更改名称并创建新的多列

Question

多索引的语法并不容易发现。 在我的例子中，考虑到这个数据集：

header = pd.MultiIndex.from_product([['topic1'],
                                     ['location1','location2'],
                                     ['S1','S2','S3']],
                                    names=['top', 'loc','S'])
df = pd.DataFrame(np.random.randn(5, 6), 
                  index=['a','b','c','d','e'], 
                  columns=header)
df

产生：

top topic1
loc location1   location2
S   S1  S2  S3  S1  S2  S3
a   -0.235613   1.064278    -2.147621   0.825380    -0.443313   -1.064031
b   0.404703    0.830838    -0.294387   -1.438028   0.836324    -2.427235
c   0.486648    -0.091448   1.246530    -0.005375   0.159478    -0.103404
d   -0.638070   -1.057061   0.596882    -1.007059   -0.654583   -0.618137
e   -0.850887   -1.660056   0.129954    1.204890    -1.457207   0.678393

我想：

1. 例如，将“top”和“loc”重命名为“aaa”、“bbb”
2. 克隆整个主题1，以便创建一个新的多列“topic2”，例如，其中的值是x2

Answer 1

您可以使用带有参数keys的^{}来定义MultiIndex的顶层，并使用^{}或赋值来更改names列：

df = (pd.concat([df['topic1'], df['topic1'] * 2], keys=('topic1','topic2'), axis=1)
        .rename_axis(('aaa','bbb', df.columns.names[2]), axis=1))

备选方案：

df = pd.concat([df['topic1'], df['topic1'] * 2], keys=('topic1','topic2'), axis=1)
df.columns.names = ('aaa','bbb', df.columns.names[2])

print (df)
aaa    topic1                                                      topic2  \
bbb location1                     location2                     location1   
S          S1        S2        S3        S1        S2        S3        S1   
a    0.511604 -0.217660 -0.521060  1.253270  1.104554 -0.770309  1.023207   
b    0.632975 -1.322322 -0.936332  0.436361  1.233744  0.527565  1.265951   
c   -0.369576  1.820059 -1.373630 -0.414554 -0.098443  0.904791 -0.739151   
d    1.656726 -0.972017 -0.300689 -0.179819  0.472515  2.379975  3.313453   
e   -0.053210 -0.180697  0.176240 -1.087404 -1.012181 -0.049870 -0.106421   

aaa                                                    
bbb                     location2                      
S          S2        S3        S1        S2        S3  
a   -0.435320 -1.042120  2.506541  2.209108 -1.540617  
b   -2.644644 -1.872664  0.872723  2.467488  1.055129  
c    3.640118 -2.747261 -0.829108 -0.196885  1.809582  
d   -1.944034 -0.601379 -0.359638  0.945030  4.759950  
e   -0.361395  0.352480 -2.174809 -2.024362 -0.099739