python之如何在python中组合两个整数列

我想将 2 个具有整数的列值与它们之间的“_”组合起来，并将其设置为我的输出数据集的索引列。 'ID' 将是我的索引。

示例数据:

import pandas as pd 
import numpy as np 
import io 
 
data = ''' 
ID,Ang,1 
23,0,0.88905321 
23,10,0.962773412 
23,20,1.004187813 
23,30,1.008301223 
105,0,0.334209544 
105,10,0.39043363 
105,20,0.434241204 
105,30,0.460348427 
47,0,0.020669404 
47,10,0.032299446 
47,20,0.050602654 
47,30,0.073371391 
''' 
df = pd.read_csv(io.StringIO(data),index_col=0) 

预期输出:

请您参考如下方法:

将索引和列转换为字符串并通过_连接，也是DataFrame.pop用于提取列，因此 drop 不是必需的:

df.index = df.index.astype(str) + '_' + df.pop('Ang').astype(str) 

或者使用DataFrame.set_index :

df = df.set_index(df.index.astype(str) + '_' + df.pop('Ang').astype(str)) 

---

print (df) 
               1 
23_0    0.889053 
23_10   0.962773 
23_20   1.004188 
23_30   1.008301 
105_0   0.334210 
105_10  0.390434 
105_20  0.434241 
105_30  0.460348 
47_0    0.020669 
47_10   0.032299 
47_20   0.050603 
47_30   0.073371 

如果还想索引名称 ID 设置 df.index.name:

df.index = df.index.astype(str) + df.pop('Ang').astype(str) 
df.index.name = 'ID' 

第二个解决方案使用DataFrame.rename_axis :

df = (df.set_index(df.index.astype(str) + '_' + df.pop('Ang').astype(str)) 
        .rename_axis('ID')) 
print (df) 
               1 
ID               
23_0    0.889053 
23_10   0.962773 
23_20   1.004188 
23_30   1.008301 
105_0   0.334210 
105_10  0.390434 
105_20  0.434241 
105_30  0.460348 
47_0    0.020669 
47_10   0.032299 
47_20   0.050603 
47_30   0.073371 

编辑:

如果有 .0 值的 float ，首先尝试转换为整数:

df.index = (df.index.astype('int').astype(str) + '_' +  
            df.pop('Ang').astype('int').astype(str)) 

如果无法转换为整数，则一个可能的原因是缺少值:

print (df) 
        Ang         1 
ID                    
23.0    0.0  0.889053 
23.0   10.0  0.962773 
23.0   20.0  1.004188 
23.0   30.0  1.008301 
105.0   0.0  0.334210 
105.0  10.0  0.390434 
105.0  20.0  0.434241 
105.0  30.0  0.460348 
47.0    NaN  0.020669 
NaN    10.0  0.032299 
47.0   20.0  0.050603 
NaN     NaN  0.073371 

Pandas 0.24+ 的一种可能解决方案是使用 integer na通过转换为 Int64:

df.index = (df.index.astype('Int64').astype(str) + '_' +  
            df.pop('Ang').astype('Int64').astype(str)) 
 
print (df) 
                1 
23_0     0.889053 
23_10    0.962773 
23_20    1.004188 
23_30    1.008301 
105_0    0.334210 
105_10   0.390434 
105_20   0.434241 
105_30   0.460348 
47_nan   0.020669 
nan_10   0.032299 
47_20    0.050603 
nan_nan  0.073371 

或者将缺失值替换为一些整数，例如-1 然后将所有值转换为整数:

df.index = (df.index.fillna(-1).astype('int').astype(str) + '_' +  
            df.pop('Ang').fillna(-1).astype('int').astype(str)) 
 
print (df) 
               1 
23_0    0.889053 
23_10   0.962773 
23_20   1.004188 
23_30   1.008301 
105_0   0.334210 
105_10  0.390434 
105_20  0.434241 
105_30  0.460348 
47_-1   0.020669 
-1_10   0.032299 
47_20   0.050603 
-1_-1   0.073371