diff --git a/ebay_api.py b/ebay_api.py
new file mode 100644
index 0000000..f6117c0
--- /dev/null
+++ b/ebay_api.py
@@ -0,0 +1,85 @@
+import json
+import requests
+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'''
+    def __init__(self):
+        self.service = [
+            'findItemsAdvanced', 'findCompletedItems',
+            'findItemsByKeywords', 'findItemsIneBayStores', 'findItemsByCategory',
+            'findItemsByProduct'
+            ]
+        self.pageNumber = list(range(1, 63))
+
+    # departments = ["3034","93427"] (womens and mens)
+
+    def get_ids_from_cats(self):
+        '''Stop bothering me for docstrings.'''
+        itemid_results_list = []
+        for category_id in cat_list:
+            for i in self.pageNumber:
+                params = {
+                    "OPERATION-NAME":self.service[4],
+                    "SECURITY-APPNAME":"scottbea-xlister-PRD-6796e0ff6-14862949",
+                    "SERVICE-VERSION":"1.13.0",
+                    "RESPONSE-DATA-FORMAT":"JSON",
+                    "categoryId":category_id,
+                    "paginationInput.entriesPerPage":"100",
+                    "paginationInput.PageNumber":self.pageNumber[i]
+                    }
+                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']:
+                    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(FindingApi):
+    '''
+    I don't need no stinkin' dockstring, foo
+    '''
+    def get_item_from_findItemsByCategory(self, item_id_results):
+        for twenty_id in item_id_results:
+            params = {
+                "callname":"GetMultipleItems",
+                "appid":"scottbea-xlister-PRD-6796e0ff6-14862949",
+                "version":"671",
+                "responseencoding":"JSON",
+                "ItemID":twenty_id,
+                "IncludeSelector":"ItemSpecifics",
+                }
+
+            response = requests.get("https://open.api.ebay.com/shopping?", params=params)
+            data = response.json()
+
+            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')
+
+# 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.
+
+# Need to make sure dataframe gets important stuff outside of nvl. Also need to
+# change init method in findingapi to have variable pages and possibly variable
+# services.