dask_ml.compose.make_column_transformer
dask_ml.compose.make_column_transformer¶
- dask_ml.compose.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 User Guide.
- Parameters
- *transformerstuples
Tuples of the form (transformer, columns) specifying the transformer objects to be applied to subsets of the data.
- transformer{‘drop’, ‘passthrough’} or estimator
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.
- columnsstr, array-like of str, int, array-like of int, slice, array-like of bool 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. To select multiple columns by name or dtype, you can usemake_column_selector
.
- 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 specifyingremainder='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 settingremainder
to be an estimator, the remaining non-specified columns will use theremainder
estimator. The estimator must support fit and transform.- sparse_thresholdfloat, 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_jobsint, 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.- verbosebool, default=False
If True, the time elapsed while fitting each transformer will be printed as it is completed.
- verbose_feature_names_outbool, default=True
If True,
ColumnTransformer.get_feature_names_out()
will prefix all feature names with the name of the transformer that generated that feature. If False,ColumnTransformer.get_feature_names_out()
will not prefix any feature names and will error if feature names are not unique.New in version 1.0.
- force_int_remainder_colsbool, default=True
Force the columns of the last entry of transformers_, which corresponds to the “remainder” transformer, to always be stored as indices (int) rather than column names (str). See description of the
ColumnTransformer.transformers_
attribute for details.Note
If you do not access the list of columns for the remainder columns in the
ColumnTransformer.transformers_
fitted attribute, you do not need to set this parameter.New in version 1.5.
Changed in version 1.7: The default value for force_int_remainder_cols will change from True to False in version 1.7.
- Returns
- ctColumnTransformer
Returns a
ColumnTransformer
object.
See also
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'])])