class feature_engine.imputation.EndTailImputer(imputation_method='gaussian', tail='right', fold=3, variables=None)[source]#

The EndTailImputer() replaces missing data by a value at either tail of the distribution. It works only with numerical variables.

You can indicate the variables to impute in a list. Alternatively, the EndTailImputer() will automatically select all numerical variables.

The imputer first calculates the values at the end of the distribution for each variable (fit). The values at the end of the distribution are determined using the Gaussian limits, the the IQR proximity rule limits, or a factor of the maximum value:

Gaussian limits:
  • right tail: mean + 3*std

  • left tail: mean - 3*std

IQR limits:
  • right tail: 75th quantile + 3*IQR

  • left tail: 25th quantile - 3*IQR

where IQR is the inter-quartile range = 75th quantile - 25th quantile

Maximum value:
  • right tail: max * 3

  • left tail: not applicable

You can change the factor that multiplies the std, IQR or the maximum value using the parameter fold (we used fold=3 in the examples above).

The imputer then replaces the missing data with the estimated values (transform).

More details in the User Guide.

imputation_method: str, default=’gaussian’

Method to be used to find the replacement values. Can take ‘gaussian’, ‘iqr’ or ‘max’.

‘gaussian’: the imputer will use the Gaussian limits to find the values to replace missing data.

‘iqr’: the imputer will use the IQR limits to find the values to replace missing data.

‘max’: the imputer will use the maximum values to replace missing data. Note that if ‘max’ is passed, the parameter ‘tail’ is ignored.

tail: str, default=’right’

Indicates if the values to replace missing data should be selected from the right or left tail of the variable distribution. Can take values ‘left’ or ‘right’.

fold: int, default=3

Factor to multiply the std, the IQR or the Max values. Recommended values are 2 or 3 for Gaussian, or 1.5 or 3 for IQR.

variables: list, default=None

The list of variables to impute. If None, the imputer will select all numerical variables.


Dictionary with the values at the end of the distribution per variable.


The group of variables that will be transformed.


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



Learn values to replace missing data.


Impute missing data.


Fit to the data, then transform it.

fit(X, y=None)[source]#

Learn the values at the end of the variable distribution.

X: pandas dataframe of shape = [n_samples, n_features]

The training dataset.

y: pandas Series, 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.

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).


Additional fit parameters.

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.


Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.


Parameter names mapped to their values.


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.


Estimator parameters.

selfestimator instance

Estimator instance.


Replace missing data with the learned parameters.

X: pandas dataframe of shape = [n_samples, n_features]

The data to be transformed.

X_new: pandas dataframe of shape = [n_samples, n_features]

The dataframe without missing values in the selected variables.