  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|5:1/2.0, A|6:1/5.0, A|6:1/4.0, A|7:1/10.0, A|7:1/10.0]     True         
849   unit4  2021-09-27  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/3.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]     True         
820   unit4  2021-10-04  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/5.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]     True         
754   unit4  2021-10-18  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/3.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]     True         
1031  unit3  2021-09-06  [A|10:1/7.0, A|12:1/2.0, A|5:1/10.0, A|5:1/2.0, A|6:1/12.0, A|6:1/11.0, A|6:1/4.0, A|7:1/9.0, A|7:1/6.0, A|9:1/2.0]      True         
1094  unit2  2021-07-26  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|6:1/11.0, A|6:1/4.0, A|7:1/9.0, A|7:1/4.0, A|8:1/4.0, A|8:1/3.0, A|9:1/2.0]        True         
1008  unit2  2021-08-09  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|5:1/4.0, A|6:1/11.0, A|6:1/4.0, A|7:1/9.0, A|7:1/9.0, A|8:1/4.0, A|9:1/2.0]        True         
1089  unit2  2021-10-04  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|5:1/2.0, A|6:1/11.0, A|6:1/6.0, A|7:1/9.0, A|7:1/3.0, A|8:1/4.0, A|9:1/2.0]        True         
1044  unit5  2021-06-14  [A|10:1/6.0, A|10:1/6.0, A|5:1/4.0, A|5:1/4.0, A|6:1/10.0, A|6:1/9.0, A|6:1/9.0, A|7:1/8.0, A|7:1/8.0, A|8:1/3.0]        True

预期输出为：

unit    Date_job                                                                                                                   Vector  VectorChanged
59    unit1  2021-06-07  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/3.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0]   False        
662   unit1  2021-06-14  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|5:1/8.0, B|6:1/5.0, B|7:1/5.0]   True         
680   unit1  2021-07-05  [A|14:1/9.0, A|14:1/4.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0]  True         
725   unit1  2021-07-26  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/3.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0]   True         
709   unit1  2021-08-30  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/3.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/5.0, B|7:1/5.0]   False        
703   unit1  2021-10-11  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/6.0, B|7:1/5.0]   True         
653   unit1  2021-10-18  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/4.0, B|2:1/4.0, B|3:1/12.0, B|4:1/12.0, B|5:1/9.0, B|6:1/6.0, B|7:1/5.0]   False        
807   unit4  2021-07-19  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|4:1/2.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]     False         
825   unit4  2021-07-26  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]     True         
778   unit4  2021-08-23  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0, A|8:1/7.0]     True         
816   unit4  2021-08-30  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|6:1/5.0, A|6:1/4.0, A|7:1/10.0, A|7:1/10.0, A|8:1/7.0]     True         
822   unit4  2021-09-06  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/2.0, A|5:1/2.0, A|6:1/5.0, A|6:1/4.0, A|7:1/10.0, A|7:1/10.0]     True         
849   unit4  2021-09-27  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/3.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]     True         
820   unit4  2021-10-04  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/5.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]     True         
754   unit4  2021-10-18  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6.0, A|5:1/3.0, A|5:1/2.0, A|6:1/5.0, A|6:1/5.0, A|7:1/10.0, A|7:1/10.0]     True         
1031  unit3  2021-09-06  [A|10:1/7.0, A|12:1/2.0, A|5:1/10.0, A|5:1/2.0, A|6:1/12.0, A|6:1/11.0, A|6:1/4.0, A|7:1/9.0, A|7:1/6.0, A|9:1/2.0]      False         
1094  unit2  2021-07-26  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|6:1/11.0, A|6:1/4.0, A|7:1/9.0, A|7:1/4.0, A|8:1/4.0, A|8:1/3.0, A|9:1/2.0]        True         
1008  unit2  2021-08-09  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|5:1/4.0, A|6:1/11.0, A|6:1/4.0, A|7:1/9.0, A|7:1/9.0, A|8:1/4.0, A|9:1/2.0]        True         
1089  unit2  2021-10-04  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|5:1/2.0, A|6:1/11.0, A|6:1/6.0, A|7:1/9.0, A|7:1/3.0, A|8:1/4.0, A|9:1/2.0]        True         
1044  unit5  2021-06-14  [A|10:1/6.0, A|10:1/6.0, A|5:1/4.0, A|5:1/4.0, A|6:1/10.0, A|6:1/9.0, A|6:1/9.0, A|7:1/8.0, A|7:1/8.0, A|8:1/3.0]        False

即每个组的第一行 i False 因为没有可比较的东西，即没有变化。

如有任何帮助，我们将不胜感激。

Answer 1

像这样尝试使用 groupby 和 transform：

df["Vector Changed"] = df.groupby("unit")["Vector"].transform(lambda x: x!=x.shift().bfill())
df["Vector Changed"] = df["Vector Changed"].mask(df.groupby("unit")["Vector"].transform("count")==1, False)

>>> df

       unit    Date_job Vector  Vector Changed
59    unit1  2021-06-07    ...           False
662   unit1  2021-06-14    ...            True
680   unit1  2021-07-05    ...            True
725   unit1  2021-07-26    ...            True
709   unit1  2021-08-30    ...           False
703   unit1  2021-10-11    ...            True
653   unit1  2021-10-18    ...           False
807   unit4  2021-07-19    ...           False
825   unit4  2021-07-26    ...            True
778   unit4  2021-08-23    ...            True
816   unit4  2021-08-30    ...            True
822   unit4  2021-09-06    ...            True
849   unit4  2021-09-27    ...            True
820   unit4  2021-10-04    ...            True
754   unit4  2021-10-18    ...            True
1031  unit3  2021-09-06    ...           False
1094  unit2  2021-07-26    ...           False
1008  unit2  2021-08-09    ...            True
1089  unit2  2021-10-04    ...            True
1044  unit5  2021-06-14    ...           False

Answer 2

比较移动后的值，然后通过 unit 为第一个值设置 False 如果性能很重要，则使用 groupby 是不必要的：

df["Vector Changed"] = (df["Vector"].shift()
                                    .ne(df['Vector'])
                                    .where(df['unit'].duplicated(), False))

编辑：

如果可能，unit 列使用连续重复：

m = df["Vector"].shift().ne(df['Vector'])
g = df["unit"].shift().ne(df['unit']).cumsum()
df["Vector Changed"] = m.where(g.duplicated(), False)


print (df)
       unit    Date_job                                             Vector  \
59    unit1  2021-06-07  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/...   
662   unit1  2021-06-14  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/...   
680   unit1  2021-07-05  [A|14:1/9.0, A|14:1/4.0, A|15:1/11.0, A|16:1/1...   
725   unit1  2021-07-26  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/...   
709   unit1  2021-08-30  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/...   
703   unit1  2021-10-11  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/...   
653   unit1  2021-10-18  [A|14:1/9.0, A|15:1/11.0, A|16:1/12.0, B|11:1/...   
807   unit4  2021-07-19  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6....   
825   unit4  2021-07-26  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6....   
778   unit4  2021-08-23  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6....   
816   unit4  2021-08-30  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6....   
822   unit1  2021-09-06  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6....   <- again 1 groups
849   unit1  2021-09-27  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6....   <- again 1 groups 
820   unit1  2021-10-04  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6....   <- again 1 groups 
754   unit4  2021-10-18  [A|10:1/13.0, A|10:1/13.0, A|3:1/6.0, A|3:1/6....   <- again 4 groups 
1031  unit3  2021-09-06  [A|10:1/7.0, A|12:1/2.0, A|5:1/10.0, A|5:1/2.0...   
1094  unit2  2021-07-26  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|6:1/11.0...   
1008  unit2  2021-08-09  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|5:1/4.0,...   
1089  unit2  2021-10-04  [A|10:1/7.0, A|12:1/2.0, A|5:1/9.0, A|5:1/2.0,...   
1044  unit5  2021-06-14  [A|10:1/6.0, A|10:1/6.0, A|5:1/4.0, A|5:1/4.0,...   

      Vector Changed  
59             False  
662             True  
680             True  
725             True  
709            False  
703             True  
653            False  
807            False  
825             True  
778             True  
816             True  
822            False  
849             True  
820             True  
754            False  
1031           False  
1094           False  
1008            True  
1089            True  
1044           False

每次列中字符串更改时的布尔标记，按组

Boolean marker every time a string changes in a column, by group

python

pandas

pandas-groupby