Project Overview

MissMecha is a Python toolkit for simulating missing data mechanisms in a principled, unified, and reproducible way.

Frustrated by the fragmented landscape of missing data tools, we built MissMecha as the first Python library to systematically support structured missingness across numerical, categorical, and time series data.

MissMecha offers:

  • Support for MCAR, MAR, and MNAR mechanisms

  • A wide range of subtypes per mechanism, each with interpretable logic

  • Flexible global and column-wise missingness simulation

  • Built-in analysis modules for evaluating and benchmarking imputation methods

Motivation

In real-world machine learning and data science tasks, missingness is rarely random or uniform. Yet most existing tools either oversimplify missing patterns or focus narrowly on numerical features.

MissMecha closes this gap by providing a consistent, extensible framework for:

  • Designing realistic and customizable missing data scenarios

  • Evaluating imputation strategies under controlled conditions

  • Bridging statistical theory with practical machine learning workflows

Where to Start