pm4py.discovery.discover_eventually_follows_graph(log: Union[EventLog, DataFrame], activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') Dict[Tuple[str, str], int][source]#

Gets the eventually follows graph from a log object.

The eventually follows graph is a dictionary associating to every couple of activities which are eventually following each other the number of occurrences of this relation.

  • log – event log / Pandas dataframe

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

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

  • case_id_key (str) – attribute to be used as case identifier

Return type:

Dict[Tuple[str, str], int]

import pm4py

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