dates

server/backend/handlers/ozon_wb_yandex_com_handler.py

@@ -2,16 +2,25 @@ from pydantic import ValidationError
 from server.backend.schemas.pydantic import ExcelInfo,settings,Translit
 from server.backend.api.nomenclature import processing
 import re
-
+import datetime
+def last_day_of_month(format: str):
+    next_month = datetime.datetime.now().replace(day=28) + datetime.timedelta(days=4)
+    last_day = next_month - datetime.timedelta(days=next_month.day)
+    return last_day.strftime(format)
 def process_sheet(df, real_arti:str, real_quantity:str, real_sum_1:str):
+    #Выборка
     df = df[[real_arti, real_quantity, real_sum_1]].copy().dropna() #copy and drop all NA values
     df = df[(df != 0).all(axis=1)] #drop all 0 values
-    df = df[[real_arti, real_quantity, real_sum_1]].copy()
+    df = df[[real_arti, real_quantity, real_sum_1]]
     df.rename(columns={real_arti: 'arti', real_quantity: 'counts', real_sum_1: 'price'}, inplace=True) #переименовываем для pydantic
+
+    #Нормализация
     df['arti'] = df['arti'].replace(Translit.TRANSLIT, regex=True)
     df['arti'] = df['arti'].astype(str).str.upper().str.extract(f'({settings.PATTERN})') #arti под regex
     df['price'] = df['price'].astype(float) #переделка к норм виду и преобразование в float
     df['counts'] = df['counts'].astype(int) #Float to Int, if exists
+
+    #Группировка
     df = df.groupby('arti', as_index=False).agg({'counts': 'sum', 'price': 'sum'}) #groupping
     df = processing(df) #vlookup for ref_keys
     validated_rows, errors = [], []
@@ -25,4 +34,5 @@ def process_sheet(df, real_arti:str, real_quantity:str, real_sum_1:str):
     return validated_rows
 def evaluating(dfs):
     validated_rows_1 = process_sheet(dfs["Лист_1"], real_arti="Артикул",real_quantity=dfs["Лист_1"].columns[4], real_sum_1=dfs["Лист_1"].columns[8])   # номера столбцов
-    return validated_rows_1
+    date = last_day_of_month(settings.TIMEFORMAT)
+    return validated_rows_1, date