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

arXiv: https://arxiv.org/abs/1905.06169 · HTML: https://mlsanity.com/paper/1905.06169/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 30 of 33)

- [SkillOps: Managing LLM Agent Skill Libraries as Self-Maintaining Software Ecosystems](https://mlsanity.com/paper/2605.13716/) (2026)
- [Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring](https://mlsanity.com/paper/2112.09641/) (2021)
- [A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes](https://mlsanity.com/paper/2102.07838/) (2021)
- [Text-Aware Predictive Monitoring of Business Processes](https://mlsanity.com/paper/2104.09962/) (2021)
- [Probability Estimation of Uncertain Process Trace Realizations](https://mlsanity.com/paper/2108.08615/) (2021)
- [PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data](https://mlsanity.com/paper/2103.05564/) (2021)
- [DABL: Detecting Semantic Anomalies in Business Processes Using Large Language Models](https://mlsanity.com/paper/2406.15781/) (2024)
- [CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version)](https://mlsanity.com/paper/2103.10507/) (2021)
- [A Framework for Explainable Concept Drift Detection in Process Mining](https://mlsanity.com/paper/2105.13155/) (2021)
- [xSemAD: Explainable Semantic Anomaly Detection in Event Logs Using Sequence-to-Sequence Models](https://mlsanity.com/paper/2406.19763/) (2024)
- [Object Type Clustering using Markov Directly-Follow Multigraph in Object-Centric Process Mining](https://mlsanity.com/paper/2206.11017/) (2022)
- [Can recurrent neural networks learn process model structure?](https://mlsanity.com/paper/2212.06430/) (2022)
- [Automata from Agent Traces: Failure and Next-Step Prediction](https://mlsanity.com/paper/2608.23670/) (2026)
- [Extracting Semantic Process Information from the Natural Language in Event Logs](https://mlsanity.com/paper/2103.11761/) (2021)
- [SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining](https://mlsanity.com/paper/2109.08501/) (2021)
- [Clustering Object-Centric Event Logs](https://mlsanity.com/paper/2207.12764/) (2022)
- [Can deep neural networks learn process model structure? An assessment framework and analysis](https://mlsanity.com/paper/2202.11985/) (2022)
- [Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors](https://mlsanity.com/paper/2206.13109/) (2022)
- [Nirdizati: an Advanced Predictive Process Monitoring Toolkit](https://mlsanity.com/paper/2210.09688/) (2022)
- [Understanding Revision Behavior in Adaptive Writing Support Systems for Education](https://mlsanity.com/paper/2306.10304/) (2023)
- [Timeline-based Process Discovery](https://mlsanity.com/paper/2401.04114/) (2023)
- [The Role of Time and Data: Online Conformance Checking in the Manufacturing Domain](https://mlsanity.com/paper/2105.01454/) (2021)
- [Alignment-based conformance checking over probabilistic events](https://mlsanity.com/paper/2209.04309/) (2022)
- [Identifying the Key Attributes in an Unlabeled Event Log for Automated Process Discovery](https://mlsanity.com/paper/2301.12829/) (2023)
- [Discovering Hierarchical Process Models: an Approach Based on Events Clustering](https://mlsanity.com/paper/2303.13531/) (2023)
- [Recording of 50 Business Assignments](https://mlsanity.com/paper/2308.12211/) (2023)
- [Navigating Process Mining: A Case study using pm4py](https://mlsanity.com/paper/2409.11294/) (2024)
- [The Impact of Event Data Partitioning on Privacy-aware Process Discovery](https://mlsanity.com/paper/2507.06008/) (2025)
- [HealthProcessAI: A Technical Framework and Proof-of-Concept for LLM-Enhanced Healthcare Process Mining](https://mlsanity.com/paper/2508.21540/) (2025)
- [Evaluating LLM-Based Process Explanations under Progressive Behavioral-Input Reduction](https://mlsanity.com/paper/2510.09732/) (2025)

## Abstract (arXiv)

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