pm4py.ml.extract_outcome_enriched_dataframe#
- pm4py.ml.extract_outcome_enriched_dataframe(log: EventLog | DataFrame, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name', start_timestamp_key: str = 'time:timestamp') DataFrame[source]#
Inserts additional columns in the dataframe which are computed on the overall case, so they model the outcome of the case.
- Parameters:
log – event log / Pandas dataframe
activity_key (
str) – attribute to be used for the activitytimestamp_key (
str) – attribute to be used for the timestampcase_id_key (
str) – attribute to be used as case identifierstart_timestamp_key (
str) – attribute to be used as start timestamp
- Return type:
pd.DataFrame
import pm4py enriched_df = pm4py.extract_outcome_enriched_dataframe(log, activity_key='concept:name', timestamp_key='time:timestamp', case_id_key='case:concept:name', start_timestamp_key='time:timestamp')