transformer = StandardScaler()
mapper = DataFrameMapper([
    (['colA'], StandardScaler()),
    (['colB'], StandardScaler()),
    ('colC', [StandardScaler(), FunctionTransformer()]
])
pipeline = Pipeline([
    ('mapper', mapper),
    ('classifier', SVC(kernel='linear'))
])

# 1. the pipeline parameters include parameters of the nested transformers
parameters = pipeline.get_params()
assert 'mapper__colA__with_mean' in parameters
assert 'mapper__colA__with_std' in parameters
assert 'mapper__colB__with_mean' in parameters
assert 'mapper__colB__with_std' in parameters

# 2. the parameters of nested transformers can be set from the outside
pipeline.set_params(
    mapper__colA__with_mean=True,
    mapper__colB__with_std=False
)

# 3. getting parameters from list of transformers
assert 'mapper__colC__standardscaler__with_mean' in parameters
assert 'mapper__colC__functiontransformer__func' in parameters

# 4. setting parameters to list of transformers
pipeline.set_params(
    mapper__colC__standardscaler__with_mean=True,
    mapper__colC__functiontransformer__func=np.log1p
)

# 5. grid search with parameters
param_grid = dict(
    mapper__colA__standardscaler__with_mean=[True, False],
    mapper__colB__standardscaler__with_std=[True, False],
    mapper__colC__functiontransformer__func=[np.log1p, np.exp, None]  
)
grid_search = GridSearchCV(pipeline, param_grid=param_grid)
grid_search.fit(X, y)

from sklearn.pipeline import Pipeline
from sklearn.feature_selection import SelectKBest, chi2
mapper_fs = DataFrameMapper([(['children','salary'], SelectKBest(chi2, k=1))])
pipeline = Pipeline([
    ('mapper', mapper_fs),
    ('classifier', SVC(kernel='linear'))
])
# how to handle transformers with several columns?
pipeline.set_params(...)