# PM4Py

PM4Py is a python library that supports state-of-the-art process mining algorithms in Python. It is open source and intended to be used in both academia and industry projects.

PM4Py is managed and developed by PIS — Process Intelligence Solutions (https://processintelligence.solutions/), a spin-off from the Fraunhofer Institute for Applied Information Technology FIT where PM4Py was initially developed.

## Licensing

The open-source version of PM4Py, available on GitHub (https://github.com/process-intelligence-solutions/pm4py), is licensed under the GNU Affero General Public License version 3 (AGPL-3.0).

We offer a separate version of PM4Py for commercial use in closed-source environments under a different license. For more information about the licensing options for using PM4Py in closed-source settings, please visit https://processintelligence.solutions/pm4py#licensing.

## Documentation / API

The documentation of PM4Py can be found at https://processintelligence.solutions/pm4py/.

## First Example

Here is a simple example to spark your interest:

import pm4py

if __name__ == “__main__”:

log = pm4py.read_xes(‘<path-to-xes-log-file.xes>’) net, initial_marking, final_marking = pm4py.discover_petri_net_inductive(log) pm4py.view_petri_net(net, initial_marking, final_marking, format=”svg”)

## Installation PM4Py can be installed on Python 3.9.x / 3.10.x / 3.11.x / 3.12.x / 3.13.x / 3.14.x by invoking:

pip install -U pm4py

PM4Py is also running on older Python environments with different requirements sets, including:

  • Python 3.8 (3.8.10): third_party/old_python_deps/requirements_py38.txt

## Requirements

PM4Py depends on some other Python packages, with different levels of importance:

  • Essential requirements: numpy, pandas, deprecation, networkx

  • Normal requirements (installed by default with the PM4Py package, important for mainstream usage): graphviz, intervaltree, lxml, matplotlib, pydotplus, pytz, scipy, tqdm

  • Optional requirements (not installed by default): requests, pyvis, jsonschema, workalendar, pyarrow, scikit-learn, polars, openai, pyemd, pyaudio, pydub, pygame, pywin32, pygetwindow, pynput

## Release Notes

To track the incremental updates, please refer to the CHANGELOG.md file.

## Contributing

If you want to contribute to PM4Py, please review the [contributing guidelines and Contributor License Agreement (CLA)](https://processintelligence.solutions/pm4py/contributing).

## Third Party Dependencies

As scientific library in the Python ecosystem, we rely on external libraries to offer our features. In the /third_party folder, we list all the licenses of our direct dependencies. Please check the /third_party/LICENSES_TRANSITIVE file to get a full list of all transitive dependencies and the corresponding license.

## Citing PM4Py

If you are using PM4Py in your scientific work, please cite PM4Py as follows:

> Alessandro Berti, Sebastiaan van Zelst, Daniel Schuster. (2023). PM4Py: A process mining library for Python. > Software Impacts, 17, 100556. doi: 10.1016/j.simpa.2023.100556

[DOI](https://doi.org/10.1016/j.simpa.2023.100556) | [Article Link](https://www.sciencedirect.com/science/article/pii/S2665963823000933)

BiBTeX:

@article{pm4py, title = {PM4Py: A process mining library for Python}, journal = {Software Impacts}, volume = {17}, pages = {100556}, year = {2023}, issn = {2665-9638}, doi = {https://doi.org/10.1016/j.simpa.2023.100556}, url = {https://www.sciencedirect.com/science/article/pii/S2665963823000933}, author = {Alessandro Berti and Sebastiaan van Zelst and Daniel Schuster}, }

## Legal Notice

This repository is managed by Process Intelligence Solutions (PIS). Further information about PIS can be found online at https://processintelligence.solutions.

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