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

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