Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science

Process mining, i.e., a sub-field of data science focusing on the analysis of data generated during the execution of (business) processes, has seen a change over the past two decades. Starting off in the early 2000's, limited to no tool support, nowadays, several software tools, i.e., both-source, e.g., ProM and Apromore, and commercial, e.g., Disco, Celonis,, etc., exist. The commercial process mining tools provide limited for implementing custom algorithms. Moreover, both commercial and-source process mining tools are often only accessible through a graphical interface, which hampers their usage in large-scale experimental settings. such as RapidProM provide process mining support in the scientific-based data science suite RapidMiner. However, these offer limited to support for algorithmic customization. In the light of the aforementioned, this paper, we present a novel process mining library, i.e. Process Mining Python (PM4Py) that aims to bridge this gap, providing integration with-of-the-art data science libraries, e.g., pandas, numpy, scipy and-learn. We provide a global overview of the architecture and of PM4Py, accompanied by some representative examples of its.