pm4py.statistics.passed_time.pandas.variants.prepost module

class pm4py.statistics.passed_time.pandas.variants.prepost.Parameters(*values)

Bases: Enum

ATTRIBUTE_KEY = 'pm4py:param:attribute_key'

ACTIVITY_KEY = 'pm4py:param:activity_key'

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'

CASE_ID_KEY = 'pm4py:param:case_id_key'

MAX_NO_POINTS_SAMPLE = 'max_no_of_points_to_sample'

KEEP_ONCE_PER_CASE = 'keep_once_per_case'

BUSINESS_HOURS = 'business_hours'

BUSINESS_HOUR_SLOTS = 'business_hour_slots'

WORKCALENDAR = 'workcalendar'

pm4py.statistics.passed_time.pandas.variants.prepost.apply(df: DataFrame, activity: str, parameters: Dict[Any, Any] | None = None) → Dict[str, Any]

Gets the time passed from each preceding activity and to each succeeding activity

Parameters:

• df – Dataframe

• activity – Activity that we are considering

• parameters – Possible parameters of the algorithm

Returns:

Dictionary containing a ‘pre’ key with the list of aggregated times from each preceding activity to the given activity and a ‘post’ key with the list of aggregates times from the given activity to each succeeding activity

Return type:

dictio