CategoricalImputer

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

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

The CategoricalImputer() 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.

Parameters

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’

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

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 imputer 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 imputer will select all variables or accept all variables entered by the user, including those cast as numeric.

Attributes

imputer_dict_:

Dictionary with the values to replace missing data in each variable.

variables_:

The group of variables that will be transformed.

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
>>> import numpy as np
>>> from feature_engine.imputation import CategoricalImputer
>>> X = pd.DataFrame(dict(
>>>        x1 = [np.nan,1,1,0,np.nan],
>>>        x2 = ["a", np.nan, "b", np.nan, "a"],
>>>        ))
>>> ci = CategoricalImputer(imputation_method='frequent')
>>> ci.fit(X)
>>> ci.transform(X)
    x1 x2
0  NaN  a
1  1.0  a
2  1.0  b
3  0.0  a
4  NaN  a

Methods

fit:	Learn the most frequent category or assign arbitrary value to variable.
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.
transform:	Impute missing data.

fit(X, y=None)

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

Parameters

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)

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_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns

routingMetadataRequest

A MetadataRequest encapsulating routing information.

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.

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)

Replace missing data with the learned parameters.

Parameters

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

The data to be transformed.

Returns

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

The dataframe without missing values in the selected variables.

rtype

DataFrame ..