pm4py 2.6.1

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 http://pm4py.org/

## 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 3.8.x / 3.9.x / 3.10.x / 3.11.x by invoking: pip install -U pm4py

## 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

## 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:

Berti, A., van Zelst, S.J., van der Aalst, W.M.P. (2019): Process Mining for Python (PM4Py): Bridging the Gap Between Process-and Data Science. In: Proceedings of the ICPM Demo Track 2019, co-located with 1st International Conference on Process Mining (ICPM 2019), Aachen, Germany, June 24-26, 2019. pp. 13-16 (2019). http://ceur-ws.org/Vol-2374/