I'm using Pipeline and ColumnTransformer modules from sklearn library to perform feature engineering on my dataset.

The dataset initially looks like this:

$> data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2935849 entries, 0 to 2935848
Data columns (total 6 columns):
 #   Column          Dtype  
---  ------          -----  
 0   date            object 
 1   date_block_num  object  
 2   shop_id         object  
 3   item_id         object  
 4   item_price      float64
dtypes: float64(2), int64(3), object(1)
memory usage: 134.4+ MB

Then I have the following transformations:

num_column_transformer = ColumnTransformer(
    transformers=[
        ("std_scaler", StandardScaler(), make_column_selector(dtype_include=np.number)),
    ],
    remainder="passthrough"
)

num_pipeline = Pipeline(
    steps=[
        ("percent_item_cnt_day_per_shop", PercentOverTotalAttributeWholeAdder(
            attribute_percent_name="shop_id",
            attribute_total_name="item_cnt_day",
            new_attribute_name="%_item_cnt_day_per_shop")
        ),
        ("percent_item_cnt_day_per_item", PercentOverTotalAttributeWholeAdder(
            attribute_percent_name="item_id",
            attribute_total_name="item_cnt_day",
            new_attribute_name="%_item_cnt_day_per_item")
        ),
        ("percent_sales_per_shop", SalesPerAttributeOverTotalSalesAdder(
            attribute_percent_name="shop_id",
            new_attribute_name="%_sales_per_shop")
        ),
        ("percent_sales_per_item", SalesPerAttributeOverTotalSalesAdder(
            attribute_percent_name="item_id",
            new_attribute_name="%_sales_per_item")
        ),
        ("num_column_transformer", num_column_transformer),
    ]
)

The first four Transformers create four new different numeric variables and the last one applies StandardScaler over all the numerical values of the dataset.

After executing it, I get the following data:

I'd like to have the following output:

As you can see, columns 5, 6, 7, and 8 from the output corresponds to the first four columns in the original dataset. For example, I don't know where the item_price feature lies in the outputted table.

1. How could I preserve the column order and names? After that, I want to do feature engineering over categorical variables and my Transformers make use of the feature column name.
2. Am I using correctly the Scikit-Learn API?