pm4py.algo.filtering.pandas.start_activities 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.start_activities.start_activities_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.start_activities.start_activities_filter.Parameters(value)[source]#

Bases: Enum

An enumeration.

CASE_ID_KEY = 'pm4py:param:case_id_key'#
ACTIVITY_KEY = 'pm4py:param:activity_key'#
DECREASING_FACTOR = 'decreasingFactor'#
GROUP_DATAFRAME = 'grouped_dataframe'#
POSITIVE = 'positive'#
pm4py.algo.filtering.pandas.start_activities.start_activities_filter.apply(df: DataFrame, values: List[str], parameters: Optional[Dict[Union[str, Parameters], Any]] = None) DataFrame[source]#

Filter dataframe on start 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.start_activities.start_activities_filter.filter_df_on_start_activities(df, values, case_id_glue='case:concept:name', activity_key='concept:name', grouped_df=None, positive=True)[source]#

Filter dataframe on start 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

grouped_df

Grouped dataframe

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.start_activities.start_activities_filter.filter_df_on_start_activities_nocc(df, nocc, sa_count0=None, case_id_glue='case:concept:name', activity_key='concept:name', grouped_df=None)[source]#

Filter dataframe on start activities number of occurrences

Parameters#

df

Dataframe

nocc

Minimum number of occurrences of the start activity

sa_count0

(if provided) Dictionary that associates each start activity with its count

case_id_glue

Column that contains the Case ID

activity_key

Column that contains the activity

grouped_df

Grouped dataframe

Returns#

df

Filtered dataframe