select#

skrub.selectors.select(df, selector)[source]#

Select the columns of a dataframe that are matched by the selector.

This function returns a new dataframe containing only the columns of df matched by selector.

Parameters:
dfdataframe

The dataframe to select columns from (pandas or polars).

selectorselector, python:str, or python:list

A selector object, single column name, or list of column names.

Returns:
dataframe

A new dataframe containing only the columns matched by the selector.

See also

drop

Return all columns except those matched by a selector

Selector.expand

Get the column names matched by a selector as a list

Notes

select is a convenience function that combines two operations:

  1. selector.expand(df) - Get list of matching column names

  2. Return the dataframe subset to those columns

If you only need the list of matching column names (without subsetting the dataframe), use selector.expand(df) directly.

Examples

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

Select all columns except ‘ID’:

>>> selector = s.all() - 'ID'
>>> selector
(all() - cols('ID'))

>>> s.select(df, selector)
   height_mm  width_mm kind
0      297.0     210.0   A4
1      420.0     297.0   A3

Pass column names directly:

>>> s.select(df, ['kind', 'ID'])
  kind  ID
0   A4   4
1   A3   3

Select by dtype:

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

Combine multiple selectors:

>>> s.select(df, s.numeric() & s.glob('*_mm'))
   height_mm  width_mm
0      297.0     210.0
1      420.0     297.0