How to know the order and labels of columns where the feature engineering is done?

Question

I have been given a assignment by my teacher for doing practise on Basic Feature engineering taught in class. So I did practise it on a basic dataset which looks as follows:-

pipe_age=Pipeline([("infused",SimpleImputer(strategy='median')),
                  ("scaled",StandardScaler())])
pipe_No_of_Children=Pipeline([("scaled_child",StandardScaler())])
pipe_balance=Pipeline([("infused_bala",SimpleImputer(strategy='mean')),
                  ("scaled_bala",StandardScaler())])
pipe_city=Pipeline([("one_hot_encod",OneHotEncoder(sparse=False)),
                  ("scaled_city",StandardScaler())])
pipe_ratings=Pipeline([("ordinal_encod",OrdinalEncoder(categories=[["Excellent",'Good', 'Bad','Can Improve']])),
                  ("scaled_ratings",StandardScaler())])
pipe_fico_min=Pipeline([("scaled_fico_min",StandardScaler())])
pipe_fico_max=Pipeline([("scaled_fico_max",StandardScaler())])


pre_processing=ColumnTransformer(transformers=[("pipe_age",pipe_age,["Age"]),
                                              ("pipe_city",pipe_city,["CITY"]),
                                              ("pipe_rating",pipe_ratings,["Ratings"]),
                                              ("pipe_balance",pipe_balance,["Balances"]),
                                              ("pipe_children",pipe_No_of_Children,["No_of_Children"]),
                                              ("pipe_fico_min",pipe_fico_min,["fico_min"]),
                                              ("pipe_fico_max",pipe_fico_max,["fico_max"])])

pre_processing.fit(df)

pd.DataFrame(pre_processing.transform(df))

Now after doing above I could not understand which columns refer to columns present in actual data frame. How to give labels to columns during the above transformation so that after that's done it's easy to distinguish the columns?

Like here 0,1,2 represents which columns in main data set