pm4py.algo.filtering.pandas.end_activities package

Submodules

pm4py.algo.filtering.pandas.end_activities.end_activities_filter module

class pm4py.algo.filtering.pandas.end_activities.end_activities_filter.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
CASE_ID_KEY = 'case_id_glue'
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, values, parameters=None)[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

Filtered dataframe

Return type

df

pm4py.algo.filtering.pandas.end_activities.end_activities_filter.apply_auto_filter(df, parameters=None)[source]

Apply auto filter on end activities

Parameters
  • df – Pandas dataframe

  • parameters

    Parameters of the algorithm, including:

    Parameters.CASE_ID_KEY -> Case ID column in the dataframe Parameters.ACTIVITY_KEY -> Column that represents the activity Parameters.DECREASING_FACTOR -> Decreasing factor that should be passed to the algorithm

Returns

Filtered dataframe

Return type

df

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

Filtered dataframe

Return type

df

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

Module contents