Union[EventLog, DataFrame], grouper_freq='W', activity_key='concept:name', timestamp_key='time:timestamp', case_id_key=None, start_timestamp_key='time:timestamp', resource_key='org:resource') DataFrame[source]#

Extracts a dataframe containing the temporal features of the provided log object

Implements the approach described in the paper: Pourbafrani, Mahsa, Sebastiaan J. van Zelst, and Wil MP van der Aalst. “Supporting automatic system dynamics model generation for simulation in the context of process mining.” International Conference on Business Information Systems. Springer, Cham, 2020.

  • log – log object (event log / Pandas dataframe)

  • grouper_freq (str) – the grouping frequency (D, W, M, Y) to use

  • activity_key (str) – the attribute to be used as activity

  • timestamp_key (str) – the attribute to be used as timestamp

  • case_id_key – (if provided, otherwise default) the attribute to be used as case identifier

  • resource_key (str) – the attribute to be used as resource

  • start_timestamp_key (str) – the attribute to be used as start timestamp

Return type:


import pm4py

temporal_features_df = pm4py.extract_temporal_features_dataframe(dataframe, activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')