pm4py.algo.filtering.pandas.timestamp.timestamp_filter module#

class pm4py.algo.filtering.pandas.timestamp.timestamp_filter.Parameters(*values)[source]#

Bases: Enum

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
pm4py.algo.filtering.pandas.timestamp.timestamp_filter.filter_traces_contained(df: DataFrame, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Get traces that are contained in the given interval

Parameters:

  • df – Pandas dataframe

  • dt1 – Lower bound to the interval (possibly expressed as string, but automatically converted)

  • dt2 – Upper bound to the interval (possibly expressed as string, but automatically converted)

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp Parameters.CASE_ID_KEY -> Column that contains the timestamp

Returns:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.timestamp.timestamp_filter.filter_traces_intersecting(df: DataFrame, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Filter traces intersecting the given interval

Parameters:

  • df – Pandas dataframe

  • dt1 – Lower bound to the interval (possibly expressed as string, but automatically converted)

  • dt2 – Upper bound to the interval (possibly expressed as string, but automatically converted)

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp Parameters.CASE_ID_KEY -> Column that contains the timestamp

Returns:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.timestamp.timestamp_filter.apply_events(df: DataFrame, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Get a new log containing all the events contained in the given interval

Parameters:

  • df – Pandas dataframe

  • dt1 – Lower bound to the interval (possibly expressed as string, but automatically converted)

  • dt2 – Upper bound to the interval (possibly expressed as string, but automatically converted)

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp

Returns:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.timestamp.timestamp_filter.filter_traces_attribute_in_timeframe(df: DataFrame, attribute: str, attribute_value: str, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) → DataFrame[source]#

Get a new log containing all the traces that have an event in the given interval with the specified attribute value

Parameters:

  • df – Dataframe

  • attribute – The attribute to filter on

  • attribute_value – The attribute value to filter on

  • dt1 – Lower bound to the interval

  • dt2 – Upper bound to the interval

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp

Returns:

Filtered dataframe

Return type:

df

pm4py.algo.filtering.pandas.timestamp.timestamp_filter.apply(df, parameters=None)[source]#
pm4py.algo.filtering.pandas.timestamp.timestamp_filter.apply_auto_filter(df, parameters=None)[source]#