AprioriAllPkg: A Python package for mining sequential patterns

This work presents a software package designed to support transparency, education, and methodological reproducibility in sequential pattern mining. The package concerns the AprioriAll algorithm, a classical method for discovering frequent ordered patterns in transactional data. Despite its historical importance, accessible and faithful implementations of AprioriAll remain difficult to find in modern software ecosystems, where available tools are often written in other programming languages or focused on newer and more complex approaches. The proposed package addresses this gap through a native Python implementation that is lightweight, modular, and independent of external libraries. Its structure follows the original stages of the AprioriAll mining process, allowing users to inspect, execute, and modify each component of the algorithm independently. This enables researchers and practitioners to reproduce complete mining workflows, test alternative configurations, and extend selected stages while preserving interpretability. By exposing the internal mechanics of candidate generation, support counting, pruning, and sequence expansion, the package provides a practical environment for understanding the computational challenges of temporal data mining. At the same time, it offers a convenient foundation for benchmarking, methodological validation, performance evaluation, and rapid prototyping of sequential data analysis workflows.