fix arti resolve

server/backend/handlers/digitalization.py

@@ -12,7 +12,14 @@ def process_sheet(df,real_arti: int):
     df.columns = ['arti']
     # нормализация
     df['arti'] = df['arti'].replace(Translit.TRANSLIT, regex=True)
-    df['arti'] = df['arti'].astype(str).str.upper().str.extract(f'({settings.PATTERN})')
+    df['arti'] = df['arti'].astype(str).str.upper().str.extract(f'({settings.PATTERN})')       # Удаляем пустые строки
+    def normalize(arti):
+        if re.search(r'\d{3,}$', arti):
+            return re.sub(r'(\d)\d{2,}$', r'\1', arti)
+        else:
+            return re.sub(r'\d{1,2}$', '', arti)
+
+    df['arti'] = df['arti'].apply(normalize)
     # группировка
     df = processing(df)
     validated_rows, errors = [], []

server/backend/handlers/ozon_handler.py

@@ -23,6 +23,13 @@ def process_sheet(df,real_arti: int,real_quantity: int,real_sum_1: int,real_sum_
     # нормализация
     df['arti'] = df['arti'].replace(Translit.TRANSLIT, regex=True)
     df['arti'] = df['arti'].astype(str).str.upper().str.extract(f'({settings.PATTERN})')
+    def normalize(arti):
+        if re.search(r'\d{3,}$', arti):
+            return re.sub(r'(\d)\d{2,}$', r'\1', arti)
+        else:
+            return re.sub(r'\d{1,2}$', '', arti)
+
+    df['arti'] = df['arti'].apply(normalize)
     df['price'] = df['price'].astype(float)
     df['counts'] = df['counts'].astype(int)
 

server/backend/handlers/ozon_purchases_handler.py

@@ -24,6 +24,13 @@ def process_sheet(df, real_arti:int, real_quantity:int, real_sum_1:int):
     # нормализация
     df['arti'] = df['arti'].replace(Translit.TRANSLIT, regex=True)
     df['arti'] = df['arti'].astype(str).str.upper().str.extract(f'({settings.PATTERN})')
+    def normalize(arti):
+        if re.search(r'\d{3,}$', arti):
+            return re.sub(r'(\d)\d{2,}$', r'\1', arti)
+        else:
+            return re.sub(r'\d{1,2}$', '', arti)
+
+    df['arti'] = df['arti'].apply(normalize)
     df['price'] = df['price'].astype(float)
     df['counts'] = df['counts'].astype(int)
 

server/backend/handlers/ozon_wb_yandex_com_handler.py

@@ -17,6 +17,13 @@ def process_sheet(df, real_arti:str, real_quantity:str, real_sum_1:str):
     #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
+    def normalize(arti):
+        if re.search(r'\d{3,}$', arti):
+            return re.sub(r'(\d)\d{2,}$', r'\1', arti)
+        else:
+            return re.sub(r'\d{1,2}$', '', arti)
+
+    df['arti'] = df['arti'].apply(normalize)
     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
 

server/backend/handlers/wb_handler.py

@@ -20,6 +20,13 @@ def process_sheet(df, document_type:str):
     #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
+    def normalize(arti):
+        if re.search(r'\d{3,}$', arti):
+            return re.sub(r'(\d)\d{2,}$', r'\1', arti)
+        else:
+            return re.sub(r'\d{1,2}$', '', arti)
+
+    df['arti'] = df['arti'].apply(normalize)
     df['price'] = df['price'].astype(float) #Float to Int, if exists  
     df['counts'] = df['counts'].astype(int) #Float to Int, if exists
     

server/backend/handlers/wb_purchases_handler.py

@@ -18,6 +18,13 @@ def process_sheet(df, real_arti:str, real_quantity:str, real_sum_1:str):
     df.rename(columns={"Unnamed: 1": 'arti', "Unnamed: 3": 'counts', "Unnamed: 4": 'price'}, inplace=True) #переименовываем для pydantic
     #Нормализация
     df['arti'] = df['arti'].astype(str).str.upper().str.extract(f'({settings.PATTERN})') #arti под regex
+    def normalize(arti):
+        if re.search(r'\d{3,}$', arti):
+            return re.sub(r'(\d)\d{2,}$', r'\1', arti)
+        else:
+            return re.sub(r'\d{1,2}$', '', arti)
+
+    df['arti'] = df['arti'].apply(normalize)
     df['arti'] = df['arti'].replace(Translit.TRANSLIT, regex=True)
     df['price'] = df['price'].str.replace(' ', '', regex=False).str.replace(',', '.', regex=False).astype(float) #переделка к норм виду и преобразование в float
     df['counts'] = df['counts'].astype(int) #Float to Int, if exists

server/backend/handlers/yandex_handler.py

@@ -18,6 +18,13 @@ def process_sheet(df, multiply_price=1, sheet_name=''):
     #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
+    def normalize(arti):
+        if re.search(r'\d{3,}$', arti):
+            return re.sub(r'(\d)\d{2,}$', r'\1', arti)
+        else:
+            return re.sub(r'\d{1,2}$', '', arti)
+
+    df['arti'] = df['arti'].apply(normalize)
     df['price'] = df['price'].astype(float) #To float, if exists 
     df['counts'] = df['counts'].astype(int) #To float, if exists