Drop

class skrub.Drop

Drop the selected DataFrame’s column unconditionally.

A note on using single column transformations

Drop is a type of single column transformation . Unlike most scikit-learn estimators, its fit, transform and fit_transform methods expect a single column (e.g. Series) not a full dataframe. To apply this transformer to one or more columns in a dataframe, use it in a ApplyToCols or a TableVectorizer.

To apply to all columns:

ApplyToCols(Drop())

To apply to selected columns:

ApplyToCols(Drop(), cols=['col_name_1', 'col_name_2'])

The other columns are kept in their original order. A ValueError is raised if any of the provided column names are not in the dataframe. This transformer is different from DropCols in that it is designed to be used with other transformers, for example to remove all columns with a given type from a transformation.

Parameters:

colslist of str, str or selector

The columns to drop, or a selector. A single column name can be passed as a str: "col_name" is the same as ["col_name"]. See the selectors user guide for more info on selectors.

See also

DropCols

drop columns by name, or skrub selectors.

ApplyToCols

Can be used to Apply a transformer to selected columns in a dataframe.

TableVectorizer

Transform a dataframe to a numeric (vectorized) representation.

Examples

Drop is meant to be used with other transformers to drop columns with a given type or other property.

For example, if we want to vectorize a dataframe but drop the numeric columns, we can do:

>>> import pandas as pd
>>> from skrub import Drop
>>> df = pd.DataFrame({"num": [1,2,3], "text": ["hello", "world", "foo"]})
>>> df
   num  text
0    1  hello
1    2  world
2    3    foo
>>> from skrub import TableVectorizer
>>> TableVectorizer(numeric=Drop()).fit_transform(df)
    text_foo  text_hello  text_world
0       0.0         1.0         0.0
1       0.0         0.0         1.0
2       1.0         0.0         0.0

Here, only the “text” column is vectorized, and the “num” column is dropped entirely.

Methods

fit(column[, y])	Fit the transformer.
get_feature_names_out([input_features])	Get the output feature names.
get_params([deep])	Get parameters for this estimator.
set_output(*[, transform])	Default no-op implementation for set_output.
set_params(**params)	Set the parameters of this estimator.
set_transform_request(*[, column])	Configure whether metadata should be requested to be passed to the transform method.

fit_transform
transform

fit(column, y=None, **kwargs)

Fit the transformer.

This default implementation simply calls fit_transform() and returns self.

Subclasses should implement fit_transform and transform.

Parameters:

columna pandas or polars Series

Unlike most scikit-learn transformers, single-column transformers transform a single column, not a whole dataframe.

ycolumn or dataframe

Prediction targets.

**kwargs

Extra named arguments are passed to self.fit_transform().

Returns:

self

The fitted transformer.

get_feature_names_out(input_features=None)

Get the output feature names.

Parameters:

input_featuresarray_like of str, default=None

Input feature names. Ignored.

Returns:

list of str

The names of the output features.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

set_output(*, transform=None)

Default no-op implementation for set_output.

Skrub transformers already output dataframes of the correct type by default so there is usually no need for set_output to do anything.

Subclasses are of course free to redefine set_output (e.g. by inheriting from TransformerMixin before SingleColumnTransformer).

Parameters:

transformstr or None, default=None

Ignored.

Returns:

SingleColumnTransformer

Returns self.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_transform_request(*, column='$UNCHANGED$')

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to transform.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

columnstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for column parameter in transform.

Returns:

selfobject

The updated object.