pm4py.algo.filtering.pandas.consecutive_act_case_grouping package#

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

Submodules#

pm4py.algo.filtering.pandas.consecutive_act_case_grouping.consecutive_act_case_grouping_filter module#

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

class pm4py.algo.filtering.pandas.consecutive_act_case_grouping.consecutive_act_case_grouping_filter.Parameters(value)[source]#

Bases: Enum

An enumeration.

CASE_ID_KEY = 'pm4py:param:case_id_key'#
ACTIVITY_KEY = 'pm4py:param:activity_key'#
FILTER_TYPE = 'filter_type'#
pm4py.algo.filtering.pandas.consecutive_act_case_grouping.consecutive_act_case_grouping_filter.apply(log_obj: Union[EventLog, EventStream, DataFrame], parameters: Optional[Dict[Any, Any]] = None) DataFrame[source]#

Groups the consecutive events of the same case having the same activity, providing option to keep the first/last event of each group

Parameters#

log_obj

Log object (EventLog, EventStream, Pandas dataframe)

parameters

Parameters of the algorithm, including: - Parameters.CASE_ID_KEY => the case identifier to be used - Parameters.ACTIVITY_KEY => the attribute to be used as activity - Parameters.FILTER_TYPE => the type of filter to be applied:

first => keeps the first event of each group last => keeps the last event of each group

Returns#

filtered_dataframe

Filtered dataframe object