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  3. Dealing with Missing Data in Python

Exercise

Finding correlations in your data

Finding correlations between missing data helps you gain a deeper understanding of the type of missing data as well as provides suitable ways in which the missing values can be addressed. In the last video exercise, you learned two important techniques for visually detecting correlation between missing data: heatmaps and dendrograms.

In this exercise, you'll create a missingness heatmap and dendrogram for the diabetes dataset using the missingno package. It has been imported as msno.

Instructions 1/2

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  • Create a missingness heatmap for the diabetes DataFrame.