deleted shopping_test.py and saved as shopping_api.py. Added classes including from finding_api.py

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spbeach46 2020-10-04 23:39:13 -07:00
parent 752fa7efaa
commit 77038e92f8

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shopping_api.py Normal file
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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