NApy: Efficient Statistics in Python for Large-Scale Heterogeneous Data with Enhanced Support for Missing Data

TL;DR

NApy is a Python package that enables efficient, scalable statistical testing on large, heterogeneous datasets with missing data, outperforming existing tools in runtime and memory usage.

Contribution

NApy introduces a high-performance Python library with a C++ backend and parallelization for large-scale, missing data statistical analysis, filling a critical gap in existing tools.

Findings

NApy significantly reduces runtime compared to competitors.

NApy uses optimized C++ backend with OpenMP for scalability.

NApy handles large, missing data efficiently in interactive environments.

Abstract

Existing Python libraries and tools lack the ability to efficiently compute statistical test results for large datasets in the presence of missing values. This presents an issue as soon as constraints on runtime and memory availability become essential considerations for a particular usecase. Relevant research areas where such limitations arise include interactive tools and databases for exploratory analysis of biomedical data. To address this problem, we present the Python package NApy, which relies on a Numba and C++ backend with OpenMP parallelization to enable scalable statistical testing for mixed-type datasets in the presence of missing values. Both with respect to runtime and memory consumption, NApy outperforms competitor tools and baseline implementations with naive Python-based parallelization by orders of magnitude, thereby enabling on-the-fly analyses in interactive applications. NApy is publicly available at https://github.com/DyHealthNet/NApy.