Using the missingno Python library to Identify and Visualise Missing Data Prior to Machine Learning

All others have a large and varying degree of missing values. Within the missingno library, there are four types of plots for visualising data completeness: the barplot, the matrix plot, the heatmap, and the dendrogram plot. Each has its own advantages for identifying missing data. Let's take a look at each of these in turn. The barplot provides a simple plot where each bar represents a column within the dataframe. The height of the bar indicates how complete that column is, i.e, how many non-null values are present.