pm4py.statistics.overlap.interval_events.pandas 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.overlap.interval_events.pandas.get 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.overlap.interval_events.pandas.get.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum
- START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- pm4py.statistics.overlap.interval_events.pandas.get.apply(df: DataFrame, parameters: Dict[str | Parameters, Any] | None = None) List[int] [source]#
Counts the intersections of each interval event with the other interval events of the log (all the events are considered, not looking at the activity)
Parameters#
- df
Pandas dataframe
- parameters
Parameters of the algorithm, including: - Parameters.START_TIMESTAMP_KEY => the attribute to consider as start timestamp - Parameters.TIMESTAMP_KEY => the attribute to consider as timestamp
Returns#
- overlap
For each interval event, ordered by the order of appearance in the log, associates the number of intersecting events.