Consistent ColumnTransformer for intersecting lists of columns

I want to use sklearn.compose.ColumnTransformer consistently (not parallel, so, the second transformer should be executed only after the first) for intersecting lists of columns in this way:

log_transformer = p.FunctionTransformer(lambda x: np.log(x))
df = pd.DataFrame({'a': [1,2, np.NaN, 4], 'b': [1,np.NaN, 3, 4], 'c': [1 ,2, 3, 4]})
compose.ColumnTransformer(n_jobs=1,
                         transformers=[
                             ('num', impute.SimpleImputer() , ['a', 'b']),
                             ('log', log_transformer, ['b', 'c']),
                             ('scale', p.StandardScaler(), ['a', 'b', 'c'])
                         ]).fit_transform(df)

So, I want to use SimpleImputer for 'a', 'b', then log for 'b', 'c', and then StandardScaler for 'a', 'b', 'c'.

But:

1. I get array of (4, 7) shape.
2. I still get Nan in a and b columns.

So, how can I use ColumnTransformer for different columns in the manner of Pipeline?

P.S. please note that I already asked a question on SO.

[konstantin-doncov]

@justmarkham thanks, it works!

[justmarkham]

@don-prog In ct2, specify the cols_3 columns by integer position, not by name.