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  3. Dealing with Missing Data in Python

Exercise

Delete MCAR

Analyzing and appropriately treating missing values is a tricky job. However, dealing with them is very simple if the number of missing values is very small. In the video exercise, you learned how to properly identify, when to drop, and remove missing data.

In this exercise, you'll listwise delete the rows where the Glucose column has missing values. The diabetes DataFrame and the missingnopackage as msno has already been loaded for you.

Note that we've used a proprietary display() function instead of plt.show() to make it easier for you to view the output.

Instructions 1/3

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  • Visualize the missingness matrix of diabetes.