pm4pyminimal 2.7.11.3

Process mining for Python

# pm4py 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.

## Documentation / API The full documentation of pm4py can be found at https://pm4py.fit.fraunhofer.de

## First Example 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”)

## Installation 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

## 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, stringdist, tqdm * Optional requirements (not installed by default): scikit-learn, pyemd, pyvis, jsonschema, polars, openai, pywin32, python-dateutil, requests, workalendar, pygetwindow, pynput

## Release Notes To track the incremental updates, please refer to the CHANGELOG 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](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}, }