pandas中特定列的强制排序

使用 difference 对于所有未在具有前置列表的列表中指定的列 L 对于新列名,上次更改按子集排序 [] :

df=pd.DataFrame(data=np.random.rand(100,1000))

L = [793, 642]

cols = L + df.columns.difference(L).tolist()
#another solution
#cols = np.concatenate([L, df.columns.difference(L)])
#list comprehension solution
#cols = L + [x for x in df.columns if not x in L]

df = df[cols]
print (df.head(2))
        793       642       0         1         2         3         4    \
0  0.462103  0.811223  0.491396  0.701752  0.494450  0.352717  0.345460   
1  0.840597  0.852080  0.681095  0.014459  0.963252  0.972862  0.490964   

        5         6         7      ...          990       991       992  \
0  0.718141  0.199168  0.379924    ...     0.279972  0.963898  0.987907   
1  0.151226  0.625833  0.428249    ...     0.069179  0.045112  0.328453   

        993       994       995       996       997       998       999  
0  0.402805  0.243648  0.624790  0.864440  0.653621  0.066524  0.072025  
1  0.894080  0.451285  0.538485  0.834018  0.926311  0.032849  0.095636  

[2 rows x 1000 columns]