pm4py.statistics.attributes.pandas package#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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
Submodules#
pm4py.statistics.attributes.pandas.get module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
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.pandas.get.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[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.pandas.get.get_events_distribution(df: DataFrame, 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#
- df
Dataframe
- 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) - weeks => Distribution of the events among the weeks of a year (from 0 to 52)
- 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.pandas.get.get_attribute_values(df: DataFrame, attribute_key: str, parameters: Dict[str | Parameters, Any] | None = None) Dict[Any, int][source]#
Return list of attribute values contained in the specified column of the CSV
Parameters#
- df
Pandas dataframe
- attribute_key
Attribute for which we want to known the values and the count
- parameters
Possible parameters of the algorithm
Returns#
- attributes_values_dict
Attributes in the specified column, along with their count
- pm4py.statistics.attributes.pandas.get.get_kde_numeric_attribute(df: DataFrame, attribute: str, parameters: Dict[str | Parameters, Any] | None = None) Dict[Any, int][source]#
Gets the KDE estimation for the distribution of a numeric attribute values
Parameters#
- df
Pandas dataframe
- 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.pandas.get.get_kde_numeric_attribute_json(df, attribute, parameters=None)[source]#
Gets the KDE estimation for the distribution of a numeric attribute values (expressed as JSON)
Parameters#
- df
Pandas dataframe
- attribute
Numeric attribute to analyse
- parameters
- Possible parameters of the algorithm, including:
graph_points -> number of points to include in the graph
Returns#
- json
JSON representing the graph points
- pm4py.statistics.attributes.pandas.get.get_kde_date_attribute(df, attribute='time:timestamp', parameters=None)[source]#
Gets the KDE estimation for the distribution of a date attribute values
Parameters#
- df
Pandas dataframe
- 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.pandas.get.get_kde_date_attribute_json(df, attribute='time:timestamp', parameters=None)[source]#
Gets the KDE estimation for the distribution of a date attribute values (expressed as JSON)
Parameters#
- df
Pandas dataframe
- attribute
Date attribute to analyse
- parameters
- Possible parameters of the algorithm, including:
graph_points -> number of points to include in the graph
Returns#
- json
JSON representing the graph points