In [1]: import dask.dataframe as dd
   ...: import pandas as pd
   ...: import sklearn.compose
   ...: import sklearn.preprocessing
   ...: from sklearn.base import clone
   ...:
   ...: import dask_ml.compose
   ...: import dask_ml.preprocessing
   ...:
   ...: df = pd.DataFrame({"A": pd.Categorical(["a", "a", "b", "a"]), "B": [1.0, 2, 4, 5]})
   ...: ddf = dd.from_pandas(df, npartitions=2)
   ...:
   ...:

In [2]:     b = dask_ml.compose.make_column_transformer(
   ...:         (["A"], dask_ml.preprocessing.OneHotEncoder(sparse=False)),
   ...:         (["B"], dask_ml.preprocessing.StandardScaler()),
   ...:     )
   ...:
   ...:

In [3]: result = b.fit_transform(ddf)

In [4]: result
Out[4]:
Dask DataFrame Structure:
                   A_a      A_b        B
npartitions=2
0              float64  float64  float64
2                  ...      ...      ...
3                  ...      ...      ...
Dask Name: concat-indexed, 22 tasks

In [5]: result.compute()
Out[5]:
   A_a  A_b         B
0  1.0  0.0 -1.264911
1  1.0  0.0 -0.632456
2  0.0  1.0  0.632456
3  1.0  0.0  1.264911