pm4py.statistics.overlap.interval_events.pandas.get module#

class pm4py.statistics.overlap.interval_events.pandas.get.Parameters(*values)[source]#

Bases: Enum

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
pm4py.statistics.overlap.interval_events.pandas.get.apply(df: DataFrame, parameters: Dict[str | Parameters, Any] | None = None) → List[int][source]#

Counts the intersections of each interval event with the other interval events of the log (all the events are considered, not looking at the activity)

Parameters:

  • df – Pandas dataframe

  • parameters – Parameters of the algorithm, including: - Parameters.START_TIMESTAMP_KEY => the attribute to consider as start timestamp - Parameters.TIMESTAMP_KEY => the attribute to consider as timestamp

Returns:

For each interval event, ordered by the order of appearance in the log, associates the number of intersecting events.

Return type:

overlap