是否可以将SimpleImputer的使用与数据帧的不同列分离？

from sklearn.impute import SimpleImputer
import pandas as pd

# 创建一个包含缺失值的数据帧
data = {'A': [1, 2, None, 4, 5],
        'B': [None, 2, 3, 4, None],
        'C': [1, 2, 3, None, 5]}
df = pd.DataFrame(data)

# 提取需要填充缺失值的列
columns_to_impute = ['A', 'B']

# 将需要填充缺失值的列提取出来形成新的数据帧
df_to_impute = df[columns_to_impute]

# 创建SimpleImputer对象并进行缺失值填充
imputer = SimpleImputer(strategy='mean')
imputed_data = imputer.fit_transform(df_to_impute)

# 将填充后的数据与原始数据帧进行合并
df_imputed = pd.DataFrame(imputed_data, columns=columns_to_impute)
df_final = pd.concat([df.drop(columns_to_impute, axis=1), df_imputed], axis=1)

# 打印填充后的数据帧
print(df_final)