pm4py.algo.filtering.pandas.end_activities 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.end_activities.end_activities_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.end_activities.end_activities_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'#

GROUP_DATAFRAME = 'grouped_dataframe'#

POSITIVE = 'positive'#

RETURN_EA_COUNT = 'return_ea_count_dict_autofilter'#

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

Filter dataframe on end activities

Parameters#

df

Dataframe

values

Values to filter on

parameters

Possible parameters of the algorithm, including:: Parameters.CASE_ID_KEY -> Case ID column in the dataframe Parameters.ACTIVITY_KEY -> Column that represents the activity Parameters.POSITIVE -> Specifies if the filtered should be applied including traces (positive=True) or excluding traces (positive=False)

Returns#

df: Filtered dataframe

pm4py.algo.filtering.pandas.end_activities.end_activities_filter.filter_df_on_end_activities(df, values, case_id_glue='case:concept:name', activity_key='concept:name', grouped_df=None, positive=True)[source]#

Filter dataframe on end activities

Parameters#

df: Dataframe
values: Values to filter on
case_id_glue: Case ID column in the dataframe
activity_key: Column that represent the activity
positive: Specifies if the filtered should be applied including traces (positive=True) or excluding traces (positive=False)

Returns#

df: Filtered dataframe

pm4py.algo.filtering.pandas.end_activities.end_activities_filter.filter_df_on_end_activities_nocc(df, nocc, ea_count0=None, case_id_glue='case:concept:name', grouped_df=None, activity_key='concept:name', return_dict=False, most_common_variant=None)[source]#

Filter dataframe on end activities number of occurrences

Parameters#

df: Dataframe
nocc: Minimum number of occurrences of the end activity
ea_count0: (if provided) Dictionary that associates each end activity with its count
case_id_glue: Column that contains the Case ID
activity_key: Column that contains the activity
grouped_df: Grouped dataframe
return_dict: Return dict