Why I get more columns than expected after OneHotEncoding in a Sklearn Pipeline?

Question

I'm using sklearn pipelines to preprocess my data.

from sklearn.pipeline import Pipeline
from sklearn.impute import KNNImputer
from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder

numeric_transformer = Pipeline(steps=[('scaler', StandardScaler()),
    ('imputer', KNNImputer(n_neighbors=2,weights='uniform', metric='nan_euclidean', add_indicator=True))
   ])
categorical_transformer = Pipeline(steps=[ 
    ('one_hot_encoder', OneHotEncoder(sparse=False, handle_unknown='ignore'))])


from sklearn.compose import make_column_selector as selector

numeric_features = ['Latitud','Longitud','Habitaciones','Dormitorios','Baños','Superficie_Total','Superficie_cubierta']
categorical_features = ['Tipo_de_propiedad']
from sklearn.compose import ColumnTransformer
preprocessor = ColumnTransformer(
   transformers=[
    ('numeric', numeric_transformer, numeric_features, selector(dtype_exclude="category"))
   ,('categorical', categorical_transformer, categorical_features, selector(dtype_include="category"))])

The feature Tipo_de_propiedad has 3 classes: 'Departamento', 'Casa', 'PH'. So the 7 other features plus these dummies should give me 10 after transforming, but when I apply fit_transform, it returns 14 features.

train_transfor=pd.DataFrame(preprocessor.fit_transform(X_train))
train_transfor.head()

When I use pd.get_dummies it works well, but I can't use that to apply in the Pipeline; OneHotEncoder is useful because I can fit on train set and transform on the test set.

dummy=pd.get_dummies(df30[["Tipo_de_propiedad"]])
df_new=pd.concat([df30,dummy],axis=1)
df_new.head()

Answer 1

Your KNNImputer has used the parameter add_indicator=True, so the additional columns are presumably missingness indicators for some of your numeric columns.