Update match-and-exclude-records-actions_v1.py

conf/scripts/match-and-exclude-records-actions_v1.py

@@ -98,12 +98,13 @@ class MatchAndExcludeRecordsAction(ActionInterface):
                 self.app.logger.info(f"Insumos cargados - {self.identifier}")
 
                 if self.create_tmp_table:
-                    import random
+                    from app.main.engine.util.Timezone import Timezone
                     from sqlalchemy import text
                     # Creamos las tablas temporales del pivot y contraparte con sufijo N° aleatorio de 6 dígitos
-                    random_number = random.randint(100000, 999999)
-                    pivot_query = f"CREATE TABLE ENGINE_{pivot_table}_{random_number} AS SELECT * FROM {pivot_table}"
-                    ctp_query = f"CREATE TABLE ENGINE_{ctp_table}_{random_number} AS SELECT * FROM {ctp_table}"
+                    timezone = Timezone(self.app)
+                    current_time = timezone.datetime_by_tzone().strftime("%Y%m%d%H%M")
+                    pivot_query = f"CREATE TABLE ENGINE_{pivot_table}_{current_time} AS SELECT * FROM {pivot_table}"
+                    ctp_query = f"CREATE TABLE ENGINE_{ctp_table}_{current_time} AS SELECT * FROM {ctp_table}"
                     source_obj.create_engine()
                     engine = source_obj.engine
                     with engine.connect() as conn:
@@ -237,7 +238,8 @@ class MatchAndExcludeRecordsAction(ActionInterface):
                 total_cols = group_cols + [amount_col, id_col,  "DIFF"]
                 self.app.logger.info(f"Aplicando algoritmo de exclusión. Máximo de {str(max_combinations)} combinaciones - {self.identifier}")
                 resultado = df3.groupby(group_cols)[total_cols].apply(lambda x: custom_func(x, amount_col, id_col, max_combinations))
-
+                if resultado.empty:
+                    return pd.DataFrame([])
                 resultado = resultado.reset_index()
                 if len(resultado.columns) == 1:
                     resultado = pd.DataFrame([], columns=group_cols + ["LISTA_DIFF"])
@@ -256,14 +258,16 @@ class MatchAndExcludeRecordsAction(ActionInterface):
                 meged2 = meged2.compute()
 
                 if meged2.empty:
-                    pass
+                    self.app.logger.info(f"El dataset resultante esta vacío luego de la ejecución del script")
                 elif self.exclude_pivot:
                     meged2['INTER_PIVOT_ID'] = meged2.apply(lambda row: self.array_except(row[self.pivot_params["id-column"]], row['LISTA_DIFF']), axis=1)
+                    meged2 = meged2[meged2["INTER_PIVOT_ID"].apply(lambda x: True if isinstance(x, List) and len(x) > 0 else False)]
                     meged2 = meged2.rename(columns={self.ctp_params["id-column"]: "INTER_CTP_ID"})
                     if meged2['INTER_CTP_ID'].dtype == 'int64':
                         meged2['INTER_CTP_ID'] = meged2['INTER_CTP_ID'].apply(lambda x: [x]).astype('object')
                 else:
                     meged2['INTER_CTP_ID'] = meged2.apply(lambda row: self.array_except(row[self.ctp_params["id-column"]], row['LISTA_DIFF']), axis=1)
+                    meged2 = meged2[meged2['INTER_CTP_ID'].apply(lambda x: True if isinstance(x, List) and len(x) > 0 else False)]
                     meged2 = meged2.rename(columns={self.pivot_params["id-column"]: "INTER_PIVOT_ID"})
                     if meged2['INTER_PIVOT_ID'].dtype == 'int64':
                         meged2['INTER_PIVOT_ID'] = meged2['INTER_PIVOT_ID'].apply(lambda x: [x]).astype('object')