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.
This exercise is part of the course
Dealing with Missing Data in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Impute airquality DataFrame with bfill method
bfill_imputed = airquality.___(___='___')