removed some linting errors and figured out how to add dicts to pandas df (see shopping_test.py comments
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@ -7,14 +7,17 @@ import pandas as p
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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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finding_service = ['findItemsAdvanced', 'findCompletedItems', 'findItemsByKeywords', 'findItemsIneBayStores', 'findItemsByCategory', 'findItemsByProduct']
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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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itemid_results_list = []
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for categoryID in cat_list[0:2]:
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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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"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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@ -23,7 +26,7 @@ for categoryID in cat_list:
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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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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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@ -24,4 +24,9 @@ for nvl_dict in nvl:
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values.append(nvl_dict['Value'])
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nvl_dict = dict(zip(names, values))
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data.update(nvl_dict)
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df = pd.json_normalize(data)
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df.to_csv('big_data.csv')
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# to append items to the pandas dataframe/csv, use var = df.append(json/dict, ignore_index=True)
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# you will have to redefine var as a new dataframe everytime, too for some reason (I'm wondering
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# if this is a bug?).
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