pm4py.stats

The pm4py.stats module contains the statistics offered in pm4py

Functions

get_activity_position_summary(log, activity)	Given an event log, returns a dictionary which summarize the positions of the activities in the different cases of the event log.
get_all_case_durations(log[, ...])	Gets the durations of the cases in the event log
get_case_arrival_average(log[, ...])	Gets the average difference between the start times of two consecutive cases
get_case_duration(log, case_id[, ...])	Gets the duration of a specific case
get_case_overlap(log[, activity_key, ...])	Associates to each case in the log the number of cases concurrently open
get_cycle_time(log[, activity_key, ...])	Calculates the cycle time of the event log.
get_end_activities(log[, activity_key, ...])	Returns the end activities of a log
get_event_attribute_values(log, attribute[, ...])	Returns the values for a specified (event) attribute
get_event_attributes(log)	Returns the attributes at the event level of the log
get_frequent_trace_segments(log, min_occ[, ...])	Get the traces (segments of activities) from an event log object.
get_minimum_self_distance_witnesses(log[, ...])	This function derives the minimum self distance witnesses.
get_minimum_self_distances(log[, ...])	This algorithm computes the minimum self-distance for each activity observed in an event log.
get_rework_cases_per_activity(log[, ...])	Find out for which activities of the log the rework (more than one occurrence in the trace for the activity) occurs.
get_service_time(log[, aggregation_measure, ...])	Gets the activities' (average/median/...) service time in the provided event log
get_start_activities(log[, activity_key, ...])	Returns the start activities from a log object
get_stochastic_language(*args, **kwargs)	Gets the stochastic language from the provided object
get_trace_attribute_values(log, attribute[, ...])	Returns the values for a specified trace attribute
get_trace_attributes(log)	Gets the attributes at the trace level of a log object
get_variants(log[, activity_key, ...])	Gets the variants from the log
get_variants_as_tuples(log[, activity_key, ...])	Gets the variants from the log (where the keys are tuples and not strings)
get_variants_paths_duration(log[, ...])	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).
split_by_process_variant(log[, ...])	Splits an event log into sub-dataframes for each process variant.