Implementation:Scikit learn Scikit learn Make Column Selector

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Overview

Concrete tool for creating callable column selectors for ColumnTransformer provided by scikit-learn.

Code Reference

Class: make_column_selector

Module: sklearn/compose/_column_transformer.py (lines 1516-1602)

Constructor signature:

class make_column_selector:
    def __init__(self, pattern=None, *, dtype_include=None, dtype_exclude=None):

Callable signature:

def __call__(self, df):

Despite being named in lowercase (following the convention of factory functions), make_column_selector is actually a class. Instantiating it returns a callable object that can be passed directly to ColumnTransformer in place of an explicit column list.

I/O Contract

Constructor parameters:

• pattern : str, default=None -- A regex pattern. Columns whose names match this pattern are included. If None, no name-based filtering is applied.
• dtype_include : column dtype or list of column dtypes, default=None -- Dtypes to include in the selection. Passed to pandas.DataFrame.select_dtypes(include=...).
• dtype_exclude : column dtype or list of column dtypes, default=None -- Dtypes to exclude from the selection. Passed to pandas.DataFrame.select_dtypes(exclude=...).

Call input:

• df : pandas DataFrame -- The input DataFrame to select columns from. Raises ValueError if the input does not have an iloc attribute (i.e., is not a DataFrame).

Call output:

• list of column names -- The selected column names as a Python list.

Selection logic: When multiple criteria are specified, all criteria must match for a column to be selected. The function first applies dtype filtering, then applies the regex pattern to the surviving columns.

Implementation Details

The __call__ method performs column selection in the following steps:

1. Takes a single-row slice of the DataFrame (df.iloc[:1]) to minimize memory usage during dtype inspection.
2. If dtype_include or dtype_exclude is set, calls select_dtypes on the single-row slice to filter columns by type.
3. If pattern is set, applies cols.str.contains(self.pattern, regex=True) to filter the remaining columns by name.
4. Returns the final column index as a Python list via cols.tolist().

Usage Examples

import numpy as np
import pandas as pd
from sklearn.compose import make_column_selector, make_column_transformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

# Create a mixed-type DataFrame
X = pd.DataFrame({
    "city": ["London", "London", "Paris", "Sallisaw"],
    "rating": [5, 3, 4, 5]
})

# Select numeric columns by dtype
num_selector = make_column_selector(dtype_include=np.number)
print(num_selector(X))  # ['rating']

# Select categorical columns by dtype
cat_selector = make_column_selector(dtype_include=[object, "string"])
print(cat_selector(X))  # ['city']

# Use selectors inside a ColumnTransformer
ct = make_column_transformer(
    (StandardScaler(), make_column_selector(dtype_include=np.number)),
    (OneHotEncoder(), make_column_selector(dtype_include=[object, "string"]))
)
ct.fit_transform(X)

Related Pages

• Principle:Scikit_learn_Scikit_learn_Column_Selection