将多列折叠(或合并为两个单独的列python

问题描述 投票:1回答:1

我有一个dataframe,如下所示:

8964_real  8964_imag   8965_real  8965_imag   8966_real  8966_imag   8967_real  ...  8984_imag   8985_real  8985_imag   8986_real  8986_imag   8987_real  8987_imag
0  112.653120   0.000000  117.104887   0.000000  127.593406   0.000000  129.522106  ...   0.000000  125.423552   0.000000  127.888477   0.000000  136.160979   0.000000
1   -0.315831  16.363974   -2.083329  22.443628   -2.166950  15.026253    0.110502  ... -26.613220    8.454297 -35.000742   11.871405 -24.914035    7.448329 -16.370041
2   -1.863497  10.672129   -6.152232  15.980813   -5.679352  18.976117   -5.775777  ... -11.131600  -18.990022  -9.520732  -11.947319  -4.641286  -17.104710  -5.691642
3   -6.749938  14.870590  -12.222749  15.012352  -10.501423   9.345518   -9.103459  ...  -2.860546  -29.862724  -5.237663  -28.791194  -5.685985  -24.565608 -10.385683
4   -2.991405 -10.332938   -4.097638 -10.204587  -12.056221  -5.684882  -12.861357  ...   0.821902   -8.787235  -1.521650   -3.798446  -2.390519   -6.527762  -1.145998

我必须转换上面的数据框,以使"_real"列中的值应位于一列下,而"_imag"之下的值应位于另一列下

那末尾应该总有两列,其中一列是real and other for imag。最有效的方法是什么?

我将其称为link。但这对于一列是好的,但是我需要两列。我得到的另一个想法是使用regex to select columns containing "real"并按照上面的链接所述进行操作(对于imag同样如此),但是感觉有些不对头。

感谢任何帮助。

编辑:例如,real应该类似于

real
112.653120
-0.315831
-1.863497
-6.749938
-2.991405
---------
117.104887
-2.083329
-6.152232
-12.222749
-4.097638
---------
127.593406
-2.166950
-5.679352
-10.501423
-12.056221

我用虚线表示清楚

python pandas dataframe multiple-columns concat
1个回答
3
投票

通过MultiIndex创建split,因此可以通过DataFrame.stack重塑形状:

DataFrame.stack

df.columns = df.columns.str.split('_', expand=True)

print (df.head(10))
         8964                   8965                   8966             \
         real       imag        real       imag        real       imag   
0  112.653120   0.000000  117.104887   0.000000  127.593406   0.000000   
1   -0.315831  16.363974   -2.083329  22.443628   -2.166950  15.026253   
2   -1.863497  10.672129   -6.152232  15.980813   -5.679352  18.976117   
3   -6.749938  14.870590  -12.222749  15.012352  -10.501423   9.345518   
4   -2.991405 -10.332938   -4.097638 -10.204587  -12.056221  -5.684882   

         8967       8984        8985                   8986             \
         real       imag        real       imag        real       imag   
0  129.522106   0.000000  125.423552   0.000000  127.888477   0.000000   
1    0.110502 -26.613220    8.454297 -35.000742   11.871405 -24.914035   
2   -5.775777 -11.131600  -18.990022  -9.520732  -11.947319  -4.641286   
3   -9.103459  -2.860546  -29.862724  -5.237663  -28.791194  -5.685985   
4  -12.861357   0.821902   -8.787235  -1.521650   -3.798446  -2.390519   

         8987             
         real       imag  
0  136.160979   0.000000  
1    7.448329 -16.370041  
2  -17.104710  -5.691642  
3  -24.565608 -10.385683  
4   -6.527762  -1.145998  

编辑:

df = df.stack(0).reset_index(level=0, drop=True).rename_axis('a').reset_index()
print (df.head(10))
      a       imag        real
0  8964   0.000000  112.653120
1  8965   0.000000  117.104887
2  8966   0.000000  127.593406
3  8967        NaN  129.522106
4  8984   0.000000         NaN
5  8985   0.000000  125.423552
6  8986   0.000000  127.888477
7  8987   0.000000  136.160979
8  8964  16.363974   -0.315831
9  8965  22.443628   -2.083329
© www.soinside.com 2019 - 2024. All rights reserved.