DropDuplicateFeatures

class feature_engine.selection.DropDuplicateFeatures(variables=None, missing_values='ignore', confirm_variables=False)

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.

Examples

>>> import pandas as pd
>>> from feature_engine.selection import DropDuplicateFeatures
>>> X = pd.DataFrame(dict(x1 = [1,1,1,1],
>>>                     x2 = [1,1,1,1],
>>>                     x3 = [True, False, False, False]))
>>> ddf = DropDuplicateFeatures()
>>> ddf.fit_transform(X)
    x1     x3
0   1   True
1   1  False
2   1  False
3   1  False

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.
get_support:	Get a mask, or integer index, of the features selected.
transform:	Remove duplicated features.

fit(X, y=None)

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)

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)

Get output feature names for transformation. In other words, returns the variable names of transformed dataframe.

Parameters

input_featuresarray or list, default=None

This parameter exits only for compatibility with the Scikit-learn pipeline.

• If None, then feature_names_in_ is used as feature names in.

• If an array or list, then input_features must match feature_names_in_.

Returns

feature_names_out: list

Transformed feature names.

rtype

List[Union[str, int]] ..

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns

routingMetadataRequest

A MetadataRequest encapsulating routing information.

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.

get_support(indices=False)

Get a mask, or integer index, of the features selected.

Parameters

indicesbool, default=False

If True, the return value will be an array of integers, rather than a boolean mask.

Returns

supportarray

An index that selects the retained features from a feature vector. If indices is False, this is a boolean array of shape [# input features], in which an element is True if its corresponding feature is selected for retention. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

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.

transform(X)

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 ..