pm4py.statistics.traces.generic.pandas.case_statistics module#
- class pm4py.statistics.traces.generic.pandas.case_statistics.Parameters(*values)[source]#
Bases:
Enum- ATTRIBUTE_KEY = 'pm4py:param:attribute_key'#
- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
- MAX_VARIANTS_TO_RETURN = 'max_variants_to_return'#
- VARIANTS_DF = 'variants_df'#
- ENABLE_SORT = 'enable_sort'#
- SORT_BY_COLUMN = 'sort_by_column'#
- SORT_ASCENDING = 'sort_ascending'#
- MAX_RET_CASES = 'max_ret_cases'#
- BUSINESS_HOURS = 'business_hours'#
- BUSINESS_HOUR_SLOTS = 'business_hour_slots'#
- WORKCALENDAR = 'workcalendar'#
- pm4py.statistics.traces.generic.pandas.case_statistics.get_variant_statistics(df: DataFrame, parameters: Dict[str | Parameters, Any] | None = None) List[Dict[str, int]] | List[Dict[List[str], int]][source]#
Get variants from a Pandas dataframe
- Parameters:
df – Dataframe
parameters –
- Parameters of the algorithm, including:
Parameters.CASE_ID_KEY -> Column that contains the Case ID Parameters.ACTIVITY_KEY -> Column that contains the activity Parameters.MAX_VARIANTS_TO_RETURN -> Maximum number of variants to return variants_df -> If provided, avoid recalculation of the variants dataframe
- Returns:
List of variants inside the Pandas dataframe
- Return type:
variants_list
- pm4py.statistics.traces.generic.pandas.case_statistics.get_variants_df_and_list(df: DataFrame, parameters: Dict[str | Parameters, Any] | None = None) Tuple[DataFrame, List[Dict[str, int]] | List[Dict[List[str], int]]][source]#
(Technical method) Provides variants_df and variants_list out of the box
- Parameters:
df – Dataframe
parameters –
- Parameters of the algorithm, including:
Parameters.CASE_ID_KEY -> Column that contains the Case ID Parameters.ACTIVITY_KEY -> Column that contains the activity
- Returns:
variants_df – Variants dataframe
variants_list – List of variants sorted by their count
- pm4py.statistics.traces.generic.pandas.case_statistics.get_cases_description(df: DataFrame, parameters: Dict[str | Parameters, Any] | None = None) Dict[str, Dict[str, Any]][source]#
Get a description of traces present in the Pandas dataframe
- Parameters:
df – Pandas dataframe
parameters –
- Parameters of the algorithm, including:
Parameters.CASE_ID_KEY -> Column that identifies the case ID Parameters.TIMESTAMP_KEY -> Column that identifies the timestamp enable_sort -> Enable sorting of traces Parameters.SORT_BY_COLUMN -> Sort traces inside the dataframe using the specified column. Admitted values: startTime, endTime, caseDuration Parameters.SORT_ASCENDING -> Set sort direction (boolean; it true then the sort direction is ascending, otherwise descending) Parameters.MAX_RET_CASES -> Set the maximum number of returned traces
- Returns:
Dictionary of traces associated to their start timestamp, their end timestamp and their duration
- Return type:
ret
- pm4py.statistics.traces.generic.pandas.case_statistics.get_variants_df(df, parameters=None)[source]#
Get variants dataframe from a Pandas dataframe
- Parameters:
df – Dataframe
parameters –
- Parameters of the algorithm, including:
Parameters.CASE_ID_KEY -> Column that contains the Case ID Parameters.ACTIVITY_KEY -> Column that contains the activity
- Returns:
Variants dataframe
- Return type:
variants_df
- pm4py.statistics.traces.generic.pandas.case_statistics.get_variants_df_with_case_duration(df, parameters=None)[source]#
Get variants dataframe from a Pandas dataframe, with case duration that is included
- Parameters:
df – Dataframe
parameters –
- Parameters of the algorithm, including:
Parameters.CASE_ID_KEY -> Column that contains the Case ID Parameters.ACTIVITY_KEY -> Column that contains the activity Parameters.TIMESTAMP_KEY -> Column that contains the timestamp
- Returns:
Variants dataframe
- Return type:
variants_df
- pm4py.statistics.traces.generic.pandas.case_statistics.get_events(df: DataFrame, case_id: str, parameters: Dict[str | Parameters, Any] | None = None) List[Dict[str, Any]][source]#
Get events belonging to the specified case
- Parameters:
df – Pandas dataframe
case_id – Required case ID
parameters –
- Possible parameters of the algorithm, including:
Parameters.CASE_ID_KEY -> Column in which the case ID is contained
- Returns:
List of events belonging to the case
- Return type:
list_eve
- pm4py.statistics.traces.generic.pandas.case_statistics.get_kde_caseduration(df, parameters=None)[source]#
Gets the estimation of KDE density for the case durations calculated on the dataframe
- Parameters:
df – Pandas dataframe
parameters –
- Possible parameters of the algorithm, including:
Parameters.GRAPH_POINTS -> number of points to include in the graph Parameters.CASE_ID_KEY -> Column hosting the Case ID
- Returns:
x – X-axis values to represent
y – Y-axis values to represent
- pm4py.statistics.traces.generic.pandas.case_statistics.get_kde_caseduration_json(df, parameters=None)[source]#
Gets the estimation of KDE density for the case durations calculated on the log/dataframe (expressed as JSON)
- Parameters:
df – Pandas dataframe
parameters –
- Possible parameters of the algorithm, including:
Parameters.GRAPH_POINTS -> number of points to include in the graph Parameters.CASE_ID_KEY -> Column hosting the Case ID
- Returns:
JSON representing the graph points
- Return type:
json
- pm4py.statistics.traces.generic.pandas.case_statistics.get_all_case_durations(df, parameters=None)[source]#
Gets all the case durations out of the log
- Parameters:
df – Pandas dataframe
parameters – Possible parameters of the algorithm
- Returns:
List of all duration values
- Return type:
duration_values
