pm4py.filtering.filter_time_range#

pm4py.filtering.filter_time_range(log: EventLog | DataFrame, dt1: str, dt2: str, mode: str = 'events', timestamp_key: str = 'time:timestamp', case_id_key: str = 'case:concept:name') → EventLog | DataFrame[source]#

Filters a log based on a time interval.

Parameters:

log – Event log or Pandas DataFrame.
dt1 (str) – Left extreme of the interval.
dt2 (str) – Right extreme of the interval.
mode (str) – Modality of filtering (‘events’, ‘traces_contained’, ‘traces_intersecting’). - ‘events’: Any event that fits the time frame is retained. - ‘traces_contained’: Any trace completely contained in the timeframe is retained. - ‘traces_intersecting’: Any trace intersecting with the timeframe is retained.
timestamp_key (str) – Attribute to be used for the timestamp.
case_id_key (str) – Attribute to be used as case identifier.

Returns:

Filtered event log or Pandas DataFrame.

import pm4py

filtered_dataframe1 = pm4py.filter_time_range(
    dataframe,
    '2010-01-01 00:00:00',
    '2011-01-01 00:00:00',
    mode='traces_contained',
    case_id_key='case:concept:name',
    timestamp_key='time:timestamp'
)
filtered_dataframe2 = pm4py.filter_time_range(
    dataframe,
    '2010-01-01 00:00:00',
    '2011-01-01 00:00:00',
    mode='traces_intersecting',
    case_id_key='case:concept:name',
    timestamp_key='time:timestamp'
)
filtered_dataframe3 = pm4py.filter_time_range(
    dataframe,
    '2010-01-01 00:00:00',
    '2011-01-01 00:00:00',
    mode='events',
    case_id_key='case:concept:name',
    timestamp_key='time:timestamp'
)