DropFeatures

class feature_engine.selection.DropFeatures(features_to_drop)[source]

DropFeatures() drops a list of variables indicated by the user from the dataframe.

More details in the User Guide.

Parameters
features_to_drop: str or list

Variable(s) to be dropped from the dataframe

Attributes
features_to_drop_:

The features that will be dropped.

feature_names_in_:

List with the names of features seen during fit.

n_features_in_:

The number of features in the train set used in fit.

Methods

fit:

This transformer does not learn any parameter.

fit_transform:

Fit to data, then transform it.

get_feature_names_out:

Get output feature names for transformation.

get_params:

Get parameters for this estimator.

set_params:

Set the parameters of this estimator.

transform:

Drops indicated features.

fit(X, y=None)[source]

This transformer does not learn any parameter.

Parameters
Xpandas dataframe of shape = [n_samples, n_features]

The input dataframe

ypandas Series, default = None

y is not needed for this transformer. You can pass y or None.

fit_transform(X, y=None, **fit_params)[source]

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)[source]

Get output feature names for transformation.

input_features: None

This parameter exists only for compatibility with the Scikit-learn pipeline, but has no functionality. You can pass a list of feature names or None.

Returns
feature_names_out: list

The feature names.

:rtype:py:class:~typing.List
get_params(deep=True)[source]

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_params(**params)[source]

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.

transform(X)[source]

Return dataframe with selected features.

Parameters
X: pandas dataframe of shape = [n_samples, n_features].

The input dataframe.

Returns
X_new: pandas dataframe of shape = [n_samples, n_selected_features]

Pandas dataframe with the selected features.

:rtype:py:class:~pandas.core.frame.DataFrame