class_docstr: Applies transformers to columns of an array or pandas DataFrame.
  
  EXPERIMENTAL: some behaviors may change between releases without
  deprecation.
  
  This estimator allows different columns or column subsets of the input
  to be transformed separately and the results combined into a single
  feature space.
  This is useful for heterogeneous or columnar data, to combine several
  feature extraction mechanisms or transformations into a single transformer.
  
  Read more in the :ref:`User Guide <column_transformer>`.
  
  .. versionadded:: 0.9.0
  
  .. note::
  
     This requires scikit-learn 0.20.0 or newer.
  
  Parameters
  ----------
  transformers : list of tuples
      List of (name, transformer, column(s)) tuples specifying the
      transformer objects to be applied to subsets of the data.
  
      name : string
          Like in Pipeline and FeatureUnion, this allows the transformer and
          its parameters to be set using ``set_params`` and searched in grid
          search.
      transformer : estimator or {'passthrough', 'drop'}
          Estimator must support `fit` and `transform`. Special-cased
          strings 'drop' and 'passthrough' are accepted as well, to
          indicate to drop the columns or to pass them through untransformed,
          respectively.
      column(s) : string or int, array-like of string or int, slice, boolean mask array or callable
          Indexes the data on its second axis. Integers are interpreted as
          positional columns, while strings can reference DataFrame columns
          by name.  A scalar string or int should be used where
          ``transformer`` expects X to be a 1d array-like (vector),
          otherwise a 2d array will be passed to the transformer.
          A callable is passed the input data `X` and can return any of the
          above.
  
  remainder : {'drop', 'passthrough'} or estimator, default 'drop'
      By default, only the specified columns in `transformers` are
      transformed and combined in the output, and the non-specified
      columns are dropped. (default of ``'drop'``).
      By specifying ``remainder='passthrough'``, all remaining columns that
      were not specified in `transformers` will be automatically passed
      through. This subset of columns is concatenated with the output of
      the transformers.
      By setting ``remainder`` to be an estimator, the remaining
      non-specified columns will use the ``remainder`` estimator. The
      estimator must support `fit` and `transform`.
  
  sparse_threshold : float, default = 0.3
      If the transformed output consists of a mix of sparse and dense data,
      it will be stacked as a sparse matrix if the density is lower than this
      value. Use ``sparse_threshold=0`` to always return dense.
      When the transformed output consists of all sparse or all dense data,
      the stacked result will be sparse or dense, respectively, and this
      keyword will be ignored.
  
  n_jobs : int or None, optional (default=None)
      Number of jobs to run in parallel.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      for more details.
  
  transformer_weights : dict, optional
      Multiplicative weights for features per transformer. The output of the
      transformer is multiplied by these weights. Keys are transformer names,
      values the weights.
  
  preserve_dataframe : bool, (default=True)
      Whether to preserve preserve pandas DataFrames when concatenating
      the results.
  
      .. warning::
  
         The default behavior of keeping DataFrames differs from
         scikit-learn's current behavior. Set ``preserve_dataframe=False``
         if you need to ensure that the output matches scikit-learn's
         ColumnTransformer.
  
  Attributes
  ----------
  transformers_ : list
      The collection of fitted transformers as tuples of
      (name, fitted_transformer, column). `fitted_transformer` can be an
      estimator, 'drop', or 'passthrough'. If there are remaining columns,
      the final element is a tuple of the form:
      ('remainder', transformer, remaining_columns) corresponding to the
      ``remainder`` parameter. If there are remaining columns, then
      ``len(transformers_)==len(transformers)+1``, otherwise
      ``len(transformers_)==len(transformers)``.
  
  named_transformers_ : Bunch object, a dictionary with attribute access
      Read-only attribute to access any transformer by given name.
      Keys are transformer names and values are the fitted transformer
      objects.
  
  sparse_output_ : boolean
      Boolean flag indicating whether the output of ``transform`` is a
      sparse matrix or a dense numpy array, which depends on the output
      of the individual transformers and the `sparse_threshold` keyword.
  
  Notes
  -----
  The order of the columns in the transformed feature matrix follows the
  order of how the columns are specified in the `transformers` list.
  Columns of the original feature matrix that are not specified are
  dropped from the resulting transformed feature matrix, unless specified
  in the `passthrough` keyword. Those columns specified with `passthrough`
  are added at the right to the output of the transformers.
  
  See also
  --------
  dask_ml.compose.make_column_transformer : convenience function for
      combining the outputs of multiple transformer objects applied to
      column subsets of the original feature space.
  
  Examples
  --------
  >>> from dask_ml.compose import ColumnTransformer
  >>> from sklearn.preprocessing import Normalizer
  >>> ct = ColumnTransformer(
  ...     [("norm1", Normalizer(norm='l1'), [0, 1]),
  ...      ("norm2", Normalizer(norm='l1'), slice(2, 4))])
  >>> X = np.array([[0., 1., 2., 2.],
  ...               [1., 1., 0., 1.]])
  >>> # Normalizer scales each row of X to unit norm. A separate scaling
  >>> # is applied for the two first and two last elements of each
  >>> # row independently.
  >>> ct.fit_transform(X)    # doctest: +NORMALIZE_WHITESPACE
  array([[0. , 1. , 0.5, 0.5],
         [0.5, 0.5, 0. , 1. ]])
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code: def __init__(
      self,
      transformers,
      remainder="drop",
      sparse_threshold=0.3,
      n_jobs=1,
      transformer_weights=None,
      preserve_dataframe=True,
  ):
      self.preserve_dataframe = preserve_dataframe
      super(ColumnTransformer, self).__init__(
          transformers=transformers,
          remainder=remainder,
          sparse_threshold=sparse_threshold,
          n_jobs=n_jobs,
          transformer_weights=transformer_weights,
      )
  
file_path: dask-ml/dask_ml/compose/_column_transformer.py
imports: import warnings
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import sklearn.compose
  from scipy import sparse
  from sklearn.compose._column_transformer import _get_transformer_list
label_desc: Use image node_id 1 to create a new ColumnTransformer object from inherited base classes: sklearn.compose with example: obj = ColumnTransformer(transformers, remainder, sparse_threshold, n_jobs, transformer_weights, preserve_dataframe)