pm4py.algo.filtering.pandas.start_activities.start_activities_filter module#

class pm4py.algo.filtering.pandas.start_activities.start_activities_filter.Parameters(*values)[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'#
pm4py.algo.filtering.pandas.start_activities.start_activities_filter.apply(df: DataFrame, values: List[str], parameters: Dict[str | Parameters, Any] | None = 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:

Filtered dataframe

Return type:

df

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:

Filtered dataframe

Return type:

df

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:

Filtered dataframe

Return type:

df