pm4py.vis.view_events_distribution_graph#
- pm4py.vis.view_events_distribution_graph(log: EventLog | DataFrame, distr_type: str = 'days_week', format='png', activity_key='concept:name', timestamp_key='time:timestamp', case_id_key='case:concept:name')[source]#
Shows the distribution of the events in the specified dimension
Observing the distribution of events over time permits to infer useful information about the work shifts, the working days, and the period of the year that are more or less busy.
- Parameters:
log – Event log
distr_type (
str) – Type of distribution (default: days_week): - days_month => Gets the distribution of the events among the days of a month (from 1 to 31) - months => Gets the distribution of the events among the months (from 1 to 12) - years => Gets the distribution of the events among the years of the event log - hours => Gets the distribution of the events among the hours of a day (from 0 to 23) - days_week => Gets the distribution of the events among the days of a week (from Monday to Sunday) - weeks => Gets the distribution of the events among the weeks of a year (from 0 to 52)format (
str) – Format of the visualization (default: png)activity_key (
str) – attribute to be used as activitycase_id_key (
str) – attribute to be used as case identifiertimestamp_key (
str) – attribute to be used as timestamp
import pm4py pm4py.view_events_distribution_graph(dataframe, format='svg', distr_type='days_week', activity_key='concept:name', case_id_key='case:concept:name', timestamp_key='time:timestamp')