pm4py.algo.filtering.pandas.timestamp_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.timestamp_case_grouping.timestamp_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.timestamp_case_grouping.timestamp_case_grouping_filter.Parameters(value)[source]#

Bases: Enum

An enumeration.

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

Groups the events of the same case happening at the same timestamp, providing option to keep the first event of each group, keep the last event of each group, create an event having as activity the concatenation of the activities happening in the 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.TIMESTAMP_KEY => the attribute to be used as timestamp - 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 concat => creates an event having as activity the concatenation of the activities happening in the group

Returns#

filtered_dataframe

Filtered dataframe object