Missing data poses a significant challenge in data science, affecting
decision-making processes and outcomes. Understanding what missing data is, how
it occurs, and why it is crucial to handle it appropriately is paramount when
working with real-world data, especially in tabular data, one of the most
commonly used data types in the real world. Three missing mechanisms are
defined in the literature: Missing Completely At Random (MCAR), Missing At
Random (MAR), and Missing Not At Random (MNAR), each presenting unique
challenges in imputation. Most existing work are focused on MCAR that is
relatively easy to handle. The special missing mechanisms of MNAR and MAR are
less explored and understood. This article reviews existing literature on
handling missing values. It compares and contrasts existing methods in terms of
their ability to handle different missing mechanisms and data types. It
identifies research gap in the existing literature and lays out potential
directions for future research in the field. The information in this review
will help data analysts and researchers to adopt and promote good practices for
handling missing data in real-world problems.

在数据科学中，缺失数据是一个重要的挑战，它对决策过程和结果产生影响。本文回顾了现有文献对处理缺失数据的方法进行了比较和对比，揭示了现有文献中的研究空白，并提出了未来研究的潜在方向。这个综述将帮助数据分析人员和研究人员在实际问题中采用和推广处理缺失数据的良好实践。