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
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.
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 here).
First, let’s load the data and separate it into train and test:
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
MeanMedianImputer() to impute in this case with the median
and only 2 variables from the dataset.
# set up the imputer median_imputer = MeanMedianImputer( imputation_method='median', variables=['LotFrontage', 'MasVnrArea'] ) # fit the imputer median_imputer.fit(X_train)
With fit, the
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.
# 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):
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')
In the following Jupyter notebook you will find more details on the functionality of the
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.
For more details about this and other feature engineering methods check out these resources: