XeroGraph: enhancing data integrity in the presence of missing values with statistical and predictive analysis

Missing data present a pervasive challenge in data analysis, potentially biasing outcomes and undermining conclusions if not addressed properly. Missing data are commonly classified into Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). While MCAR poses a minimal risk of data distortion, both MAR and MNAR can seriously affect the results of subsequent analyses. Therefore, it is important to know the type of missing data and appropriately handle them.

To facilitate efficient handling of missing data, we introduce a Python package named XeroGraph that is designed to evaluate data quality, categorize the nature of missingness, and guide imputation decisions. By comparing how various imputation methods influence underlying distributions, XeroGraph provides a systematic framework that supports more accurate and transparent analyses. Through its comprehensive preliminary assessments and user-friendly interface, this package facilitates the selection of optimal strategies tailored to the specific missing data mechanisms present in a dataset. In doing so, XeroGraph may significantly improve the validity and reproducibility of research findings, making it a valuable tool for professionals in data-intensive fields.

XeroGraph is compatible with all operating systems and requires Python version 3.9 or higher. It can be freely downloaded from PyPI (https://pypi.org/project/XeroGraph). The source code is accessible on GitHub (https://github.com/kazilab/XeroGraph), and comprehensive documentation is available at Read the Docs (https://xerograph.readthedocs.io). This software is distributed under the Apache License 2.0.