Why is ordinalencoder not encoding some of my features properly?

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Python

artificial-intelligence

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Hey, I have a problem with OrdinalEncoder. I'm using OrdinalEncoder to encode this dataset: Loan Prediction - Analytics Vidhya | Kaggle[^] , but there are 2 features (Dependants and Education) that are -1 for all 614 rows. Can you please tell me what I did wrong?

What I have tried:

object_cols = [col for col in X.columns if X[col].dtype == "object"]
num_cols = list(set(X.columns) - set(object_cols))

X = X.fillna(X.mean())
X_test = X_test.fillna(X_test.mean())

object_imputer = SimpleImputer(strategy="most_frequent")
X[object_cols] = pd.DataFrame(object_imputer.fit_transform(X[object_cols]))
X_test[object_cols] = pd.DataFrame(object_imputer.transform(X_test[object_cols]))

mapping = [
    {"col": "Dependents", "mapping": {
        "0": 0, "1": 1, "2": 2, "3+": 3
    }},
    {"col": "Education", "mapping": {
        "Not Graduate": 0, "Graduate": 1
    }}
]
onehot_cols = ["Gender", "Property_Area"]
ord_cols = list(set(object_cols) - set(onehot_cols)) 
for col in ord_cols:
    mapping.append({"col": col, "mapping": {
        "No": 0, "Yes": 1
    }})

encoder = Pipeline(steps=[
    ("ordinal", OrdinalEncoder(mapping=mapping)),
    ("onehot", OneHotEncoder(cols=onehot_cols))
])

X = pd.DataFrame(encoder.fit_transform(X))
X_test = pd.DataFrame(encoder.transform(X_test))