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
Xandywith optional parametersfit_paramsand returns a transformed version ofX.- 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