pm4py.algo.filtering.pandas.consecutive_act_case_grouping package#

PM4Py – A Process Mining Library for Python

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program 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 Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

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

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

class pm4py.algo.filtering.pandas.consecutive_act_case_grouping.consecutive_act_case_grouping_filter.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

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: EventLog | EventStream | DataFrame, parameters: Dict[Any, Any] | None = 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