pm4py.algo.filtering.pandas.timestamp 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.algo.filtering.pandas.timestamp.timestamp_filter 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.algo.filtering.pandas.timestamp.timestamp_filter.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- pm4py.algo.filtering.pandas.timestamp.timestamp_filter.filter_traces_contained(df: DataFrame, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) DataFrame[source]#
Get traces that are contained in the given interval
Parameters#
- df
Pandas dataframe
- dt1
Lower bound to the interval (possibly expressed as string, but automatically converted)
- dt2
Upper bound to the interval (possibly expressed as string, but automatically converted)
- parameters
- Possible parameters of the algorithm, including:
Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp Parameters.CASE_ID_KEY -> Column that contains the timestamp
Returns#
- df
Filtered dataframe
- pm4py.algo.filtering.pandas.timestamp.timestamp_filter.filter_traces_intersecting(df: DataFrame, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) DataFrame[source]#
Filter traces intersecting the given interval
Parameters#
- df
Pandas dataframe
- dt1
Lower bound to the interval (possibly expressed as string, but automatically converted)
- dt2
Upper bound to the interval (possibly expressed as string, but automatically converted)
- parameters
- Possible parameters of the algorithm, including:
Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp Parameters.CASE_ID_KEY -> Column that contains the timestamp
Returns#
- df
Filtered dataframe
- pm4py.algo.filtering.pandas.timestamp.timestamp_filter.apply_events(df: DataFrame, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) DataFrame[source]#
Get a new log containing all the events contained in the given interval
Parameters#
- df
Pandas dataframe
- dt1
Lower bound to the interval (possibly expressed as string, but automatically converted)
- dt2
Upper bound to the interval (possibly expressed as string, but automatically converted)
- parameters
- Possible parameters of the algorithm, including:
Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp
Returns#
- df
Filtered dataframe
- pm4py.algo.filtering.pandas.timestamp.timestamp_filter.filter_traces_attribute_in_timeframe(df: DataFrame, attribute: str, attribute_value: str, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) DataFrame[source]#
Get a new log containing all the traces that have an event in the given interval with the specified attribute value
Parameters#
- df
Dataframe
- attribute
The attribute to filter on
- attribute_value
The attribute value to filter on
- dt1
Lower bound to the interval
- dt2
Upper bound to the interval
- parameters
- Possible parameters of the algorithm, including:
Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp
Returns#
- df
Filtered dataframe