33 lines
1.3 KiB
Python
33 lines
1.3 KiB
Python
import requests
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import json
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from bs4 import BeautifulSoup as b
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import pandas as p
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# keywords = input('keyword search: ')
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with open('cat_list.txt') as jf:
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cat_list = json.load(jf)
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finding_service = ['findItemsAdvanced', 'findCompletedItems', 'findItemsByKeywords', 'findItemsIneBayStores', 'findItemsByCategory', 'findItemsByProduct'] # list(set(re.findall(r'find\w+', response.text)))
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pageNumber = list(range(1,101))
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# departments = ["3034","93427"]
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item_results_list = []
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for categoryID in cat_list:
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params = {
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"OPERATION-NAME":finding_service[4],
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"SECURITY-APPNAME":"scottbea-xlister-PRD-6796e0ff6-14862949",
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"SERVICE-VERSION":"1.13.0",
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"RESPONSE-DATA-FORMAT":"JSON",
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"categoryId":categoryID ,
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"paginationInput.entriesPerPage":"100",
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"paginationInput.PageNumber":pageNumber[0]
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}
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# extract item id here for piping into shopping_test.py
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response = requests.get("https://svcs.ebay.com/services/search/FindingService/v1", params=params) #
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data = response.json()
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pretty_data = json.dumps(data, indent=2)
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# can use pandas.json_normalize(custom dict cobbled from respons.json())
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# Additional problem you will run into when getting labeled data is shoe types and features not in features, accents, styles, categories or subcategories.
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