ebay-ml-lister/ebay_api_test.py

import requests
import json
from bs4 import BeautifulSoup as b
import pandas as p

# keywords = input('keyword search: ')

with open('cat_list.txt') as jf:
    cat_list = json.load(jf)
finding_service = ['findItemsAdvanced', 'findCompletedItems', 'findItemsByKeywords', 'findItemsIneBayStores', 'findItemsByCategory', 'findItemsByProduct']

pageNumber = list(range(1,101))

# departments = ["3034","93427"]

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)
# can use pandas.json_normalize(custom dict cobbled from respons.json())


# Additional problem you will run into when getting labeled data is shoe types and features not in features, accents, styles, categories or subcategories.