Pipeline

import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

num_features = ["age", "income"]
cat_features = ["city"]

preprocess = ColumnTransformer([
    ("num", Pipeline([
        ("imputer", SimpleImputer(strategy="median")),
        ("scaler", StandardScaler()),
    ]), num_features),
    ("cat", Pipeline([
        ("imputer", SimpleImputer(strategy="most_frequent")),
        ("onehot", OneHotEncoder(handle_unknown="ignore")),
    ]), cat_features),
])

pipe = Pipeline([
    ("preprocess", preprocess),
    ("clf", RandomForestClassifier(n_estimators=100, random_state=42)),
])

# Treats the whole pipeline as a single estimator
pipe.fit(X_train, y_train)
score = pipe.score(X_test, y_test)
print(score)