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
Xandywith optional parametersfit_paramsand 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_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..