python - Pandas 按3个变量分组，但总和超过2个

我有一个数据框：

In [59]: df.head()
Out[59]:

       src-subnet        dst-subnet  bytes       Start
0             ATL          Internet    180  1477252800
1             ATL               ATL    206  1477252800
2             ATL  Private-10.0.0.0  17160  1477252800
3             SAT          Internet  15768  1477252800
4  AWS-CloudFront               ATL   1592  1477252800

对于每个start值，我希望在每对src-subnet和dst-subnet之间传输的字节之和。

我写了以下内容：

g_start = df.groupby('Start')
g_start_subnet = g_start.apply(lambda x: x.groupby(['src-subnet', 'dst-subnet']).sum())

我获得了：

```

In [60]: g_start_subnet
Out[60]:

                                                               bytes
Start      src-subnet          dst-subnet
1476896400 ATL                 ATL                        5190647907
                               AWS                             67442
                               AWS-CloudFront                3523866
                               AWS-EC2                      10231320
                               Broadcast-255.255.255.255      550470
                               COL                          25459487
                               Corvil-CNE                       5130
                               DEN                          68247258
                               DFB                          49930518
                               DFW                         109329393
                               HOU                          43601191
                               IND                          97444445
                               Internal Network              2295849
                               Internet                     72555057
                               JES                          64789642
                               LAF                          30442304
                               LOU                           27372802
                               MGO                            307340
                               Multicast-224.0.0.0          25339730
                               NOL                          49526023
                               Private-10.0.0.0            814280925
                               Private-172.16.0.0            9348122
                               Private-192.168.0.0           8907521
                               SAT                          43590577
                               SCI                           2473674
                               WMSApp                       74085307
                               WMSDB                         9604726
           AWS                 ATL                             78309
           AWS-CloudFront      ATL                          10026686
                               HOU                              9285
...                                                              ...
1477497600 Private-10.0.0.0    Internet                     90581028
                               Link-169.254.0.0                  276
                               Private-10.0.0.0            159103776
                               Private-192.168.0.0          60724723
                               WMSApp                           1100
           Private-100.64.0.0  ATL                           1304731
           Private-192.168.0.0 ATL                             77031
                               Internet                          926
                               Multicast-224.0.0.0            238220
                               Private-10.0.0.0                19320
                               Private-192.168.0.0            393982
           SAT                 ATL                          18278815
                               AWS-EC2                         28380
                               Internet                     42773386
                               Private-10.0.0.0              8030664
                               Private-172.16.0.0             230389
                               Private-192.168.0.0           5938773
           SCI                 ATL                           2055407
                               Internet                          900
                               Multicast-224.0.0.0               256
           Solace-Servers      Broadcast-255.255.255.255      153438
           WMSApp              ATL                          95861882
                               AWS-EC2                           360
                               DFB                              3066
                               DFW                              9000
                               Internet                         3330
                               Multicast-224.0.0.0          31292117
                               Private-10.0.0.0                 1022
           WMSDB               ATL                           8678562
                               Internet                         2160

我怎么知道删除多索引并获得像dataframe这样的矩阵？

最终结果应具有列Start, bytes, src-subnet, dst-subnet

最佳答案

我认为您可以使用：

g_start_subnet = g_start_subnet.reset_index()

但是使用可能更好：

print (df.groupby(['Start', 'src-subnet', 'dst-subnet'], as_index=False).sum())

关于python - Pandas 按3个变量分组，但总和超过2个，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/40678742/