DropFeatures

class feature_engine.selection.DropFeatures(features_to_drop)

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.

Examples

>>> import pandas as pd
>>> from feature_engine.selection import DropFeatures
>>> X = pd.DataFrame(dict(x1 = [1,2,3,4],
>>>                         x2 = ["a", "a", "b", "c"],
>>>                         x3 = [True, False, False, True]))
>>> df = DropFeatures(features_to_drop=["x2"])
>>> df.fit_transform(X)
    x1     x3
0   1   True
1   2  False
2   3  False
3   4   True

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_support:	Get a mask, or integer index, of the features selected.
get_params:	Get parameters for this estimator.
set_params:	Set the parameters of this estimator.
transform:	Drops indicated features.

fit(X, y=None)

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)

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_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 ..