分组列的访问

Question

我有一张这样的桌子：

 Bank        Our Credit Rating      External Credit Rating       Deviation
 A             11                      12                          1
 D             10                      8                           2
 A             4                       4                           0
 B             6                       7                           1
 C             12                      11                          1
 A             9                       10                          1

所有偏差总和为>=提取50个。我已经通过上面给出的代码做了同样的事情

输出：

   [IN]
   workbbok = pd.read_csv("Credit_Rating_comparison.csv")
   df33= workbook.groupby('Bank').aggregate({"Deviation":np.sum})
   df44=df33[df33['Deviation']>=50]
   [OUT]
    Bank                                      Deviation                                  
    B                                          68.0
    A                                          72.0

    and so on for the relevant banks. (Basically sum of all deviations for 
    one bank where sum of all deviations is at least 50)

我无法访问第1列，即df44中所有银行的名称

    [IN]: df44.columns
    [OUT]: Index(['Deviation'], dtype='object')
    [IN]: df44.iloc[:,0]
    [OUT]
     Bank                                      
     B                                          68.0
     A                                          72.0
     #Using df44.iloc[:,0] doesnt give column name deviation also and 
     returns deviation results along with Bank name.  I want only bank names list. 

基本上，我只需要一个名单的银行名称（没有偏差的总和），以便我可以进一步使用下面的操作列表

在我得到所有银行的名字之后，我需要找到偏差列的频率分布

下面的代码给出了对应于所有行的频率bin。我只想提取df44['bank']中bank name所在的行。任何帮助都将不胜感激

     [IN]:
     bins = [0, 1,2,3,4,5]
     workbook['Deviation Bins'] = pd.cut(workbook['Deviation'], bins, 
     include_lowest =True)
     workbook 
     [OUT]:
 Bank   Our Credit Rating  External Credit Rating Deviation  Deviation Bins
 A             11                      12              1        (-inf.,1]
 D             10                      8               2        (1,2]
 A             4                       4               0        (-inf.,1]
 B             6                       7               1        (-inf.,1]
 C             12                      11              1        (-inf.,1]  
 A             9                       10              1        (-inf.,1]

Answer 1

应用.aggregate()时，组进入返回数据帧的索引，而不是列。您可以将索引转换为新列，例如：

df33['Bank'] = df33.index

然后可以筛选出感兴趣的组：

df44=df33[df33['Deviation']>=50]

对于第二部分，您需要使用.isin()：

workbook[workbook['Bank'].isin(df44['Bank'])]