pm4py is a python library that supports (state-of-the-art) process mining algorithms in python. It is open source (licensed under GPL) and intended to be used in both academia and industry projects. pm4py is a product of the Fraunhofer Institute for Applied Information Technology.
The full documentation of pm4py can be found at https://pm4py.fit.fraunhofer.de
A very simple example, to whet your appetite:
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")pm4py can be installed on Python 3.9.x / 3.10.x / 3.11.x / 3.12.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
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
To track the incremental updates, please refer to the CHANGELOG file.
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.
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 | Article Link
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},
}