我在执行任务中遇到麻烦。在我的第一种情况中，我需要比较数据框中的一些变量，如果它们相同，它将返回相同的标识符列值。
这是我的多个排序数据框，看起来像
| no | age| gender | income_group | cars
| 1 | 15 | male | 0 | ford
| 2 | 15 | male | 0 | renault
| 3 | 15 | female| 1 | bmw
| 4 | 16 | female| 1 | bmw
| 5 | 16 | female| 1 | mercedes
| 6 | 16 | female| 1 | honda
我想要一些代码来比较此排序的数据帧上的每一行，如果某些行的[年龄，性别，收入组]相同，它将复制第一个[no]列的值来替换其他
代码将使我的数据框看起来像这样
| no | age| gender | income_group | cars
| 1 | 15 | male | 0 | ford
| 1 | 15 | male | 0 | renault
| 3 | 15 | female| 1 | bmw
| 4 | 16 | female| 1 | bmw
| 4 | 16 | female| 1 | mercedes
| 4 | 16 | female| 1 | honda
在python中有没有办法做到这一点？
已编辑： 我的第二种情况变得更加复杂，因为我发现一些相同的[年龄，性别，收入组]变量但具有相同的[汽车]值，因此我希望在这种情况下将其视为不同的个体，而不同[否]值
如果展开数据框并获得一个像这样的列
| no | age| gender | income_group | cars
| 1 | 15 | male | 0 | ford
| 2 | 15 | male | 0 | renault
| 3 | 15 | female| 1 | bmw
| 4 | 16 | female| 1 | bmw
| 5 | 16 | female| 1 | mercedes
| 6 | 16 | female| 1 | honda
| 7 | 17 | male | 0 | bmw
| 8 | 17 | male | 0 | honda
| 9 | 17 | male | 0 | bmw
| 10 | 17 | male | 0 | honda
| 11 | 17 | male | 0 | renault
一个人不能拥有相同的汽车价值，该代码将成为df：
| 7 | 17 | male | 0 | bmw
| 7 | 17 | male | 0 | honda
| 9 | 17 | male | 0 | bmw
| 9 | 17 | male | 0 | honda
| 9 | 17 | male | 0 | renault
jezrael的解决方案：
df['a'] = df.duplicated(['age','gender','income_group', 'cars'], keep=False).cumsum()
df['no'] = df.groupby(['age','gender','income_group','a'], sort=False)['no'].transform('first')
df = df.drop('a', axis=1)
我得到：
no age gender income_group cars a
0 15 male 0 ford 0
0 15 male 0 renault 0
2 15 female 1 bmw 0
3 16 female 1 bmw 0
3 16 female 1 mercedes 0
3 16 female 1 honda 0
6 17 male 0 bmw 1
7 17 male 0 honda 2
8 17 male 0 bmw 3
9 17 male 0 honda 4
9 17 male 0 reanult 4
答案 0 :(得分：6)
将GroupBy.transform与GroupBy.first一起使用：
df['no'] = df.groupby(['age','gender','income_group'], sort=False)['no'].transform('first')
print (df)
no age gender income_group cars
0 1 15 male 0 ford
1 1 15 male 0 renault
2 3 15 female 1 bmw
3 4 16 female 1 bmw
4 4 16 female 1 mercedes
5 4 16 female 1 honda
或者通过DataFrame.duplicated获取第一个值，然后向前填充缺少的值：
df['no'] = df.loc[(~df.duplicated(['age','gender','income_group'])), 'no']
df['no'] = df['no'].ffill().astype(int)
print (df)
no age gender income_group cars
0 1 15 male 0 ford
1 1 15 male 0 renault
2 3 15 female 1 bmw
3 4 16 female 1 bmw
4 4 16 female 1 mercedes
5 4 16 female 1 honda
编辑：
df['a'] = df.duplicated(['age','gender','income_group', 'cars'])
mask = df.groupby(['age','gender','income_group'])['a'].transform('any')
df.loc[mask, 'no'] = df.groupby(df.loc[mask].groupby('cars').cumcount(ascending=False))['no'].transform('first')
df = df.drop('a', axis=1)
print (df)
no age gender income_group cars
0 1.0 15 male 0 ford
1 2.0 15 male 0 renault
2 3.0 15 female 1 bmw
3 4.0 16 female 1 bmw
4 5.0 16 female 1 mercedes
5 6.0 16 female 1 honda
6 7.0 17 male 0 bmw
7 7.0 17 male 0 honda
8 9.0 17 male 0 bmw
9 9.0 17 male 0 honda
10 9.0 17 male 0 reanult