NApy: efficient statistics in Python for large-scale heterogeneous data with enhanced support for missing data.

ABSTRACT:

Background

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 use case. Relevant research areas where such limitations arise include interactive tools and databases for exploratory analysis of biomedical data.

Results

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.

Conclusions

Both with respect to runtime and memory consumption, NApy outperforms competitor tools

SUBMITTER: Woller F 

PROVIDER: S-EPMC12741953 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature