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
andy
with optional parametersfit_params
and returns a transformed version ofX
.- 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
- 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