DropDuplicateFeatures#
- class feature_engine.selection.DropDuplicateFeatures(variables=None, missing_values='ignore', confirm_variables=False)[source]#
DropDuplicateFeatures() finds and removes duplicated features in a dataframe.
Duplicated features are identical features, regardless of the variable or column name. If they show the same values for every observation, then they are considered duplicated.
This transformer works with numerical and categorical variables. The user can indicate a list of variables to examine. Alternatively, the transformer will evaluate all the variables in the dataset.
The transformer will first identify and store the duplicated variables. Next, the transformer will drop these variables from a dataframe.
More details in the User Guide.
- Parameters
- variables: list, default=None
The list of variables to evaluate. If None, the transformer will evaluate all variables in the dataset.
- missing_values: str, default=ignore
Whether the missing values should be raised as error or ignored when determining correlation. Takes values ‘raise’ and ‘ignore’.
- confirm_variables: bool, default=False
If set to True, variables that are not present in the input dataframe will be removed from the list of variables. Only used when passing a variable list to the parameter
variables. See parameter variables for more details.
- Attributes
- features_to_drop_:
Set with the duplicated features that will be dropped.
- duplicated_feature_sets_:
Groups of duplicated features. Each list is a group of duplicated features.
- variables_:
The variables that will be considered for the feature selection procedure.
- 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:
Find duplicated features.
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:
Remove duplicated features.
- fit(X, y=None)[source]#
Find duplicated features.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features]
The input dataframe.
- y: 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