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.
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.
# Import missingno
import missingno as msno
# Plot missingness heatmap of diabetes
___.___(___)
# Show plot
plt.show()