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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.

Cet exercice fait partie du cours

Dealing with Missing Data in Python

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Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Impute airquality DataFrame with bfill method
bfill_imputed = airquality.___(___='___')
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