From 77038e92f8f541978463b326ddfb64da363f4c61 Mon Sep 17 00:00:00 2001 From: spbeach46 Date: Sun, 4 Oct 2020 23:39:13 -0700 Subject: [PATCH] deleted shopping_test.py and saved as shopping_api.py. Added classes including from finding_api.py --- shopping_api.py | 67 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 shopping_api.py diff --git a/shopping_api.py b/shopping_api.py new file mode 100644 index 0000000..a5c151e --- /dev/null +++ b/shopping_api.py @@ -0,0 +1,67 @@ +import requests +import json +from bs4 import BeautifulSoup as b +import pandas as pd + +with open('cat_list.txt') as jf: + cat_list = json.load(jf) +finding_service = ['findItemsAdvanced', 'findCompletedItems', 'findItemsByKeywords', 'findItemsIneBayStores', 'findItemsByCategory', 'findItemsByProduct'] + +class Finding_api: + pageNumber = list(range(1, 63)) + + # departments = ["3034","93427"] + + def get_ids(self): + itemid_results_list = [] + for categoryID in cat_list[0:2]: + params = { + "OPERATION-NAME":finding_service[4], + "SECURITY-APPNAME":"scottbea-xlister-PRD-6796e0ff6-14862949", + "SERVICE-VERSION":"1.13.0", + "RESPONSE-DATA-FORMAT":"JSON", + "categoryId":categoryID , + "paginationInput.entriesPerPage":"100", + "paginationInput.PageNumber":pageNumber[0] + } + # extract item id here for piping into shopping_test.py + + response = requests.get("https://svcs.ebay.com/services/search/FindingService/v1", params=params) + data = response.json() + pretty_data = json.dumps(data, indent=2) + return data + +class Shopping_api: + + def get_item(self): + + 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", + } + + response = requests.get("https://open.api.ebay.com/shopping?", params=params) + data = response.json() + pretty_data = json.dumps(data, indent=2) + + 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. Consider