Is there a way to inverse\_transform one column with sklearn, when the initial transformer was fit on the whole data set? Below is an example of what I am trying to get after.

```
import pandas as pd
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler

# Setting up a dummy pipeline
pipes = []
pipes.append(('scaler', MinMaxScaler()))
transformation_pipeline = Pipeline(pipes)

# Random data.
df = pd.DataFrame(
    {'data1': [1, 2, 3, 1, 2, 3],
     'data2': [1, 1, 1, 2, 2, 2],
     'Y': [1, 4, 1, 2, 2, 2]
    }
)

# Fitting the transformation pipeline
test = transformation_pipeline.fit_transform(df)

# Pulling the scaler function from the pipeline.
scaler = transformation_pipeline.named_steps['scaler']

# This is what I thought may work.
predicted_transformed = scaler.inverse_transform(test['Y'])

# The output would look something like this
# Essentially overlooking that scaler was fit on 3 variables and fitting
# the last one, or any I need.
predicted_transfromed = [1, 4, 1, 2, 2, 2]

```

I need to be able to fit the whole dataset as part of a data prep process. But then I am importing the scaler later into another instance with sklearn.externals joblibs. In this new instance the predicted values are the only thing that exists. So I need to extract just the inverse scaler for the Y column to get back the originals. 

I am aware that I could fit one transformer for X variables and Y variables, However, I would like to avoid this. This method would add to the complexity of moving the scalers around and maintaining both of them in future projects.