MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms

TL;DR

MissMecha is a comprehensive Python toolkit that enables simulation, visualization, and evaluation of missing data mechanisms in mixed-type tabular datasets, facilitating research and education on data quality issues.

Contribution

It introduces a unified, open-source platform supporting mechanism-aware missing data simulation, diagnostics, and imputation evaluation for both numerical and categorical data.

Findings

Supports MCAR, MAR, MNAR mechanisms

Includes visual diagnostics and testing utilities

Enables benchmarking and educational use

Abstract

Incomplete data is a persistent challenge in real-world datasets, often governed by complex and unobservable missing mechanisms. Simulating missingness has become a standard approach for understanding its impact on learning and analysis. However, existing tools are fragmented, mechanism-limited, and typically focus only on numerical variables, overlooking the heterogeneous nature of real-world tabular data. We present MissMecha, an open-source Python toolkit for simulating, visualizing, and evaluating missing data under MCAR, MAR, and MNAR assumptions. MissMecha supports both numerical and categorical features, enabling mechanism-aware studies across mixed-type tabular datasets. It includes visual diagnostics, MCAR testing utilities, and type-aware imputation evaluation metrics. Designed to support data quality research, benchmarking, and education,MissMecha offers a unified platform for researchers and practitioners working with incomplete data.