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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.

Questo esercizio fa parte del corso

Dealing with Missing Data in Python

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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