pm4py.statistics.passed_time.pandas.variants package#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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.statistics.passed_time.pandas.variants.post module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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
- class pm4py.statistics.passed_time.pandas.variants.post.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- ATTRIBUTE_KEY = 'pm4py:param:attribute_key'#
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- MAX_NO_POINTS_SAMPLE = 'max_no_of_points_to_sample'#
- KEEP_ONCE_PER_CASE = 'keep_once_per_case'#
- BUSINESS_HOURS = 'business_hours'#
- BUSINESS_HOUR_SLOTS = 'business_hour_slots'#
- WORKCALENDAR = 'workcalendar'#
- pm4py.statistics.passed_time.pandas.variants.post.apply(df: DataFrame, activity: str, parameters: Dict[Any, Any] | None = None) Dict[str, Any][source]#
Gets the time passed to each succeeding activity
Parameters#
- df
Dataframe
- activity
Activity that we are considering
- parameters
Possible parameters of the algorithm
Returns#
- dictio
Dictionary containing a ‘post’ key with the list of aggregates times from the given activity to each succeeding activity
pm4py.statistics.passed_time.pandas.variants.pre module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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
- class pm4py.statistics.passed_time.pandas.variants.pre.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- ATTRIBUTE_KEY = 'pm4py:param:attribute_key'#
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- MAX_NO_POINTS_SAMPLE = 'max_no_of_points_to_sample'#
- KEEP_ONCE_PER_CASE = 'keep_once_per_case'#
- BUSINESS_HOURS = 'business_hours'#
- BUSINESS_HOUR_SLOTS = 'business_hour_slots'#
- WORKCALENDAR = 'workcalendar'#
- pm4py.statistics.passed_time.pandas.variants.pre.apply(df: DataFrame, activity: str, parameters: Dict[Any, Any] | None = None) Dict[str, Any][source]#
Gets the time passed from each preceding activity
Parameters#
- df
Dataframe
- activity
Activity that we are considering
- parameters
Possible parameters of the algorithm
Returns#
- dictio
Dictionary containing a ‘pre’ key with the list of aggregates times from each preceding activity to the given activity
pm4py.statistics.passed_time.pandas.variants.prepost module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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
- class pm4py.statistics.passed_time.pandas.variants.prepost.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- ATTRIBUTE_KEY = 'pm4py:param:attribute_key'#
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- MAX_NO_POINTS_SAMPLE = 'max_no_of_points_to_sample'#
- KEEP_ONCE_PER_CASE = 'keep_once_per_case'#
- BUSINESS_HOURS = 'business_hours'#
- BUSINESS_HOUR_SLOTS = 'business_hour_slots'#
- WORKCALENDAR = 'workcalendar'#
- pm4py.statistics.passed_time.pandas.variants.prepost.apply(df: DataFrame, activity: str, parameters: Dict[Any, Any] | None = None) Dict[str, Any][source]#
Gets the time passed from each preceding activity and to each succeeding activity
Parameters#
- df
Dataframe
- activity
Activity that we are considering
- parameters
Possible parameters of the algorithm
Returns#
- dictio
Dictionary containing a ‘pre’ key with the list of aggregated times from each preceding activity to the given activity and a ‘post’ key with the list of aggregates times from the given activity to each succeeding activity