MeanMedianImputer¶

class feature_engine.imputation.MeanMedianImputer(imputation_method='median', variables=None)[source]¶

The MeanMedianImputer() replaces missing data by the mean or median value of the variable. It works only with numerical variables.

You can pass a list of variables to impute. Alternatively, the MeanMedianImputer() will automatically select all variables of type numeric in the training set.

More details in the User Guide.

Parameters

imputation_method: str, default=’median’: Desired method of imputation. Can take ‘mean’ or ‘median’.
variables: list, default=None: The list of variables to impute. If None, the imputer will select all numerical variables.

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.

Methods

fit:	Learn the mean or median values.
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)[source]¶

Learn the mean or median values.

Parameters

X: pandas dataframe of shape = [n_samples, n_features]: The training dataset.
y: pandas series or None, 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.

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.

Parameters

input_features: str, list, default=None: If None, then the names of all the variables in the transformed dataset is returned. If list with feature names, the features in the list will be returned. This parameter exists mostly for compatibility with the Scikit-learn Pipeline.

Returns

feature_names_out: list: The feature names.
:rtype:py:class:~typing.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.

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]¶

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:py:class:~pandas.core.frame.DataFrame

Missing Data Imputation

ArbitraryNumberImputer