Missing Data Imputation#

Feature-engine’s missing data imputers replace missing data by parameters estimated from data or arbitrary values pre-defined by the user.

The following table summarizes each imputer’s functionality:

Transformer	Numerical variables	Categorical variables	Description
`MeanMedianImputer()`	√	×	Replaces missing values with the mean or median
`ArbitraryNumberImputer()`	√	x	Replaces missing values with an arbitrary value
`EndTailImputer()`	√	×	Replaces missing values with a value at the end of the distribution
`CategoricalImputer()`	×	√	Replaces missing values with the most frequent category or an arbitrary string
`RandomSampleImputer()`	√	√	Replaces missing values with random value extractions from the variable
`AddMissingIndicator()`	√	√	Adds a binary variable to flag missing observations
`DropMissingData()`	√	√	Removes observations with missing data from the dataset

Imputers#