Python 将多行按列值合并为一行，并根据多个列的连接行数拆分为多个数据帧

以下是本SO问题的后续内容：

它显示了在有一列要合并，另一列要合并的情况下如何合并行

我现在正在寻找一种解决方案，用于有许多列的情况，并且我仍然希望基于一列合并行

我希望这样处理：首先列出一种类型的所有列，然后按照与第一次相同的顺序列出另一种类型的列

这里有一个最小的例子

data = [['tom', 'ca', 2], ['ni2ck', 'ma', 2], ['j3uli', 'ny', 4] , ['nic4k', 'ma', 4], ['jul5i', 'ny', 4] , ['nic6k', 'ma', 7], ['ju7li', 'ny', 7] , ['nic8k', 'ma', 7], ['ju9li', 'ny', 7] , ['nic1k', 'ma', 8], ['car', 'ny', 8]]
df = pd.DataFrame(data, columns = ['Name', 'Location', 'Age']) 
df 

结果是

Name    Location    Age
0   tom ca  2
1   ni2ck   ma  2
2   j3uli   ny  4
3   nic4k   ma  4
4   jul5i   ny  4
5   nic6k   ma  7
6   ju7li   ny  7
7   nic8k   ma  7
8   ju9li   ny  7
9   nic1k   ma  8
10  car ny  8

这将是预期的结果

    Name    Name    Location    Location    Age
0   tom ni2ck   ca  ma  2
1   nic1k   car ma  ny  8


Name    Name    Name    Location    Location    Location    Age
0   j3uli   nic4k   jul5i   ny  ma  ny  4


Name    Name    Name    Name    Location    Location    Location    Location    Age
0   nic6k   ju7li   nic8k   ju9li   ma  ny  ma  ny  7

---

重要的是，正确的位置与相应的名称顺序相同

从@Wen解决方案发展而来。使用

pivot\u表代替
pivot

df['New']=df.groupby('Age').cumcount()
s= df.pivot_table(index='Age',columns='New',
                  values=['Name', 'Location'], 
                  aggfunc='first').reindex(['Name', 'Location'], axis=1, level=0)
s.columns = s.columns.map('{0[0]}{0[1]}'.format)

l=[y.dropna(1).reset_index() for _ , y in s.groupby(s.isnull().sum(1))]

In [499]: l[0]
Out[499]:
   Age  Name0  Name1  Name2  Name3 Location0 Location1 Location2 Location3
0    7  nic6k  ju7li  nic8k  ju9li        ma        ny        ma        ny

In [500]: l[1]
Out[500]:
   Age  Name0  Name1  Name2 Location0 Location1 Location2
0    4  j3uli  nic4k  jul5i        ny        ma        ny

In [501]: l[2]
Out[501]:
   Age  Name0  Name1 Location0 Location1
0    2    tom  ni2ck        ca        ma
1    8  nic1k    car        ma        ny


如果要保留多索引列，请跳过列上的
map
命令

df['New']=df.groupby('Age').cumcount()
s= df.pivot_table(index='Age',columns='New',
                  values=['Name', 'Location'], 
                  aggfunc='first').reindex(['Name', 'Location'], axis=1, level=0)

l=[y.dropna(1).reset_index() for _ , y in s.groupby(s.isnull().sum(1))]

In [544]: l[0]
Out[544]:
    Age   Name                      Location
New          0      1      2      3        0   1   2   3
0     7  nic6k  ju7li  nic8k  ju9li       ma  ny  ma  ny

In [545]: l[1]
Out[545]:
    Age   Name               Location
New          0      1      2        0   1   2
0     4  j3uli  nic4k  jul5i       ny  ma  ny

In [546]: l[2]
Out[546]:
    Age   Name        Location
New          0      1        0   1
0     2    tom  ni2ck       ca  ma
1     8  nic1k    car       ma  ny

您是否可以在编辑之前包含原始解决方案。多级标题对于我的具体案例更为有用。