sklearn.compose
.make_column_transformer¶

sklearn.compose.
make_column_transformer
(*transformers, **kwargs)[source]¶ 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
.Parameters:  *transformers : tuples of transformers and column selections
 remainder : {‘drop’, ‘passthrough’} or estimator, default ‘drop’
By default, only the specified columns in
transformers
are transformed and combined in the output, and the nonspecified columns are dropped. (default of'drop'
). By specifyingremainder='passthrough'
, all remaining columns that were not specified intransformers
will be automatically passed through. This subset of columns is concatenated with the output of the transformers. By settingremainder
to be an estimator, the remaining nonspecified columns will use theremainder
estimator. The estimator must supportfit
andtransform
. 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 ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details.
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(n_jobs=None, remainder='drop', sparse_threshold=0.3, transformer_weights=None, transformers=[('standardscaler', StandardScaler(...), ['numerical_column']), ('onehotencoder', OneHotEncoder(...), ['categorical_column'])])