import numpy as np
import pandas as pd

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer, OneHotEncoder

df = pd.DataFrame({"Type": ["Beta", "Beta", "Alpha", "Charlie", "Beta", "Charlie"], "A": [1, 2, 3, np.nan, 22, 4], "B": [5, 7, 12, 21, 12, 10]})

def engineer_feature(df):
    df["C"] = df["A"] / df["B"]
    return df

categorical_transformer = Pipeline([
    ("one_hot", OneHotEncoder())
])

preprocessor = ColumnTransformer([
    ("categorical", categorical_transformer, ["Type"]),
    ("numeric", FunctionTransformer(engineer_feature), ["A", "B"])
])

preprocessor.fit_transform(df)
"""
array([[ 0.        ,  1.        ,  0.        ,  1.        ,  5.        ,
         0.2       ],
       [ 0.        ,  1.        ,  0.        ,  2.        ,  7.        ,
         0.28571429],
       [ 1.        ,  0.        ,  0.        ,  3.        , 12.        ,
         0.25      ],
       [ 0.        ,  0.        ,  1.        ,         nan, 21.        ,
                nan],
       [ 0.        ,  1.        ,  0.        , 22.        , 12.        ,
         1.83333333],
       [ 0.        ,  0.        ,  1.        ,  4.        , 10.        ,
         0.4       ]])
"""