sklearn.compose
.make_column_selector¶
-
sklearn.compose.
make_column_selector
(pattern=None, *, dtype_include=None, dtype_exclude=None)[source]¶ Create a callable to select columns to be used with
ColumnTransformer
.make_column_selector
can select columns based on datatype or the columns name with a regex. When using multiple selection criteria, all criteria must match for a column to be selected.- Parameters
- patternstr, default=None
Name of columns containing this regex pattern will be included. If None, column selection will not be selected based on pattern.
- dtype_includecolumn dtype or list of column dtypes, default=None
A selection of dtypes to include. For more details, see
pandas.DataFrame.select_dtypes
.- dtype_excludecolumn dtype or list of column dtypes, default=None
A selection of dtypes to exclude. For more details, see
pandas.DataFrame.select_dtypes
.
- Returns
- selectorcallable
Callable for column selection to be used by a
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 >>> from sklearn.compose import make_column_selector >>> import pandas as pd # doctest: +SKIP >>> X = pd.DataFrame({'city': ['London', 'London', 'Paris', 'Sallisaw'], ... 'rating': [5, 3, 4, 5]}) # doctest: +SKIP >>> ct = make_column_transformer( ... (StandardScaler(), ... make_column_selector(dtype_include=np.number)), # rating ... (OneHotEncoder(), ... make_column_selector(dtype_include=object))) # city >>> ct.fit_transform(X) # doctest: +SKIP array([[ 0.90453403, 1. , 0. , 0. ], [-1.50755672, 1. , 0. , 0. ], [-0.30151134, 0. , 1. , 0. ], [ 0.90453403, 0. , 0. , 1. ]])