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 impute 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.

More details in the User Guide.

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.

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 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.


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.


Replace missing data with the learned parameters.

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

The data to be transformed.

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

The dataframe without missing values in the selected variables.