Column Transformer

num_columns = [""] # Add the appropriate column names
cat_columns = [""] # Add the appropriate column names

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

num_pipeline = Pipeline(
                steps=[
                    ("impute", SimpleImputer(strategy="median")),
                    ("scaler", StandardScaler()),
                ]
            )

preprocessor = ColumnTransformer(
                transformers=[
                    ("num_pipeline", num_pipeline, num_columns)
                ]
            )

input_feature_train = df.drop('price', axis=1)

arr = pd.DataFrame(preprocessor.fit_transform(input_feature_train),columns=preprocessor.get_feature_names_out())
print(arr)