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从groupby pandas中提取多索引

series group-by pandas python

sync11 · 技术社区 · 7 年前

提取后第二个索引的有效方法是什么 groupby 根据每组的最大客户计数。

假设一个数据帧 df 有不同的州和10名官员(姓名 Officer 1 到 Officer 10 )在每个州。专栏 Current Status 总是有价值的 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

预期产出包括每个州客户数最高的销售员:

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()

给我以下信息:

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,我一直在寻找 Sales Officer 反对每个 State List 最大客户数。任何想法!

1 回复 | 直到 7 年前

jezrael 7 年前

使用 boolean indexing 具有 transform 属于 max 为了回报 Series 与原尺寸相同:

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

细节 :

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

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