如何从sklearn编码/缩放管道取回DF？

Question

我正在尝试使用sklearns管道来编码和缩放数据名人堂。它只是返回一个numpy数组而不是一个数据框。我希望能提供一种更简单/标准的方法来找回编码/缩放的数据帧，而不是提出一个骇人的解决方案（我最擅长！）。

这是我要编码/缩放的代码示例：

from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer


num_attributes = list(train_set.select_dtypes(exclude=['object'])) #to select all num columns, we exclude any column with object types
cat_attributes = list(train_set.select_dtypes(include=['object'])) #here we select all columns with object types

cat_pipeline = Pipeline([ 
    ('imputer', SimpleImputer(fill_value='none', strategy='constant')),
    ('one_hot', OneHotEncoder())
    ])

full_pipeline = ColumnTransformer([
    ('num', StandardScaler(), num_attributes),
    ('cat', cat_pipeline, cat_attributes)
])

train_set_prepared = full_pipeline.fit_transform(train_set)

结果是numpy数组：

  (0, 0)    nan
  (0, 1)    -0.002676506826924531
  (0, 2)    nan
  (0, 3)    -0.03350622836892517
  (0, 4)    nan
  (0, 5)    -0.03294496247236749
  (0, 6)    0.002534826949104915

有没有一种方法可以轻松地将其转换回经过缩放/编码的数据名望？