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 impute all numerical variables.

Attributes
imputer_dict_:

Dictionary with the mean or median values per variable.

variables_:

The group of variables that will be transformed.

n_features_in_:

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

Methods

fit:

Learn the mean or median values.

transform:

Impute missing data.

fit_transform:

Fit to the data, then transform it.

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_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

DataFrame ..