class feature_engine.encoding.RareLabelEncoder(tol=0.05, n_categories=10, max_n_categories=None, replace_with='Rare', variables=None, missing_values='raise', ignore_format=False)[source]#

The RareLabelEncoder() groups rare or infrequent categories in a new category called “Rare”, or any other name entered by the user.

For example in the variable colour, if the percentage of observations for the categories magenta, cyan and burgundy are < 5 %, all those categories will be replaced by the new label “Rare”.


Infrequent labels can also be grouped under a user defined name, for example ‘Other’. The name to replace infrequent categories is defined with the parameter replace_with.

The encoder will encode only categorical variables by default (type ‘object’ or ‘categorical’). You can pass a list of variables to encode.Alternatively, the encoder will find and encode all categorical variables (type ‘object’ or ‘categorical’).

With ignore_format=True you have the option to encode numerical variables as well. The procedure is identical, you can either enter the list of variables to encode, or the transformer will automatically select all variables.

The encoder first finds the frequent labels for each variable (fit). The encoder then groups the infrequent labels under the new label ‘Rare’ or by another user defined string (transform).

More details in the User Guide.

tol: float, default=0.05

The minimum frequency a label should have to be considered frequent. Categories with frequencies lower than tol will be grouped.

n_categories: int, default=10

The minimum number of categories a variable should have for the encoder to find frequent labels. If the variable contains less categories, all of them will be considered frequent.

max_n_categories: int, default=None

The maximum number of categories that should be considered frequent. If None, all categories with frequency above the tolerance (tol) will be considered frequent. If you enter 5, only the 5 most frequent categories will be retained and the rest grouped.

replace_with: string, intege or float, default=’Rare’

The value that will be used to replace infrequent categories.

variables: list, default=None

The list of categorical variables that will be encoded. If None, the encoder will find and transform all variables of type object or categorical by default. You can also make the transformer accept numerical variables, see the parameter ignore_format.

missing_values: string, default=’raise’

Indicates if missing values should be ignored or raised. If 'raise' the transformer will return an error if the the datasets to fit or transform contain missing values. If 'ignore', missing data will be ignored when learning parameters or performing the transformation.

ignore_format: bool, default=False

This transformer operates only on variables of type object or categorical. To override this behaviour and allow the transformer to transform numerical variables as well, set to True.

If ignore_format is False, the encoder will automatically select variables of type object or categorical, or check that the variables entered by the user are of type object or categorical. If True, the encoder will select all variables or accept all variables entered by the user, including those cast as numeric.

In short, set to True when you want to encode numerical variables.


Dictionary with the frequent categories, i.e., those that will be kept, per variable.


The group of variables that will be transformed.


List with the names of features seen during fit.


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


>>> import pandas as pd
>>> from feature_engine.encoding import RareLabelEncoder
>>> X = pd.DataFrame(dict(x1 = [1,2,3,4,5,6], x2 = ["b", "b", "b", "b", "b", "a"]))
>>> rle = RareLabelEncoder(n_categories = 1, tol=0.2)
>>> rle.fit(X)
>>> rle.transform(X)
   x1    x2
0   1     b
1   2     b
2   3     b
3   4     b
4   5     b
5   6  Rare



Find frequent categories.


Fit to data, then transform it.


Get output feature names for transformation.


Get parameters for this estimator.


Set the parameters of this estimator.


Group rare categories

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

Learn the frequent categories for each variable.

X: pandas dataframe of shape = [n_samples, n_features]

The training input samples. Can be the entire dataframe, not just selected variables

y: None

y is not required. 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.

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).


Additional fit parameters.

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.


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

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_.

feature_names_out: list

Transformed feature names.


List[Union[str, int]] ..


Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.


A MetadataRequest encapsulating routing information.


Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.


Parameter names mapped to their values.


inverse_transform is not implemented for this transformer.


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.


Estimator parameters.

selfestimator instance

Estimator instance.


Group infrequent categories. Replace infrequent categories by the string ‘Rare’ or any other name provided by the user.

X: pandas dataframe of shape = [n_samples, n_features]

The input samples.

X: pandas dataframe of shape = [n_samples, n_features]

The dataframe where rare categories have been grouped.


DataFrame ..