Selecting based on dtype or data properties#

Selectors can filter columns based on different conditions.

all() is a simple selector, especially useful for default arguments since it keeps all columns:

>>> import pandas as pd
>>> from skrub import SelectCols
>>> import skrub.selectors as s
>>> df = pd.DataFrame(
...     {
...         "height_mm": [297.0, 420.0],
...         "width_mm": [210.0, 297.0],
...         "kind": ["A4", "A3"],
...         "ID": [4, 3],
...     }
... )
>>> SelectCols(cols=s.all()).fit_transform(df)
   height_mm  width_mm kind  ID
0      297.0     210.0   A4   4
1      420.0     297.0   A3   3

Selectors can be combined with operators, for example if we wanted all columns except the “mm” columns above:

>>> SelectCols(s.all() - s.cols("height_mm", "width_mm")).fit_transform(df)
  kind  ID
0   A4   4
1   A3   3

This module provides several kinds of selectors, which allow to select columns by name, data type, contents, or according to arbitrary user-provided rules.

>>> SelectCols(s.numeric()).fit_transform(df)
   height_mm  width_mm  ID
0      297.0     210.0   4
1      420.0     297.0   3

Selectors can be inverted with ~, or inv():

>>> SelectCols(~s.numeric()).fit_transform(df)
  kind
0   A4
1   A3

>>> SelectCols(s.inv(s.numeric())).fit_transform(df)
  kind
0   A4
1   A3

Selectors can work on the column names. For example, to select the columns that end with _mm we can do:

>>> SelectCols(s.glob('*_mm')).fit_transform(df)
   height_mm  width_mm
0      297.0     210.0
1      420.0     297.0

Categories of selectors#

The selectors in this module can be categorized based on what aspect of the columns they examine:

Selectors based on column data types#

  • numeric(): Select columns with numeric data types (float and integer)

  • integer(): Select columns with integer data types

  • float(): Select columns with floating-point data types

  • any_date(): Select columns with date or datetime data types

  • categorical(): Select columns with categorical data types

  • string(): Select columns with string data types

  • boolean(): Select columns with boolean data types

Selectors based on column content and properties#

Selectors based on column names#

  • cols(): Select columns explicitly by name

  • glob(): Select columns by name using Unix shell-style pattern matching

  • regex(): Select columns by name using regular expressions