pm4py.stats.get_variants_paths_duration#

pm4py.stats.get_variants_paths_duration(log: EventLog | DataFrame, activity_key: str = 'concept:name', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name', variant_column: str = '@@variant_column', variant_count: str = '@@variant_count', index_in_trace_column: str = '@@index_in_trace', cumulative_occ_path_column: str = '@@cumulative_occ_path_column', times_agg: str = 'mean') → DataFrame[source]#

Method that associates to a log object a Pandas dataframe aggregated by variants and positions (inside the variant). Each row is associated to different columns: - The variant - The position (in the variant) - The source activity (of the path) - The target activity (of the path) - An aggregation of the times between the two activities (for example, the mean over all the cases of the same variant) - The cumulative occurrences of the path inside the case (for example, the first A->B would be associated to 0,

and the second A->B would be associated to 1)

Parameters:

  • log – Event log

  • activity_key (str) – attribute to be used for the activity

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

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

  • variant_column (str) – name of the utility column that stores the variant’s tuple

  • variant_count (str) – name of the utility column that stores the variant’s number of occurrences

  • index_in_trace_column (str) – name of the utility column that stores the index of the event in the case

  • cumulative_occ_path_column (str) – name of the column that stores the cumulative occurrences of the path inside the case

  • times_agg (str) – aggregation (mean, median) to be used

Return type:

pd.DataFrame

import pandas as pd
import pm4py

dataframe = pd.read_csv('tests/input_data/receipt.csv')
dataframe = pm4py.format_dataframe(dataframe)

var_paths_durs = pm4py.get_variants_paths_duration(dataframe)
print(var_paths_durs)
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