pm4py.filtering.filter_log_relative_occurrence_event_attribute(log: Union[EventLog, DataFrame], min_relative_stake: float, attribute_key: str = 'concept:name', level='cases', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') Union[EventLog, DataFrame][source]#

Filters the event log keeping only the events having an attribute value which occurs: - in at least the specified (min_relative_stake) percentage of events, when level=”events” - in at least the specified (min_relative_stake) percentage of cases, when level=”cases”

  • log – event log / Pandas dataframe

  • min_relative_stake (float) – minimum percentage of cases (expressed as a number between 0 and 1) in which the attribute should occur.

  • attribute_key (str) – the attribute to filter

  • level (str) – the level of the filter (if level=”events”, then events / if level=”cases”, then cases)

  • timestamp_key (str) – attribute to be used for the timestamp

  • case_id_key (str) – attribute to be used as case identifier

Return type:

Union[EventLog, pd.DataFrame]

import pm4py

filtered_dataframe = pm4py.filter_log_relative_occurrence_event_attribute(dataframe, 0.5, level='cases', case_id_key='case:concept:name', timestamp_key='time:timestamp')