# 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 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.
## 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:
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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