data.py

data = pd.DataFrame({'pet':      ['cat', 'dog', 'dog', 'fish', 'cat', 'dog', 'cat', 'fish'],
                     'children': [4., 6, 3, 3, 2, 3, 5, 4],
                     'salary':   [90., 24, 44, 27, 32, 59, 36, 27]})
data['pet'] = data['pet'].astype("category")

mapper.out

array([[ 1.        ,  0.        ,  0.        ,  0.20851441],
       [ 0.        ,  1.        ,  0.        ,  1.87662973],
       [ 0.        ,  1.        ,  0.        , -0.62554324],
       [ 0.        ,  0.        ,  1.        , -0.62554324],
       [ 1.        ,  0.        ,  0.        , -1.4596009 ],
       [ 0.        ,  1.        ,  0.        , -0.62554324],
       [ 1.        ,  0.        ,  0.        ,  1.04257207],
       [ 0.        ,  0.        ,  1.        ,  0.20851441]])

mapper.py

mapper = DataFrameMapper([
    ('pet', preprocessing.LabelBinarizer()),
    (['children'], preprocessing.StandardScaler())
])
mapper.fit_transform(data.copy())

pipe-partial.py

pipeline.make_pipeline(
    TypeSelector("category"),
    preprocessing.OneHotEncoder()
)

pipe.out

array([[ 1.        ,  0.        ,  0.        ,  0.20851441,  2.27500192],
       [ 0.        ,  1.        ,  0.        ,  1.87662973, -0.87775665],
       [ 0.        ,  1.        ,  0.        , -0.62554324,  0.07762474],
       [ 0.        ,  0.        ,  1.        , -0.62554324, -0.73444944],
       [ 1.        ,  0.        ,  0.        , -1.4596009 , -0.49560409],
       [ 0.        ,  1.        ,  0.        , -0.62554324,  0.79416078],
       [ 1.        ,  0.        ,  0.        ,  1.04257207, -0.30452782],
       [ 0.        ,  0.        ,  1.        ,  0.20851441, -0.73444944]])

pipe.py

pipe = pipeline.make_union(
    pipeline.make_pipeline(
        TypeSelector("category"),
        preprocessing.OneHotEncoder()
    ),
    pipeline.make_pipeline(
        TypeSelector(np.number),
        preprocessing.StandardScaler()
    )
)
pipe.fit_transform(data.copy()).toarray()

TypeSelector.py

from sklearn.base import BaseEstimator, TransformerMixin
class TypeSelector(BaseEstimator, TransformerMixin):
    def __init__(self, dtype):
        self.dtype = dtype
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        assert isinstance(X, pd.DataFrame)
        return X.select_dtypes(include=[self.dtype])