pm4py.algo.discovery.temporal_profile.variants.dataframe module#

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class pm4py.algo.discovery.temporal_profile.variants.dataframe.Parameters(*values)[source]#

Bases: Enum

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'#
BUSINESS_HOURS = 'business_hours'#
BUSINESS_HOUR_SLOTS = 'business_hour_slots'#
WORKCALENDAR = 'workcalendar'#
pm4py.algo.discovery.temporal_profile.variants.dataframe.apply(df: DataFrame, parameters: Dict[Any, Any] | None = None) → Dict[Tuple[str, str], Tuple[float, float]][source]#

Gets the temporal profile from a dataframe.

Implements the approach described in: Stertz, Florian, Jürgen Mangler, and Stefanie Rinderle-Ma. “Temporal Conformance Checking at Runtime based on Time-infused Process Models.” arXiv preprint arXiv:2008.07262 (2020).

Parameters:

  • df – Dataframe

  • parameters – Parameters, including: - Parameters.ACTIVITY_KEY => the column to use as activity - Parameters.START_TIMESTAMP_KEY => the column to use as start timestamp - Parameters.TIMESTAMP_KEY => the column to use as timestamp - Parameters.CASE_ID_KEY => the column to use as case ID

Returns:

Temporal profile of the dataframe

Return type:

temporal_profile