使用python和pandas按列将2个数据帧拼接在一起

Question

我有两个熊猫数据帧

df1：

           Genes         N1         N2        N3         N4        N5  \
1      100130426          0          0    0.2262          0         0   
2      100133144     6.0377     4.3819   15.9742     4.5751   14.5776   
3      100134869     3.9512     2.3768   12.3047     5.6267    4.8288   
4          10357   197.2475    87.8119   78.7874   328.9158  113.7614   
5          10431  1615.9822  1645.1704   769.866   722.7625  863.5845  

df2：

           Genes        T1        T2        T3        T4        T5        T6  \
1      100130426         0         0         0         0         0         0   
2      100133144    4.0315    1.4705         0    8.2678    5.3467    3.1702   
3      100134869   10.9554     9.111    7.5432    9.0772    8.2126    5.9363   
4          10357  128.3177  129.6157  144.8024    108.58  107.5162  153.8304   
5          10431   659.423  835.5713   900.873  878.8159  433.5901  579.3967 

如何将这些文件以这种格式输出到单个csv？注意，“Genes”列保持不变，N列与对应的T列相邻

           Genes         N1         T1        N2         T2        N3  \
1      100130426          0          0    0.2262          0         0   
2      100133144     6.0377     4.0315   15.9742     1.4705   14.5776   
3      100134869     3.9512    10.9554   12.3047      9.111    4.8288   
4          10357   197.2475   128.3177   78.7874   129.6157  113.7614   
5          10431  1615.9822    659.423   769.866   835.5713  863.5845  and so on...

我有25列数据，所以我希望输出列的顺序如下：

[‘基因’、‘N1’、‘T1’、‘N2’、‘T2’、‘N3’、‘T3’、‘N4’、‘T4’、、…、'N23'，'T23'，'N24'，'T24'，'N25'，'T25']

Answer 1

你可以做：

merged = df1.merge(df2).set_index('Genes')

merged = merged[sorted(merged.columns,key=lambda x: int(x[1:]))].reset_index()

这将按照除gene列之外的所有列上的字母后的数字对列进行排序：

       Genes         N1        T1         N2        T2        N3        T3  \
0  100130426     0.0000    0.0000     0.0000    0.0000    0.2262    0.0000   
1  100133144     6.0377    4.0315     4.3819    1.4705   15.9742    0.0000   
2  100134869     3.9512   10.9554     2.3768    9.1110   12.3047    7.5432   
3      10357   197.2475  128.3177    87.8119  129.6157   78.7874  144.8024   
4      10431  1615.9822  659.4230  1645.1704  835.5713  769.8660  900.8730   

         N4        T4        N5        T5        T6  
0    0.0000    0.0000    0.0000    0.0000    0.0000  
1    4.5751    8.2678   14.5776    5.3467    3.1702  
2    5.6267    9.0772    4.8288    8.2126    5.9363  
3  328.9158  108.5800  113.7614  107.5162  153.8304  
4  722.7625  878.8159  863.5845  433.5901  579.3967