changing defs to methods and variables to attributes. Trial and error. Code seems to be broken right now

shopping_api.py

@@ -6,7 +6,10 @@ import pandas as pd
 with open('cat_list.txt') as jf:
     cat_list = json.load(jf)
 
+big_data = pd.read_csv('big_data.csv')
+
 class FindingApi:
+    '''Some docstring to get rid of linting errors'''
     finding_service = [
         'findItemsAdvanced', 'findCompletedItems',
         'findItemsByKeywords', 'findItemsIneBayStores', 'findItemsByCategory',
@@ -16,8 +19,9 @@ class FindingApi:
 
     # departments = ["3034","93427"] (womens and mens)
 
-    def get_ids_from_cats():
-        itemid_results_list = []
+    def get_ids_from_cats(self):
+        '''Stop bothering me for docstrings.'''
+        self.itemid_results_list = []
         for category_id in cat_list:
             for i in FindingApi.pageNumber:
                 params = {
@@ -29,44 +33,47 @@ class FindingApi:
                     "paginationInput.entriesPerPage":"100",
                     "paginationInput.PageNumber":FindingApi.pageNumber[i]
                     }
-                response = requests.get("https://svcs.ebay.com/services/search/FindingService/v1", params=params)
+                response = requests.get("https://svcs.ebay.com/services/search/FindingService/v1",
+                        params=params)
                 data = response.json()
-                for item in data['findItemsByCategoryResponse'][0]['searchResult'[0]['item']:
-                    itemid_results_list.append(item['itemId']) # above this line you will have to check whether the id exists in the pandas dataframe before you go on to create your list for the shopping
-                    #api: check df --> if does not exist, append itemId to itemid_results_list --> with curated list, make a list of 20-itemId lists. 
-        item_id_results = ','.join(item_id_results_list) # you will have to split this into 20 item lists strings to feed into ItemID param. Consider making a list of str lists.
+                for item in data['findItemsByCategoryResponse'][0]['searchResult'][0]['item']:
+                    if item not in big_data.values:
+                        itemid_results_list.append(item['itemId'])
+        item_id_results = [','.join(itemid_results_list[n:n+20]) for n in list(range(0,
+            len(itemid_results_list), 20))]
+
         return item_id_results
 
-class ShoppingApi:
+class ShoppingApi(FindingApi):
+    self.item_id_results = FindingApi.get_ids_from_cats()
+    def get_item_from_findItemsByCategory(self):
+        for twentyId in item_id_results:
+            params = {
+                "callname":"GetMultipleItems",
+                "appid":"scottbea-xlister-PRD-6796e0ff6-14862949",
+                "version":"671",
+                "responseencoding":"JSON",
+                "ItemID":twentyId, # you pass in a list? If not then maybe a comma-separated
+                "IncludeSelector":"ItemSpecifics",
+                }
 
-    def get_item_from_findItemsByCategory():
+            response = requests.get("https://open.api.ebay.com/shopping?", params=params)
+            data = response.json()
 
-        params = {
-            "callname":"GetMultipleItems",
-            "appid":"scottbea-xlister-PRD-6796e0ff6-14862949",
-            "version":"671",
-            "responseencoding":"JSON",
-            "ItemID":item_id_results, # you pass in a list? If not then maybe a comma-separated
-            "IncludeSelector":"ItemSpecifics",
-            }
+            names = []
+            values = []
+            nvl = data['Item'][0]['ItemSpecifics']['NameValueList']
 
-        response = requests.get("https://open.api.ebay.com/shopping?", params=params)
-        data = response.json()
+            for nvl_dict in nvl:
+                names.append(nvl_dict['Name'])
+                values.append(nvl_dict['Value'])
 
-        names = []
-        values = []
-        nvl = data['Item'][0]['ItemSpecifics']['NameValueList']
-
-        for nvl_dict in nvl:
-            names.append(nvl_dict['Name'])
-            values.append(nvl_dict['Value'])
-
-        nvl_dict = dict(zip(names, values))
-        data.update(nvl_dict)
-        df = pd.json_normalize(data)
-        df.to_csv('big_data.csv')
+            nvl_dict = dict(zip(names, values))
+            data.update(nvl_dict)
+            df = pd.json_normalize(data)
+            df.to_csv('big_data.csv')
 
 # Limited to 5000 calls to shopping api per day, and getMultpileitems service maxes out at 20 items
 # per call leaving you 100,000 items per day for you pandas dataframe initially. So you'll have
-# to divide these up into the categories. This will leave you with about 6.25K results per cat. 
-# More than enough data for your dataset. Consider 
+# to divide these up into the categories. This will leave you with about 6.25K results per cat.
+# More than enough data for your dataset. Consider