sklearn.compose.make_column_selector

sklearn.compose.make_column_selector(pattern=None, *, dtype_include=None, dtype_exclude=None)

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

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 numpy as np
>>> import pandas as pd  
>>> X = pd.DataFrame({'city': ['London', 'London', 'Paris', 'Sallisaw'],
...                   'rating': [5, 3, 4, 5]})  
>>> 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)  
array([[ 0.90453403,  1.        ,  0.        ,  0.        ],
       [-1.50755672,  1.        ,  0.        ,  0.        ],
       [-0.30151134,  0.        ,  1.        ,  0.        ],
       [ 0.90453403,  0.        ,  0.        ,  1.        ]])

Examples using sklearn.compose.make_column_selector

Categorical Feature Support in Gradient Boosting

Combine predictors using stacking

Column Transformer with Mixed Types