我有一个涉及500个流感序列的距离矩阵。我想将其转换为柱状格式，具有250,000个成对比较。是否有一项功能可以让我快速完成这项工作？
以下是我正在使用的数据集。索引是“Accession”列，我将数据表示为Pandas DataFrame。
CY135678 CY142013 CY130339 CY130379 CY130460 CY135850 CY135930 CY143958 CY142889 CY141341 CY143073 CY142145 CY142817 CY142417 CY142985 CY136196 CY130412 CY135744 CY135326 CY135502
Accession
CY135678 1.000000 0.959670 0.937148 0.932813 0.972692 0.951452 0.996966 0.998266 0.953619 0.993498 0.920628 0.956635 0.921936 0.956030 0.902904 0.968791 0.998700 0.952319 0.917642 0.922440
CY142013 0.959670 1.000000 0.939289 0.936253 0.963573 0.973981 0.956635 0.957936 0.974848 0.954033 0.923245 0.976149 0.924117 0.975620 0.913270 0.960104 0.958369 0.974848 0.923244 0.925926
CY130339 0.937148 0.939289 1.000000 0.975389 0.942783 0.938256 0.934114 0.935847 0.940415 0.935233 0.930222 0.939722 0.930659 0.939051 0.917098 0.938882 0.935847 0.939119 0.927612 0.927233
CY130379 0.932813 0.936253 0.975389 1.000000 0.935847 0.936960 0.929779 0.931946 0.939119 0.931347 0.923681 0.935820 0.924553 0.935133 0.915371 0.932813 0.931513 0.938687 0.925444 0.920697
CY130460 0.972692 0.963573 0.942783 0.935847 1.000000 0.955787 0.969658 0.970958 0.957087 0.966623 0.921936 0.961839 0.922809 0.961254 0.907239 0.991764 0.971391 0.957087 0.917642 0.920697
CY135850 0.951452 0.973981 0.938256 0.936960 0.955787 1.000000 0.947984 0.949718 0.993092 0.946891 0.922372 0.973114 0.923245 0.972573 0.909758 0.953619 0.950152 0.996546 0.916775 0.925054
CY135930 0.996966 0.956635 0.934114 0.929779 0.969658 0.947984 1.000000 0.996099 0.950152 0.991331 0.919320 0.953599 0.920628 0.952982 0.900737 0.965756 0.996532 0.948851 0.914608 0.919390
CY143958 0.998266 0.957936 0.935847 0.931946 0.970958 0.949718 0.996099 1.000000 0.951886 0.992631 0.919756 0.954900 0.921064 0.954288 0.901170 0.967057 0.997833 0.950585 0.916775 0.921569
CY142889 0.953619 0.974848 0.940415 0.939119 0.957087 0.993092 0.950152 0.951886 1.000000 0.949050 0.922372 0.973981 0.923245 0.973444 0.912349 0.954920 0.952319 0.993092 0.918075 0.925490
CY141341 0.993498 0.954033 0.935233 0.931347 0.966623 0.946891 0.991331 0.992631 0.949050 1.000000 0.919756 0.951431 0.921064 0.950805 0.896805 0.963589 0.993065 0.947755 0.915908 0.925054
CY143073 0.920628 0.923245 0.930222 0.923681 0.921936 0.922372 0.919320 0.919756 0.922372 0.919756 1.000000 0.921500 0.999128 0.917139 0.908853 0.917139 0.920192 0.923245 0.942433 0.938945
CY142145 0.956635 0.976149 0.939722 0.935820 0.961839 0.973114 0.953599 0.954900 0.973981 0.951431 0.921500 1.000000 0.921936 0.999565 0.911969 0.957936 0.955334 0.973981 0.918040 0.923747
CY142817 0.921936 0.924117 0.930659 0.924553 0.922809 0.923245 0.920628 0.921064 0.923245 0.921064 0.999128 0.921936 1.000000 0.917575 0.909725 0.918011 0.921500 0.924117 0.942870 0.939817
CY142417 0.956030 0.975620 0.939051 0.935133 0.961254 0.972573 0.952982 0.954288 0.973444 0.950805 0.917139 0.999565 0.917575 1.000000 0.911189 0.957336 0.954724 0.973444 0.917283 0.923312
CY142985 0.902904 0.913270 0.917098 0.915371 0.907239 0.909758 0.900737 0.901170 0.912349 0.896805 0.908853 0.911969 0.909725 0.911189 1.000000 0.902904 0.901170 0.911917 0.900737 0.905011
CY136196 0.968791 0.960104 0.938882 0.932813 0.991764 0.953619 0.965756 0.967057 0.954920 0.963589 0.917139 0.957936 0.918011 0.957336 0.902904 1.000000 0.967490 0.954920 0.913741 0.916340
CY130412 0.998700 0.958369 0.935847 0.931513 0.971391 0.950152 0.996532 0.997833 0.952319 0.993065 0.920192 0.955334 0.921500 0.954724 0.901170 0.967490 1.000000 0.951019 0.916342 0.921133
CY135744 0.952319 0.974848 0.939119 0.938687 0.957087 0.996546 0.948851 0.950585 0.993092 0.947755 0.923245 0.973981 0.924117 0.973444 0.911917 0.954920 0.951019 1.000000 0.918075 0.925926
CY135326 0.917642 0.923244 0.927612 0.925444 0.917642 0.916775 0.914608 0.916775 0.918075 0.915908 0.942433 0.918040 0.942870 0.917283 0.900737 0.913741 0.916342 0.918075 1.000000 0.949455
CY135502 0.922440 0.925926 0.927233 0.920697 0.920697 0.925054 0.919390 0.921569 0.925490 0.925054 0.938945 0.923747 0.939817 0.923312 0.905011 0.916340 0.921133 0.925926 0.949455 1.000000
应用affmat.unstack()后得到的输出如下：
Accession
CY135678 CY135678 0.939085
CY142013 0.959670
CY130339 0.937148
CY130379 0.932813
CY130460 0.972692
CY135850 0.951452
CY135930 0.996966
CY143958 0.998266
CY142889 0.953619
CY141341 0.993498
CY143073 0.920628
CY142145 0.956635
CY142817 0.921936
CY142417 0.956030
CY142985 0.902904
...
CY135502 CY135850 0.925054
CY135930 0.919390
CY143958 0.921569
CY142889 0.925490
CY141341 0.925054
CY143073 0.938945
CY142145 0.923747
CY142817 0.939817
CY142417 0.923312
CY142985 0.905011
CY136196 0.916340
CY130412 0.921133
CY135744 0.925926
CY135326 0.949455
CY135502 0.939085
Length: 400, dtype: float64
从输出中可以看出，CY135678本身应该具有1.000000的身份，但在应用affmat.unstack()后变为0.939085。这种行为有解释吗？有没有什么方法可以让原始值正确堆叠？
答案 0 :(得分：1)
也许您正在寻找unstack：
In [29]: df
Out[29]:
a b c d e
a 0.453367 0.000969 0.199400 0.515258 0.610870
b 0.949461 0.002380 2.993674 1.357350 0.189058
c 0.117990 1.397985 0.093681 0.417855 0.686190
d 0.757732 4.975183 3.108177 0.019095 1.613240
e 0.022297 0.518517 0.006883 0.896779 0.485518
In [30]: df.unstack()
Out[30]:
a a 0.453367
b 0.949461
c 0.117990
d 0.757732
e 0.022297
b a 0.000969
b 0.002380
c 1.397985
d 4.975183
e 0.518517
c a 0.199400
b 2.993674
c 0.093681
d 3.108177
e 0.006883
d a 0.515258
b 1.357350
c 0.417855
d 0.019095
e 0.896779
e a 0.610870
b 0.189058
c 0.686190
d 1.613240
e 0.485518
显示每次成对比较两次。要削减它，请考虑Joe Kington's comment on this answer中讨论的np.triu_indicies_from。
答案 1 :(得分：0)
现在好了，不知怎的，我设法通过在导入数据时不将索引设置为'Accession'来解决问题。相反，我只在我真正需要索引为“加入”时才调用.set_index()函数。因此，当我想要堆叠数据时（而不是将其拆开），我最终做的是：
affmat_stacked = affmat.set_index('Accession').stack()
这似乎很好地解决了问题。再次感谢大家的所有提示！