.. _mean_median_imputer: .. currentmodule:: feature_engine.imputation MeanMedianImputer ================= The :class:`MeanMedianImputer()` replaces missing data with the mean or median of the variable. It works only with numerical variables. You can pass the list of variables you want to impute, or alternatively, the imputer will automatically select all numerical variables in the train set. Note that in symetrical distributions, the mean and the median are very similar. But in skewed distributions, the median is a better representation of the majority, as the mean is biased to extreme values. The following image was taken from Wikipedia. The image links to the use license. .. figure:: ../../images/1024px-Relationship_between_mean_and_median_under_different_skewness.png :align: center :target: https://commons.wikimedia.org/wiki/File:Relationship_between_mean_and_median_under_different_skewness.png With the `fit()` method, the transformer learns and stores the mean or median values per variable. Then it uses these values in the `transform()` method to transform the data. Below a code example using the House Prices Dataset (more details about the dataset :ref:`here `). First, let's load the data and separate it into train and test: .. code:: python import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from feature_engine.imputation import MeanMedianImputer # Load dataset data = pd.read_csv('houseprice.csv') # Separate into train and test sets X_train, X_test, y_train, y_test = train_test_split( data.drop(['Id', 'SalePrice'], axis=1), data['SalePrice'], test_size=0.3, random_state=0, ) Now we set up the :class:`MeanMedianImputer()` to impute in this case with the median and only 2 variables from the dataset. .. code:: python # set up the imputer median_imputer = MeanMedianImputer( imputation_method='median', variables=['LotFrontage', 'MasVnrArea'] ) # fit the imputer median_imputer.fit(X_train) With fit, the :class:`MeanMedianImputer()` learned the median values for the indicated variables and stored it in one of its attributes. We can now go ahead and impute both the train and the test sets. .. code:: python # transform the data train_t= median_imputer.transform(X_train) test_t= median_imputer.transform(X_test) Note that after the imputation, if the percentage of missing values is relatively big, the variable distribution will differ from the original one (in red the imputed variable): .. code:: python fig = plt.figure() ax = fig.add_subplot(111) X_train['LotFrontage'].plot(kind='kde', ax=ax) train_t['LotFrontage'].plot(kind='kde', ax=ax, color='red') lines, labels = ax.get_legend_handles_labels() ax.legend(lines, labels, loc='best') .. image:: ../../images/medianimputation.png More details ^^^^^^^^^^^^ In the following Jupyter notebook you will find more details on the functionality of the :class:`MeanMedianImputer()`, including how to select numerical variables automatically. You will also see how to navigate the different attributes of the transformer to find the mean or median values of the variables. - `Jupyter notebook `_ For more details about this and other feature engineering methods check out these resources: - `Feature engineering for machine learning `_, online course. - `Python Feature Engineering Cookbook `_, book.