parameters: (*transformers, **kwargs)
return_statement: return ColumnTransformer(transformer_list, n_jobs=n_jobs,
                               remainder=remainder,
                               sparse_threshold=sparse_threshold,
                               verbose=verbose)
docstring: Construct a ColumnTransformer from the given transformers.
  
      This is a shorthand for the ColumnTransformer constructor; it does not
      require, and does not permit, naming the transformers. Instead, they will
      be given names automatically based on their types. It also does not allow
      weighting with ``transformer_weights``.
  
      Read more in the :ref:`User Guide <make_column_transformer>`.
  
      Parameters
      ----------
      *transformers : tuples
          Tuples of the form (transformer, column(s)) specifying the
          transformer objects to be applied to subsets of the data.
  
          transformer : estimator or {'passthrough', 'drop'}
              Estimator must support :term:`fit` and :term:`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 :term:`fit` and :term:`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.
  
      verbose : boolean, optional(default=False)
          If True, the time elapsed while fitting each transformer will be
          printed as it is completed.
  
      Returns
      -------
      ct : ColumnTransformer
  
      See also
      --------
      sklearn.compose.ColumnTransformer : Class that allows combining the
          outputs of multiple transformer objects used on column subsets
          of the data into a single feature space.
  
      Examples
      --------
      >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
      >>> from sklearn.compose import make_column_transformer
      >>> make_column_transformer(
      ...     (StandardScaler(), ['numerical_column']),
      ...     (OneHotEncoder(), ['categorical_column']))
      ColumnTransformer(transformers=[('standardscaler', StandardScaler(...),
                                       ['numerical_column']),
                                      ('onehotencoder', OneHotEncoder(...),
                                       ['categorical_column'])])
docstring_summary: Construct a ColumnTransformer from the given transformers.
docstring_tokens: Construct a ColumnTransformer from the given transformers .
function: def make_column_transformer(*transformers, **kwargs):
      """Construct a ColumnTransformer from the given transformers.
  
      This is a shorthand for the ColumnTransformer constructor; it does not
      require, and does not permit, naming the transformers. Instead, they will
      be given names automatically based on their types. It also does not allow
      weighting with ``transformer_weights``.
  
      Read more in the :ref:`User Guide <make_column_transformer>`.
  
      Parameters
      ----------
      *transformers : tuples
          Tuples of the form (transformer, column(s)) specifying the
          transformer objects to be applied to subsets of the data.
  
          transformer : estimator or {'passthrough', 'drop'}
              Estimator must support :term:`fit` and :term:`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 :term:`fit` and :term:`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.
  
      verbose : boolean, optional(default=False)
          If True, the time elapsed while fitting each transformer will be
          printed as it is completed.
  
      Returns
      -------
      ct : ColumnTransformer
  
      See also
      --------
      sklearn.compose.ColumnTransformer : Class that allows combining the
          outputs of multiple transformer objects used on column subsets
          of the data into a single feature space.
  
      Examples
      --------
      >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
      >>> from sklearn.compose import make_column_transformer
      >>> make_column_transformer(
      ...     (StandardScaler(), ['numerical_column']),
      ...     (OneHotEncoder(), ['categorical_column']))
      ColumnTransformer(transformers=[('standardscaler', StandardScaler(...),
                                       ['numerical_column']),
                                      ('onehotencoder', OneHotEncoder(...),
                                       ['categorical_column'])])
  
      """
      # transformer_weights keyword is not passed through because the user
      # would need to know the automatically generated names of the transformers
      n_jobs = kwargs.pop('n_jobs', None)
      remainder = kwargs.pop('remainder', 'drop')
      sparse_threshold = kwargs.pop('sparse_threshold', 0.3)
      verbose = kwargs.pop('verbose', False)
      if kwargs:
          raise TypeError('Unknown keyword arguments: "{}"'
                          .format(list(kwargs.keys())[0]))
      transformer_list = _get_transformer_list(transformers)
      return ColumnTransformer(transformer_list, n_jobs=n_jobs,
                               remainder=remainder,
                               sparse_threshold=sparse_threshold,
                               verbose=verbose)
function_tokens: def make_column_transformer ( * transformers , * * kwargs ) : # transformer_weights keyword is not passed through because the user # would need to know the automatically generated names of the transformers n_jobs = kwargs . pop ( 'n_jobs' , None ) remainder = kwargs . pop ( 'remainder' , 'drop' ) sparse_threshold = kwargs . pop ( 'sparse_threshold' , 0.3 ) verbose = kwargs . pop ( 'verbose' , False ) if kwargs : raise TypeError ( 'Unknown keyword arguments: "{}"' . format ( list ( kwargs . keys ( ) ) [ 0 ] ) ) transformer_list = _get_transformer_list ( transformers ) return ColumnTransformer ( transformer_list , n_jobs = n_jobs , remainder = remainder , sparse_threshold = sparse_threshold , verbose = verbose )