How can I standardize only numeric variables in an sklearn pipeline?

Question

I am trying to create an sklearn pipeline with 2 steps:

1. Standardize the data
2. Fit the data using KNN

However, my data has both numeric a

Answer 1

Since you have converted your categorical features into dummies using pd.get_dummies, so you don't need to use OneHotEncoder. As a result, your pipeline should be:

from sklearn.preprocessing import StandardScaler,FunctionTransformer
from sklearn.pipeline import Pipeline,FeatureUnion

knn=KNeighborsClassifier()

pipeline=Pipeline(steps= [
    ('feature_processing', FeatureUnion(transformer_list = [
            ('categorical', FunctionTransformer(lambda data: data[:, cat_indices])),

            #numeric
            ('numeric', Pipeline(steps = [
                ('select', FunctionTransformer(lambda data: data[:, num_indices])),
                ('scale', StandardScaler())
                        ]))
        ])),
    ('clf', knn)
    ]
)