pm4py.discovery.discover_dfg

pm4py.discovery.discover_dfg(log: EventLog | DataFrame, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') → Tuple[dict, dict, dict]

Discovers a Directly-Follows Graph (DFG) from a log.

This method returns a tuple containing: - A dictionary with pairs of directly-following activities as keys and the frequency of the relationship as values. - A dictionary of start activities with their respective frequencies. - A dictionary of end activities with their respective frequencies.

Parameters:

• log – Event log or Pandas DataFrame.

• activity_key (str) – Attribute to be used for the activity (default: “concept:name”).

• timestamp_key (str) – Attribute to be used for the timestamp (default: “time:timestamp”).

• case_id_key (str) – Attribute to be used as case identifier (default: “case:concept:name”).

Returns:

A tuple of three dictionaries: (dfg, start_activities, end_activities).

Return type:

Tuple[dict, dict, dict]

import pm4py

dfg, start_activities, end_activities = pm4py.discover_dfg(
    dataframe,
    case_id_key='case:concept:name',
    activity_key='concept:name',
    timestamp_key='time:timestamp'
)