Ever since I came across LittleMCAR’s test in R for finding out the significance of the association between the missingness of variables, I have been searching for a similar test in python. This article will shed some light on performing a similar test in python. In this article, I will walk you through a set of codes, which analyses on missing values in categorical data.

Before deep-diving into testing missing data mechanisms, let’s just understand them clearly.

Let’s say we have a dataset, and we need to analyze those columns for missing values as well as for the relationship between variables. By analyzing the mechanisms, we can better decide how we want to handle them.

There are three different mechanisms by which data goes missing in any dataset.

1. Missing completely at random: (MCAR)

2. Missing at Random: (MAR)

3. Missing not at Random: (MNAR)

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