Missingno: a missing data visualization suite

Algorithmic models and outputs are only as good as the data they are computed on. As the popular saying goes: garbage in, garbage out. In tabular datasets, it is usually relatively easy to, at a glance, understand patterns of missing data (or nullity) of individual rows, columns, and entries. However, it is far harder to see patterns in the missingness of data that extend between them. Understanding such patterns in data is beneficial, if not outright critical, to most applications. missingno is a Python package for visualizing missing data. It works by converting tabular data matrices into boolean masks based on whether individual entries contain data (which evaluates to true) or left empty (which evaluates to false). This "nullity matrix" is then exposed to user assessment through a variety of special-purpose data visualizations.