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]#

Associates a pandas DataFrame aggregated by variants and their positions within each variant. Each row includes: - The variant - The position within the variant - The source activity of the path - The target activity of the path - An aggregation of the times between the two activities (e.g., mean) - The cumulative occurrences of the path within the case

Return type:

DataFrame

Parameters:

log – Event log (EventLog or pandas DataFrame).
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 the 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 occurrence count.
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 within the case.
times_agg (str) – Aggregation function to be used for time differences (e.g., “mean”, “median”).

Returns:

A pandas DataFrame with the aggregated variant paths and durations.

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)