Fast Capture of Cell-Level Provenance in Numpy

Effective provenance tracking enhances reproducibility, governance, and data quality in array workflows.However, significant challenges arise in capturing this provenance, including: (1) rapidly evolving APIs, (2) diverse operation types, and (3) large-scale datasets.To address these challenges, this paper presents a prototype annotation system designed for arrays, which captures cell-level provenance specifically within the numpy library.With this prototype, we explore straightforward memory optimizations that substantially reduce annotation latency.We envision this provenance capture approach for arrays as part of a broader governance system for tracking for structured data workflows and diverse data science applications.