python pandas嵌套循环：将一个函数应用于每个元素，例如第2列中涉及到前一列1s中相同元素的复合

Question

我对pandas/data-frame编程还不熟悉，还没有掌握实现等效的简单循环。在

我基本上要实现的是这样的：

counter = 0
for port in portfolios:
    for bin in bins:
        value[port[counter +1, bin]] 
        = port[counter + 1, bin] * port[counter, bin]
counter += 1

实际上，使用下面的一个示例表，对于每个portfolio端口中的每个bin，我尝试将rebal_wgt [port=i,bin=j]乘以rebal_wgt[port=i-1,bin=j]。我想对所有公文包端口（共有11个）和每个公文包端口中的所有bin都这样做，这样每个公文包的对应bin将乘以以前的公文包对应的bin（例如port5 bin 1 rebal_weight * port 4 bin 1's rebal_weight等）

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事实上，我认为我要达到的是一个累积的结果。
从上面看

^{pr2}$

因此，任何值[port，bin]都可以表示为具有值[port-1，bin]的乘积。因此，从value[0，bin]开始，可以进行累加积，从而得出每个bin的连续投资组合的值。这就是我想做的！不幸的是，我不认为下面给出的第一个答案能做到这一点，因为它并没有为每个bin循环投资组合，它似乎在每个投资组合中循环使用bin。非常感谢任何帮助！下面是一个示例数据表，忽略累计和存量。在

     port  bin   cum_ret_sum  stocks     rebal_wgt  
  0       0    0  4.067563e+03     216  1.883131e+01  
  1       0    1  1.300282e+04     213  6.104612e+01  
  2       0    2  1.426061e+04     214  6.663837e+01  
  3       0    3  4.904957e+02     205  2.392662e+00  
  4       0    4  1.100993e+04     209  5.267908e+01  
  5       0    5  4.630904e+03     208  2.226396e+01  
  6       0    6  1.019425e+04     215  4.741514e+01  
  7       0    7  2.249585e+04     213  1.056143e+02  
  8       0    8  8.831653e+03     214  4.126941e+01  
  9       0    9  3.098015e+05     212  1.461328e+03  
  10      1    0  1.881155e+00     267  7.045525e-03  
  11      1    1  7.486650e+00     280  2.673804e-02  
  12      1    2  4.492010e+00     268  1.676123e-02  
  13      1    3  1.191500e+01     273  4.364468e-02  
  14      1    4  4.388776e+00     266  1.649916e-02  
  15      1    5  1.384601e+01     270  5.128153e-02  

bin=0的示例输出（希望所有bin=0到bin）

port    bin rebal_wgt   rebal_wgt(port[counter,bin] * port[counter - 1, bin])
0   0   18.83131173675383   18.83131173675383
0   1   61.04612379316069   
0   2   66.63837376523843   
0   3   2.3926618103462602  
0   4   52.67907609709447   
0   5   22.26396127204363   
0   6   47.415137683181634  
0   7   105.61432751079496  
0   8   41.26940808193825   
0   9   1461.3276714958988  
1   0   0.0070455247063908755   0.13267647209504757
1   1   0.026738035817712412    
1   2   0.01676122975460385 
1   3   0.04364467915381678 
1   4   0.016499159440430355    
1   5   0.051281526270788164    
1   6   0.04977016623588389 
1   7   0.0645014820724396  
1   8   0.1438106018214078  
1   9   0.0340451076286303  
2   0   5.1196753262692285  0.6792604605614628
2   1   0.014870173557215314    
2   2   3.263374203937453   
2   3   73.32640040253595   
2   4   3.915173886409575   
2   5   67.46028895344207   
2   6   7.654613865824991   
2   7   12.837204120226547  
2   8   2983.065107673766   
2   9   0.4204701203425892  
3   0   71.16936245719319   48.34253392053873

实际期望输出：

port    bin rebal_wgt   rebal_wgt(port[counter,bin] * port[counter - 1, bin])
0   0   18.83131173675383   18.83131173675383
0   1   61.04612379316069   61.04612379316069
0   2   66.63837376523843   66.63837376523843
0   3   2.3926618103462602  2.3926618103462602
0   4   52.67907609709447   52.67907609709447
0   5   22.26396127204363   22.26396127204363
0   6   47.415137683181634  47.415137683181634
0   7   105.61432751079496  105.61432751079496
0   8   41.26940808193825   41.26940808193825
0   9   1461.3276714958988  1461.3276714958988
1   0   0.0070455247063908755   0.13267647209504757
1   1   0.026738035817712412    1.6322534445140364
1   2   0.01676122975460385 1.116941093152327
1   3   0.04364467915381678 0.10442695703615294
1   4   0.016499159440430355    0.8691604757005253
1   5   0.051281526270788164    1.1417299148641158
1   6   0.04977016623588389 2.3598592845892727
1   7   0.0645014820724396  6.812280652530306
1   8   0.1438106018214078  5.934978413076811
1   9   0.0340451076286303  49.75105785677358
2   0   5.1196753262692285  0.6792604605614628
2   1   0.014870173557215314    0.024271892009286238
2   2   3.263374203937453   3.6449967507110035
2   3   73.32640040253595   7.65725286445137
2   4   3.915173886409575   3.4029143975620206
2   5   67.46028895344207   77.02142996352207
2   6   7.654613865824991   18.06381160121289
2   7   12.837204120226547  87.45063726080163
2   8   2983.065107673766   17704.427018846454
2   9   0.4204701203425892  20.918833284208702

Answer 1

累计操作

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In [5]:
df.groupby(df.bin)['rebal_wgt'].cumprod()
Out[5]:
0       18.831310
1       61.046120
2       66.638370
3        2.392662
4       52.679080
5       22.263960
6       47.415140
7      105.614300
8       41.269410
9     1461.328000
10       0.132676
11       1.632254
12       1.116941
13       0.104427
14       0.869161
15       1.141730
dtype: float64