cols#

skrub.selectors.cols(*columns)[source]#

Select columns by name.

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

>>> s.select(df, s.cols('height_mm', 'ID'))
   height_mm  ID
0      297.0   4
1      420.0   3

When this selector is used on its own, an error is raised if some columns are missing:

>>> s.select(df, s.cols('width_mm', 'depth_mm'))
Traceback (most recent call last):
    ...
ValueError: The following columns are requested for selection but missing from dataframe: ['depth_mm']

However, no error is raised when this selector is combined with other selectors:

>>> s.select(df, s.all() & s.cols('width_mm', 'depth_mm'))
   width_mm
0     210.0
1     297.0

In all skrub functions that accept a selector, a list of column names can be passed and cols will be used to turn it into a selector.

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

cols#

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