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262 lines
10 KiB
262 lines
10 KiB
__author__ = 'DarkWeb'
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# Here, we are importing the auxiliary functions to clean or convert data
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from MarketPlaces.Utilities.utilities import *
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# Here, we are importing BeautifulSoup to search through the HTML tree
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from bs4 import BeautifulSoup
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# This is the method to parse the Description Pages (one page to each Product in the Listing Pages)
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def wethenorth_description_parser(soup):
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# Fields to be parsed
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vendor = "-1" # 0 *Vendor_Name
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success = "-1" # 1 Vendor_Successful_Transactions
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rating_vendor = "-1" # 2 Vendor_Rating
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name = "-1" # 3 *Product_Name
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describe = "-1" # 4 Product_Description
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CVE = "-1" # 5 Product_CVE_Classification (Common Vulnerabilities and Exposures)
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MS = "-1" # 6 Product_MS_Classification (Microsoft Security)
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category = "-1" # 7 Product_Category
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views = "-1" # 8 Product_Number_Of_Views
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reviews = "-1" # 9 Product_Number_Of_Reviews
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rating_item = "-1" # 10 Product_Rating
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addDate = "-1" # 11 Product_AddedDate
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BTC = "-1" # 12 Product_BTC_SellingPrice
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USD = "-1" # 13 Product_USD_SellingPrice
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EURO = "-1" # 14 Product_EURO_SellingPrice
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sold = "-1" # 15 Product_QuantitySold
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left = "-1" # 16 Product_QuantityLeft
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shipFrom = "-1" # 17 Product_ShippedFrom
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shipTo = "-1" # 18 Product_ShippedTo
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image = "-1" # 19 Product_Image
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vendor_image = "-1" # 20 Vendor_Image
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# Finding Product Name
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listDes = soup.find('div', {'class': "listDes"})
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name = listDes.find('h2').text
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name = cleanString(name)
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name = name.strip()
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# Finding Vendor
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vendor = listDes.find('b').text
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vendor = cleanString(vendor)
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vendor = vendor.strip()
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# Finding Vendor Rating
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rating = listDes.find('span', {'class': 'levelSet'})
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rating = rating.text
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rating = cleanNumbers(rating)
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rating_vendor = rating.strip()
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# Finding Prices - all prices in We The North are in CAD, I left the CAD in the resulting String so that it would show CAD for all prices
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padp = listDes.find('p', {'class': 'padp'})
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USD = padp.find('span').text
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USD = USD.strip()
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BTC = padp.find_next_sibling('p').text
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BTC = cleanNumbers(BTC)
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BTC = BTC.strip()
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# Finding Escrow - no escrow on WTN market
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shipping_info = listDes.find('tbody')
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if "Digital" not in shipping_info:
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shipping_info = shipping_info.find_all('tr')
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row1 = shipping_info[0].find_all('td')
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# Finding Shipment Information (Origin)
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shipFrom = row1[-1].text
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shipFrom = cleanString(shipFrom)
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shipFrom = shipFrom.strip()
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if shipFrom == "":
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shipFrom = "-1"
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row2 = shipping_info[1].find_all('td')
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# Finding Shipment Information (Destination)
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shipTo = row2[-1].text
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shipTo = cleanString(shipTo)
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shipTo = shipTo.strip()
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if shipTo == "":
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shipTo = "-1"
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# Finding the Product description
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describe = soup.find("div", {'class': 'tabcontent'})
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describe = describe.find('p').text
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describe = cleanString(describe)
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describe = describe.strip()
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# Searching for CVE and MS categories
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# no CVE or MS for WTN market
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# Populating the final variable (this should be a list with all fields scraped)
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row = (vendor, rating_vendor, success, name, describe, CVE, MS, category, views, reviews, rating_item, addDate,
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BTC, USD, EURO, sold, left, shipFrom, shipTo, image, vendor_image)
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# Sending the results
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return row
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# This is the method to parse the Listing Pages
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def wethenorth_listing_parser(soup):
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# Fields to be parsed
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nm = 0 # *Total_Products (Should be Integer)
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mktName = "WeTheNorth" # 0 *Marketplace_Name
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vendor = [] # 1 *Vendor y
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rating_vendor = [] # 2 Vendor_Rating
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success = [] # 3 Vendor_Successful_Transactions
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name = [] # 4 *Product_Name y
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CVE = [] # 5 Product_CVE_Classification (Common Vulnerabilities and Exposures)
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MS = [] # 6 Product_MS_Classification (Microsoft Security)
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category = [] # 7 Product_Category y
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describe = [] # 8 Product_Description
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views = [] # 9 Product_Number_Of_Views
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reviews = [] # 10 Product_Number_Of_Reviews
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rating_item = [] # 11 Product_Rating
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addDate = [] # 12 Product_AddDate
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BTC = [] # 13 Product_BTC_SellingPrice
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USD = [] # 14 Product_USD_SellingPrice y
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EURO = [] # 15 Product_EURO_SellingPrice
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sold = [] # 16 Product_QuantitySold
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qLeft =[] # 17 Product_QuantityLeft
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shipFrom = [] # 18 Product_ShippedFrom
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shipTo = [] # 19 Product_ShippedTo
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image = [] # 20 Product_Image
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image_vendor = [] # 21 Vendor_Image
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href = [] # 22 Product_Links
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right_content = soup.find('div', {"class": "right-content"})
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listing = right_content.findAll('div', {"class": "col-1search"})
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listing = listing[3:]
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# Populating the Number of Products
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nm = len(listing)
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for a in listing:
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bae = a.findAll('a', href=True)
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# Adding the url to the list of urls
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link = bae[0].get('href')
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href.append(link)
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# Finding the Vendor
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vendor_name = a.find('p', {'class': 'padp'})
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vendor_name = vendor_name.find('a').text
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vendor_name = cleanString(vendor_name)
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vendor_name = vendor_name.strip()
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vendor.append(vendor_name)
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# Finding the Product
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product = a.find('div', {'class': 'col-1centre'})
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product = product.find('div', {'class': 'head'}).find('a').text
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product = cleanString(product)
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product = product.strip()
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name.append(product)
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# Finding the Category
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category_name = a.find('p', {'class': 'padp'}).text
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first_dash = category_name.find('-')
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second_dash = category_name[first_dash+1:].find('-')
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category_name = category_name[first_dash+1: first_dash + second_dash]
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category_name = cleanString(category_name)
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category_name = category_name.strip()
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category.append(category_name)
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# Finding Success Transactions
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vendor_success = a.find('p', {'class': 'padp'}).text
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first_dash = vendor_success.find('(')
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vendor_success = vendor_success[first_dash + 1:]
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vendor_success = cleanNumbers(vendor_success)
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vendor_success = vendor_success.strip()
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success.append(vendor_success)
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# Finding Views
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view_count = a.text
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view_count = view_count[view_count.find('Views:'): view_count.find('Sales:')]
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view_count = view_count.replace('Views:', ' ')
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view_count = cleanNumbers(view_count)
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view_count = view_count.strip()
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views.append(view_count)
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# Finding Quantity Sold
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sold_count = a.text
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sold_count = sold_count[sold_count.find('Sales:'): sold_count.find('Short')]
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sold_count = sold_count.replace('Sales:', ' ')
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sold_count = cleanNumbers(sold_count)
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sold_count = sold_count.strip()
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sold.append(sold_count)
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right = a.find('div', {'class': 'col-1right'})
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# Finding USD
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usd = right.find('a').text
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usd = "CAD " + usd.strip()
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USD.append(usd)
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# Finding BTC
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btc = right.text
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first_dash = btc.find('(')
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second_dash = btc[first_dash + 1:].find(')')
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btc = btc[first_dash + 1: first_dash + second_dash]
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btc = cleanNumbers(btc)
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btc = btc.strip()
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BTC.append(btc)
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# Finding Product Image
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product_image = right.find('img')
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product_image = product_image.get('src')
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product_image = product_image.split('base64,')[-1]
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image.append(product_image)
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# Searching for CVE and MS categories
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# no CVE or MS in WTN market
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cve = a.findAll(text=re.compile('CVE-\d{4}-\d{4}'))
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if not cve:
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cveValue="-1"
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else:
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cee = " "
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for idx in cve:
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cee += (idx)
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cee += " "
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cee = cee.replace(',', ' ')
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cee = cee.replace('\n', '')
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cveValue=cee
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CVE.append(cveValue)
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ms = a.findAll(text=re.compile('MS\d{2}-\d{3}'))
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if not ms:
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MSValue="-1"
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else:
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me = " "
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for im in ms:
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me += (im)
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me += " "
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me = me.replace(',', ' ')
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me = me.replace('\n', '')
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MSValue=me
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MS.append(MSValue)
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# Populate the final variable (this should be a list with all fields scraped)
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return organizeProducts(mktName, nm, vendor, rating_vendor, success, name, CVE, MS, category, describe, views,
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reviews, rating_item, addDate, BTC, USD, EURO, sold, qLeft, shipFrom, shipTo, href, image, image_vendor)
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def wethenorth_links_parser(soup):
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# Returning all links that should be visited by the Crawler
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href = []
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right_content = soup.find('div',{"class": "right-content"})
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listing = right_content.findAll('div', {"class": "col-1search"})
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#cut out the irrelevant products that are in blue, the first three products of each page usually unrelated
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listing = listing[3:]
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for a in listing:
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link = a.find('a')
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link = link['href']
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href.append(link)
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return href
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