DropDuplicateFeatures

class feature_engine.selection.DropDuplicateFeatures(variables=None, missing_values='ignore')[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_valuesstr, default=ignore

Takes values ‘raise’ and ‘ignore’. Whether the missing values should be raised as error or ignored when finding duplicated features.

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.

n_features_in_:

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

Methods

fit:

Find duplicated features.

transform:

Remove duplicated features.

fit_transform:

Fit to data. Then transform it.

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 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_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

DataFrame ..