pm4py.algo.filtering.pandas.timestamp package

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

Bases: enum.Enum

An enumeration.

CASE_ID_KEY = 'pm4py:param:case_id_key'

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'

pm4py.algo.filtering.pandas.timestamp.timestamp_filter.apply(df, parameters=None)

pm4py.algo.filtering.pandas.timestamp.timestamp_filter.apply_auto_filter(df, parameters=None)

pm4py.algo.filtering.pandas.timestamp.timestamp_filter.apply_events(df: pandas.core.frame.DataFrame, dt1: Union[str, datetime.datetime], dt2: Union[str, datetime.datetime], parameters: Optional[Dict[Union[str, pm4py.algo.filtering.pandas.timestamp.timestamp_filter.Parameters], Any]] = None) → pandas.core.frame.DataFrame

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:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.timestamp.timestamp_filter.filter_traces_attribute_in_timeframe(df: pandas.core.frame.DataFrame, attribute: str, attribute_value: str, dt1: Union[str, datetime.datetime], dt2: Union[str, datetime.datetime], parameters: Optional[Dict[Union[str, pm4py.algo.filtering.pandas.timestamp.timestamp_filter.Parameters], Any]] = None) → pandas.core.frame.DataFrame

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:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.timestamp.timestamp_filter.filter_traces_contained(df: pandas.core.frame.DataFrame, dt1: Union[str, datetime.datetime], dt2: Union[str, datetime.datetime], parameters: Optional[Dict[Union[str, pm4py.algo.filtering.pandas.timestamp.timestamp_filter.Parameters], Any]] = None) → pandas.core.frame.DataFrame

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:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.timestamp.timestamp_filter.filter_traces_intersecting(df: pandas.core.frame.DataFrame, dt1: Union[str, datetime.datetime], dt2: Union[str, datetime.datetime], parameters: Optional[Dict[Union[str, pm4py.algo.filtering.pandas.timestamp.timestamp_filter.Parameters], Any]] = None) → pandas.core.frame.DataFrame

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:

Filtered dataframe

Return type:

df

Module contents

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/>.