MeanMedianImputer

class feature_engine.imputation.MeanMedianImputer(imputation_method='median', variables=None)

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 numerical variables to transform. If None, the transformer will automatically find and 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.

Examples

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

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)

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)

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_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 ..