API Reference

class feature_engine.imputation.CategoricalImputer(imputation_method='missing', fill_value='Missing', variables=None, return_object=False, ignore_format=False)[source]

The CategoricalImputer() replaces missing data in categorical variables by an arbitrary value or by the most frequent category.

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

If you want to impute numerical variables with this transformer, there are 2 ways of doing it:

Option 1: Cast your numerical variables as object in the input dataframe, before passing it to the transformer.

Option 2: Set ignore_format=True. Note that if you do this and do not pass the list of variables to impute, the imputer will automatically select and impute all variables in the dataframe.

imputation_method: str, default=’missing’

Desired method of imputation. Can be ‘frequent’ for frequent category imputation or ‘missing’ to impute with an arbitrary value.

fill_value: str, int, float, default=’Missing’

Only used when imputation_method='missing'. User-defined value to replace the missing data.

variables: list, default=None

The list of categorical variables that will be imputed. If None, the imputer 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 below.

return_object: bool, default=False

If working with numerical variables cast as object, decide whether to return the variables as numeric or re-cast them as object. Note that pandas will re-cast them automatically as numeric after the transformation with the mode or with an arbitrary number.

ignore_format: bool, default=False

Whether the format in which the categorical variables are cast should be ignored. If 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.



Dictionary with most frequent category or arbitrary value per variable.


The group of variables that will be transformed.


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



Learn the most frequent category, or assign arbitrary value to variable.


Impute missing data.


Fit to the data, than transform it.

fit(X, y=None)[source]

Learn the most frequent category if the imputation method is set to frequent.

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

The training dataset.

y: pandas Series, default=None

y is not needed in this imputation. You can pass None or y.

  • If the input is not a Pandas DataFrame.

  • If user enters non-categorical variables (unless ignore_format is True)


If there are no categorical variables in the df or the df is empty


Replace missing data with the learned parameters.

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

The data to be transformed.

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

The dataframe without missing values in the selected variables.


DataFrame ..


If the input is not a Pandas DataFrame


If the dataframe has different number of features than the df used in fit()


The CategoricalImputer() replaces missing data in categorical variables with an arbitrary value, like the string ‘Missing’ or by the most frequent category.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

from feature_engine.imputation import CategoricalImputer

# Load dataset
data = pd.read_csv('houseprice.csv')

# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
data.drop(['Id', 'SalePrice'], axis=1), data['SalePrice'], test_size=0.3, random_state=0)

# set up the imputer
imputer = CategoricalImputer(variables=['Alley', 'MasVnrType'])

# fit the imputer

# transform the data
train_t= imputer.transform(X_train)
test_t= imputer.transform(X_test)