pm4py.algo.filtering.pandas.traces package#

PM4Py – A Process Mining Library for Python

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.traces.trace_filter module#

PM4Py – A Process Mining Library for Python

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.traces.trace_filter.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

CASE_ID_KEY = 'pm4py:param:case_id_key'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

DECREASING_FACTOR = 'decreasingFactor'#

POSITIVE = 'positive'#

pm4py.algo.filtering.pandas.traces.trace_filter.apply(df: DataFrame, admitted_traces: List[List[str]], parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Apply a filter on variants

Parameters#

df

Dataframe

admitted_traces

List of admitted traces (to include/exclude)

parameters

Parameters of the algorithm, including:: Parameters.CASE_ID_KEY -> Column that contains the Case ID Parameters.ACTIVITY_KEY -> Column that contains the activity Parameters.POSITIVE -> Specifies if the filter should be applied including traces (positive=True) or excluding traces (positive=False) variants_df -> If provided, avoid recalculation of the variants dataframe

Returns#

df: Filtered dataframe