pm4py.statistics.attributes.log.get module#

PM4Py – A Process Mining Library for Python Copyright (C) 2026 Process Intelligence Solutions GmbH

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

class pm4py.statistics.attributes.log.get.Parameters(*values)[source]#

Bases: Enum

ATTRIBUTE_KEY = 'pm4py:param:attribute_key'#
ACTIVITY_KEY = 'pm4py:param:activity_key'#
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
MAX_NO_POINTS_SAMPLE = 'max_no_of_points_to_sample'#
KEEP_ONCE_PER_CASE = 'keep_once_per_case'#
pm4py.statistics.attributes.log.get.get_events_distribution(log: EventLog, distr_type: str = 'days_month', parameters: Dict[str | Parameters, Any] | None = None) → Tuple[List[str], List[int]][source]#

Gets the distribution of the events in the specified dimension

Parameters:

  • log – Event log

  • distr_type – Type of distribution: - days_month => Gets the distribution of the events among the days of a month (from 1 to 31) - months => Gets the distribution of the events among the months (from 1 to 12) - years => Gets the distribution of the events among the years of the event log - hours => Gets the distribution of the events among the hours of a day (from 0 to 23) - days_week => Gets the distribution of the events among the days of a week (from Monday to Sunday)

  • parameters – Parameters of the algorithm, including: - Parameters.TIMESTAMP_KEY

Returns:

  • x – Points (of the X-axis)

  • y – Points (of the Y-axis)

pm4py.statistics.attributes.log.get.get_all_trace_attributes_from_log(log: EventLog) → Set[str][source]#

Get all trace attributes from the log

Parameters:

log – Log

Returns:

all_attributes – All trace attributes from the log

Return type:

set

pm4py.statistics.attributes.log.get.get_all_event_attributes_from_log(log: EventLog) → Set[str][source]#

Get all events attributes from the log

Parameters:

log – Log

Returns:

all_attributes – All trace attributes from the log

Return type:

set

pm4py.statistics.attributes.log.get.get_attribute_values(log: EventLog, attribute_key: str, parameters: Dict[str | Parameters, Any] | None = None) → Dict[Any, int][source]#

Get the attribute values of the log for the specified attribute along with their count

Parameters:

  • log – Log

  • attribute_key – Attribute for which we would like to know the values along with their count

  • parameters – Possible parameters of the algorithm

Returns:

attributes – Dictionary of attributes associated with their count

Return type:

dict

pm4py.statistics.attributes.log.get.get_trace_attribute_values(log: EventLog, attribute_key: str, parameters: Dict[str | Parameters, Any] | None = None) → Dict[Any, int][source]#

Get the attribute values of the log for the specified attribute along with their count

Parameters:

  • log – Log

  • attribute_key – Attribute for which we wish to get the values along with their count

  • parameters – Possible parameters of the algorithm

Returns:

attributes – Dictionary of attributes associated with their count

Return type:

dict

pm4py.statistics.attributes.log.get.get_kde_numeric_attribute(log, attribute, parameters=None)[source]#

Gets the KDE estimation for the distribution of a numeric attribute values

Parameters:

  • log – Event stream object (if log, is converted)

  • attribute – Numeric attribute to analyse

  • parameters –

    Possible parameters of the algorithm, including:

    graph_points -> number of points to include in the graph

Returns:

  • x – X-axis values to represent

  • y – Y-axis values to represent

pm4py.statistics.attributes.log.get.get_kde_numeric_attribute_json(log, attribute, parameters=None)[source]#

Gets the KDE estimation for the distribution of a numeric attribute values (expressed as JSON)

Parameters:

  • log – Event log object (if log, is converted)

  • attribute – Numeric attribute to analyse

  • parameters –

    Possible parameters of the algorithm, including:

    graph_points -> number of points to include in the graph

Returns:

  • x – X-axis values to represent

  • y – Y-axis values to represent

pm4py.statistics.attributes.log.get.get_kde_date_attribute(log, attribute='time:timestamp', parameters=None)[source]#

Gets the KDE estimation for the distribution of a date attribute values

Parameters:

  • log – Event stream object (if log, is converted)

  • attribute – Date attribute to analyse

  • parameters –

    Possible parameters of the algorithm, including:

    graph_points -> number of points to include in the graph

Returns:

  • x – X-axis values to represent

  • y – Y-axis values to represent

pm4py.statistics.attributes.log.get.get_kde_date_attribute_json(log, attribute='time:timestamp', parameters=None)[source]#

Gets the KDE estimation for the distribution of a date attribute values (expressed as JSON)

Parameters:

  • log – Event stream object (if log, is converted)

  • attribute – Date attribute to analyse

  • parameters –

    Possible parameters of the algorithm, including:

    graph_points -> number of points to include in the graph

Returns:

  • x – X-axis values to represent

  • y – Y-axis values to represent