Visualize backward fill imputation
To understand the quality of imputations, it is important to analyze how the imputations vary with respect to the actual dataset. The quickest way to do so is by visualizing the imputations.
In the previous exercise, you visualized the time-series forward filled imputation of airquality DataFrame. In this exercise, you will visualize the backward filled imputation of airquality DataFrame.
Diese Übung ist Teil des Kurses
<Kurs>Dealing with Missing Data in Python</Kurs>Interaktive praktische Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Impute airquality DataFrame with bfill method
bfill_imputed = airquality.___(___='___')