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

PM4Py – A Process Mining Library for Python

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class pm4py.statistics.passed_time.pandas.variants.prepost.Parameters(*values)[source]#

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][source]#

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