pm4py.algo.discovery.dfg.adapters.pandas package¶

Submodules¶

pm4py.algo.discovery.dfg.adapters.pandas.df_statistics module¶

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

pm4py.algo.discovery.dfg.adapters.pandas.df_statistics.get_concurrent_events_dataframe(df, start_timestamp_key=None, timestamp_key='time:timestamp', case_id_glue='case:concept:name', activity_key='concept:name', sort_caseid_required=True, sort_timestamp_along_case_id=True, reduce_dataframe=True, max_start_column='@@max_start_column', min_complete_column='@@min_complete_column', diff_maxs_minc='@@diff_maxs_minc', strict=False)[source]¶

Gets the concurrent events (of the same case) in a Pandas dataframe

Parameters:	df – Dataframe start_timestamp_key – Start timestamp key (if not provided, defaulted to the timestamp_key) timestamp_key – Complete timestamp case_id_glue – Column of the dataframe to use as case ID activity_key – Activity key sort_caseid_required – Tells if a sort by case ID is required (default: True) sort_timestamp_along_case_id – Tells if a sort by timestamp is required along the case ID (default: True) reduce_dataframe – To fasten operation, keep only essential columns in the dataframe strict – Gets only entries that are strictly concurrent (i.e. the length of the intersection as real interval is > 0)
Returns:	Concurrent events dataframe (with @@diff_maxs_minc as the size of the intersection of the intervals)
Return type:	conc_ev_dataframe

pm4py.algo.discovery.dfg.adapters.pandas.df_statistics.get_dfg_graph(df, measure='frequency', activity_key='concept:name', case_id_glue='case:concept:name', start_timestamp_key=None, timestamp_key='time:timestamp', perf_aggregation_key='mean', sort_caseid_required=True, sort_timestamp_along_case_id=True, keep_once_per_case=False, window=1, business_hours=False, worktiming=None, weekends=None, workcalendar=None, target_activity_key=None, reduce_columns=True)[source]¶

Get DFG graph from Pandas dataframe

Parameters:	df – Dataframe measure – Measure to use (frequency/performance/both) activity_key – Activity key to use in the grouping case_id_glue – Case ID identifier start_timestamp_key – Start timestamp key timestamp_key – Timestamp key perf_aggregation_key – Performance aggregation key (mean, median, min, max) sort_caseid_required – Specify if a sort on the Case ID is required sort_timestamp_along_case_id – Specifying if sorting by timestamp along the CaseID is required keep_once_per_case – In the counts, keep only one occurrence of the path per case (the first) window – Window of the DFG (default 1)
Returns:	DFG in the chosen measure (may be only the frequency, only the performance, or both)
Return type:	dfg

pm4py.algo.discovery.dfg.adapters.pandas.df_statistics.get_partial_order_dataframe(df, start_timestamp_key=None, timestamp_key='time:timestamp', case_id_glue='case:concept:name', activity_key='concept:name', sort_caseid_required=True, sort_timestamp_along_case_id=True, reduce_dataframe=True, keep_first_following=True, business_hours=False, worktiming=None, weekends=None, workcalendar=None, event_index='@@index')[source]¶

Gets the partial order between events (of the same case) in a Pandas dataframe

Parameters:	df – Dataframe start_timestamp_key – Start timestamp key (if not provided, defaulted to the timestamp_key) timestamp_key – Complete timestamp case_id_glue – Column of the dataframe to use as case ID activity_key – Activity key sort_caseid_required – Tells if a sort by case ID is required (default: True) sort_timestamp_along_case_id – Tells if a sort by timestamp is required along the case ID (default: True) reduce_dataframe – To fasten operation, keep only essential columns in the dataframe keep_first_following – Keep only the first event following the given event
Returns:	Partial order dataframe (with @@flow_time between events)
Return type:	part_ord_dataframe

pm4py.algo.discovery.dfg.adapters.pandas.freq_triples module¶

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

pm4py.algo.discovery.dfg.adapters.pandas.freq_triples.get_freq_triples(df, activity_key='concept:name', case_id_glue='case:concept:name', timestamp_key='time:timestamp', sort_caseid_required=True, sort_timestamp_along_case_id=True)[source]¶

Gets the frequency triples out of a dataframe

Parameters:	df – Dataframe activity_key – Activity key case_id_glue – Case ID glue timestamp_key – Timestamp key sort_caseid_required – Determine if sort by case ID is required (default: True) sort_timestamp_along_case_id – Determine if sort by timestamp is required (default: True)
Returns:	Frequency triples from the dataframe
Return type:	freq_triples

Module contents¶

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.