Question

从每个组的最大客户数量中提取groupby之后的第二个索引的有效方法是什么。

假设一个数据框df具有各种状态，并且在每个状态下有10个管理人员（名称为Officer 1至Officer 10）。列Current Status将始终具有值Customer：

    State List  Sales Officer   Current Status
0   UP          Officer 4       Customer
1   MH          Officer 5       Customer
2   AP          Officer 6       Customer
3   AN          Officer 2       Customer
4   GJ          Officer 3       Customer
.... so on

预期输出由在每个州中拥有最高客户数量的销售人员组成：

State List     Sales Officer      
AN             Officer 6          403
AP             Officer 1          266
               Officer 8          266
... and so on

到目前为止，我已经执行了以下操作：

df.groupby(['State List', 'Sales Officer'])['Current Status'].count()#.reset_index()

给我以下内容：

State List  Sales Officer
AN          Officer 1        376
            Officer 10       401
            Officer 2        353
            Officer 3        373
            Officer 4        375
            Officer 5        382
            Officer 6        403
            Officer 7        400
            Officer 8        385
            Officer 9        378
AP          Officer 1        266
            Officer 10       228
            Officer 2        240
            Officer 3        248
            Officer 4        235
            Officer 5        229
            Officer 6        242
            Officer 7        238
            Officer 8        266
            Officer 9        243

现在，我陷入了以每个客户Sales Officer的最大数量吸引State List的困境。任何想法！

Answer 1

将boolean indexing与max中的transform一起使用，返回Series的大小与原始大小相同：

s = df.groupby(['State List', 'Sales Officer'])['Current Status'].count()
df = s[s == s.groupby('State List').transform('max')]
print (df)
State List  Sales Officer
AN          Officer 6        403
AP          Officer 1        266
            Officer 8        266
Name: a, dtype: int64

详细信息：

print (s.groupby('State List').transform('max'))
State List  Sales Officer
AN          Officer 1        403
            Officer 10       403
            Officer 2        403
            Officer 3        403
            Officer 4        403
            Officer 5        403
            Officer 6        403
            Officer 7        403
            Officer 8        403
            Officer 9        403
AP          Officer 1        266
            Officer 10       266
            Officer 2        266
            Officer 3        266
            Officer 4        266
            Officer 5        266
            Officer 6        266
            Officer 7        266
            Officer 8        266
            Officer 9        266
Name: a, dtype: int64

从groupby熊猫中获取MultiIndex

1 个答案: