DropConstantFeatures#

class feature_engine.selection.DropConstantFeatures(variables=None, tol=1, missing_values='raise', confirm_variables=False)[source]#

DropConstantFeatures() drops constant and quasi-constant variables from a dataframe. Constant variables show the same value in all the observations in the dataset. Quasi-constant variables show the same value in almost all the observations in the dataset.

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 constant and quasi-constant 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.
tol: float,int, default=1: Threshold to detect constant/quasi-constant features. Variables showing the same value in a percentage of observations greater than tol will be considered constant / quasi-constant and dropped. If tol=1, the transformer removes constant variables. Else, it will remove quasi-constant variables. For example, if tol=0.98, the transformer will remove variables that show the same value in 98% of the observations.
missing_values: str, default=raises: Whether the missing values should be raised as error, ignored or included as an additional value of the variable. Takes values ‘raise’, ‘ignore’, ‘include’.
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_:: List with constant and quasi-constant 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.

See also

sklearn.feature_selection.VarianceThreshold

Notes

This transformer is a similar concept to the VarianceThreshold from Scikit-learn, but it evaluates number of unique values instead of variance.

Examples

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

Additionally, you can set the Threshold for quasi-constant features:

>>> X = pd.DataFrame(dict(x1 = [1,1,1,1],
>>>                      x2 = ["a", "a", "b", "c"],
>>>                      x3 = [True, False, False, False]))
>>> dcf = DropConstantFeatures(tol = 0.75)
>>> dcf.fit_transform(X)
    x2
0  a
1  a
2  b
3  c

Methods

fit:	Find constant and quasi-constant 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 constant and quasi-constant features.

fit(X, y=None)[source]#

Find constant and quasi-constant 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_feature_names_out(input_features=None)[source]#

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)[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.

get_support(indices=False)[source]#

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)[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 ..