86 lines
3.2 KiB
Python
86 lines
3.2 KiB
Python
import json
|
|
import requests
|
|
import pandas as pd
|
|
|
|
with open('cat_list.txt') as jf:
|
|
cat_list = json.load(jf)
|
|
|
|
big_data = pd.read_csv('big_data.csv')
|
|
|
|
class FindingApi:
|
|
'''Some docstring to get rid of linting errors'''
|
|
def __init__(self):
|
|
self.service = [
|
|
'findItemsAdvanced', 'findCompletedItems',
|
|
'findItemsByKeywords', 'findItemsIneBayStores', 'findItemsByCategory',
|
|
'findItemsByProduct'
|
|
]
|
|
self.pageNumber = list(range(1, 63))
|
|
|
|
# departments = ["3034","93427"] (womens and mens)
|
|
|
|
def get_ids_from_cats(self):
|
|
'''Stop bothering me for docstrings.'''
|
|
itemid_results_list = []
|
|
for category_id in cat_list:
|
|
for i in self.pageNumber:
|
|
params = {
|
|
"OPERATION-NAME":self.service[4],
|
|
"SECURITY-APPNAME":"scottbea-xlister-PRD-6796e0ff6-14862949",
|
|
"SERVICE-VERSION":"1.13.0",
|
|
"RESPONSE-DATA-FORMAT":"JSON",
|
|
"categoryId":category_id,
|
|
"paginationInput.entriesPerPage":"100",
|
|
"paginationInput.PageNumber":self.pageNumber[i]
|
|
}
|
|
response = requests.get("https://svcs.ebay.com/services/search/FindingService/v1",
|
|
params=params)
|
|
data = response.json()
|
|
for item in data['findItemsByCategoryResponse'][0]['searchResult'][0]['item']:
|
|
if item not in big_data.values:
|
|
itemid_results_list.append(item['itemId'])
|
|
item_id_results = [','.join(itemid_results_list[n:n+20]) for n in list(range(0,
|
|
len(itemid_results_list), 20))]
|
|
|
|
return item_id_results
|
|
|
|
class ShoppingApi(FindingApi):
|
|
'''
|
|
I don't need no stinkin' dockstring, foo
|
|
'''
|
|
def get_item_from_findItemsByCategory(self, item_id_results):
|
|
for twenty_id in item_id_results:
|
|
params = {
|
|
"callname":"GetMultipleItems",
|
|
"appid":"scottbea-xlister-PRD-6796e0ff6-14862949",
|
|
"version":"671",
|
|
"responseencoding":"JSON",
|
|
"ItemID":twenty_id,
|
|
"IncludeSelector":"ItemSpecifics",
|
|
}
|
|
|
|
response = requests.get("https://open.api.ebay.com/shopping?", params=params)
|
|
data = response.json()
|
|
|
|
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.
|
|
|
|
# Need to make sure dataframe gets important stuff outside of nvl. Also need to
|
|
# change init method in findingapi to have variable pages and possibly variable
|
|
# services.
|