pm4py.algo.filtering.pandas.timestamp 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.timestamp_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.timestamp_filter.Parameters(value)[source]#

Bases: Enum

An enumeration.

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: Union[str, datetime], dt2: Union[str, datetime], parameters: Optional[Dict[Union[str, Parameters], Any]] = 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: Union[str, datetime], dt2: Union[str, datetime], parameters: Optional[Dict[Union[str, Parameters], Any]] = 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: Union[str, datetime], dt2: Union[str, datetime], parameters: Optional[Dict[Union[str, Parameters], Any]] = 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: Union[str, datetime], dt2: Union[str, datetime], parameters: Optional[Dict[Union[str, Parameters], Any]] = 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

pm4py.algo.filtering.pandas.timestamp.timestamp_filter.apply(df, parameters=None)[source]#
pm4py.algo.filtering.pandas.timestamp.timestamp_filter.apply_auto_filter(df, parameters=None)[source]#