Watches dealers scraping

I need to extract information from this website https://www.chrono24.com/search/haendler.htm. Inside we can find watch companies. We are asked to extract the email of those companies that have a physical store and not only online presence. But we have a big problem. This website does not provide us with the email or web address of these companies.

So to solve this problem I will follow the following steps:

Extract the name of the watch companies from the above mentioned page.
Create a spider that will crawl google search for the companies and provide us with the web addresses of these companies whenever possible.
Create another spider that enters these addresses and extracts the emails it finds.
Manually review the results obtained and complete the few missing ones.

To do all this we will use the scraping and crawling framework Scrapy and Splash. Splash is a lightweight web browser that is capable of processing multiple pages in parallel, executing custom JavaScript in the page context, and much more.

1 – Extract info from web

– First we need to import libraries:

import scrapy
import urllib
import json
from scrapy.selector import Selector
from os import remove, path

-Creating a class with diferents methods inside like this:

class RelojesSpider(scrapy.Spider):
    name = 'relojes'

    base_url = 'https://www.chrono24.com/search/haendler.htm?'

    # remove file datos.json if exist
    if path.exists('data_web.json'):
        remove('data_web.json')

    params={
        'showpage':'',
    }
    features = []  # create data_web.json file

    def start_requests(self):
        for i in range(1,111):
            self.params['showpage'] = i
            next_page = self.base_url + urllib.parse.urlencode(self.params)
            print("AAAAA: ",next_page)
            yield scrapy.Request(next_page, callback = self.parse)

    def parse(self, response):
        for card in response.css('.border-radius-large'):
            company = card.css('span a::text').get()
            country = card.css('.inline-block::text').get()
            if card.css('.i-retail-store'): #Choose with presence stores
                items = {
                    'company' : company,
                    'country' : country
                }
                yield items #salida para el archivo creado con Scrapy FEED(en settings)
                self.features.append(items) #creating data_web.json file

        with open('data_web.json', 'w+') as json_file:
            json_file.write(json.dumps(self.features, indent=2) +'\n')

-I get all the names of the stores with physical presence and their country.:

[
  {
    "company": "S&A Watches",
    "country": "Austria"
  },
  {
    "company": "Nautilus Watches",
    "country": "Italy"
  },
  {
    "company": "Juwelier Burger",
    "country": "The Netherlands"
  },
  {
    "company": "World Of Watches ATMOS Roma s.r.l",
    "country": "Italy"
  },
.........................................................
.........................................................
.........................................................
  {
    "company": "ZERO Co.,Ltd.",
    "country": "Japan"
  },
  {
    "company": "Zetton",
    "country": "Japan"
  },
  {
    "company": "\u304f\u3058\u3089time",
    "country": "Japan"
  }
]

2 – Crawl google search with this directions

– First we need to import libraries:

import requests
import urllib
from requests_html import HTMLSession
import json
from os import remove, path

def get_source(url):

    try:
        session = HTMLSession()
        response = session.get(url)
        return response

    except requests.exceptions.RequestException as e:
        print(e)

def get_results(query):

    query = urllib.parse.quote_plus(query)
    response = get_source("https://www.google.es/search?q=" + query)
 
    return response

def parse_results(response):

    css_identifier_result = ".MjjYud"
    css_identifier_title = "h3"
    css_identifier_link = ".yuRUbf a"
    css_identifier_text = ".VwiC3b"

    results = response.html.find(css_identifier_result)

    output = []

    #We dont want search this domains
    not_domains = (
        'https://www.google.',
        'https://google.',
        'https://webcache.googleusercontent.',
        'http://webcache.googleusercontent.',
        'https://policies.google.',
        'https://support.google.',
        'https://maps.google.',
        'https://www.chrono24.',
        'https://www.instagram.',
        'https://www.youtube.',
        'https://business.google.',
        'https://www.facebook.',
        'https://translate.google.',
        'https://m.facebook.',
        'https://timepeaks',
        'https://www.yelp',
    )

    for result in results:  
        try:
            link = result.find(css_identifier_link, first=True).attrs['href']
            if link.startswith(not_domains):
                pass
            else:
                item = {
                    #'title': result.find(css_identifier_title, first=True).text,
                    #'link': link.text,
                    'link': link,
                    #'text': result.find(css_identifier_text, first=True).text
                }
                output.append(item)
                break #show only the first link
        except:
            pass

    return output

def google_search(query):
    response = get_results(query)
    return parse_results(response)


#remove file keyWords_liks.json if exist
if path.exists('keyWords_liks.json'):
    remove('keyWords_liks.json')

#1 - Opening json file with the key words scraping and put in a file
content= ''
with open("data_web.json", 'r') as f:
    content = json.load(f)

#2 - we go through the json and get 'company' and search for google with google_search() that we created above.
#3 - we save the results of the addresses in keyWords_liks.json
features =[]
for i,cont in enumerate(content):
    words = content[i]['company']
    words_key = words + ' watches' #our key word(if its necessary)
    results = google_search(words_key)
    print("CONTENT: ",json.dumps(content[i], indent=2))
    print(results)
    print("\n")

    for elem in results: 
        for k,v in elem.items():
            items = {
                'key_words': words,
                'link' : v
            }
            features.append(items)

    with open('keyWords_liks.json', 'w') as json_file:
        json_file.write(json.dumps(features, indent=2) + '\n')

-I get all the names of the stores(key_words) with physical presence their possible link:

[
  {
    "key_words": "S&A Watches",
    "link": "https://sell.sa-watches.at/"
  },
  {
    "key_words": "Nautilus Watches",
    "link": "https://www.patek.com/en/collection/nautilus"
  },
  {
    "key_words": "Juwelier Burger",
    "link": "https://www.juwelierburger.com/DE/index"
  },
  {
    "key_words": "World Of Watches ATMOS Roma s.r.l",
    "link": "https://watchesinrome.it/"
  },
.........................................................
.........................................................
.........................................................
  },
  {
    "key_words": "ZERO Co.,Ltd.",
    "link": "https://zerowest.watch/"
  },
  {
    "key_words": "Zetton",
    "link": "https://www.ebay.com/str/zettonjapan"
  },
  {
    "key_words": "\u304f\u3058\u3089time",
    "link": "https://kujiratime.com/"
  }
]

3 – Spider to extract emails

– First we need to import libraries:

import scrapy
import json

# for regular expression
import re
# for selenium request
from scrapy_selenium import SeleniumRequest
from os import remove, path
# for link extraction
from scrapy.linkextractors.lxmlhtml import LxmlLinkExtractor

from parsel import Selector
from scrapy_splash import SplashRequest
import pandas as pd

class EmailspiderSpider2(scrapy.Spider):
    # name of spider
    name = 'emailspider2'

    features = []

    # remove file datos.json if exist
    if path.exists('allData.json'):
        remove('allData.json')

    # remove file datos.json if exist
    if path.exists('good.json'):
        remove('good.json')

    if path.exists('fail.json'):
        remove('fail.json')

    with open("data_words_search.json", 'r') as f:
        contenta = json.load(f)

    def start_requests(self):
        for i, cont in enumerate(self.contenta):
            company = self.contenta[i]['key_words']
            link = self.contenta[i]['link']

            yield SplashRequest(
                url=link,
                callback=self.parse,
                dont_filter=True,
                meta={'company' : company},
                #args={'wait': 15},
            )

    def parse(self, response):

        company = response.meta.get('company')

        links = LxmlLinkExtractor(allow=()).extract_links(response)
        Finallinks = [str(link.url) for link in links]

        links = []
        for link in Finallinks:
            if ('Contact' in link or 'contact' in link or 'service' in link or 'team' in link or 'Contacts' in link or 'About' in link or 'about' in link or 'CONTACT' in link or 'ABOUT' in link):
                links.append(link)
        
        links.append(str(response.url))  

        l = links[0]   añadida)
        links.pop(0) 
       
        # meta helps us to transfer links list from parse to parse_link
        yield SplashRequest(
            url=l,
            callback=self.parse_link,
            dont_filter=True,
            meta={'links': links, 'company' : company},
            #args={'wait': 1},
        )

    def parse_link(self, response):
 
        company = response.meta.get('company')
        links = response.meta['links']
        flag = 0
        print("LINKS_2: ", links)
        # links that contains following bad words are discarded
        bad_words = ['facebook', 'instagram', 'youtube', 'twitter', 'wiki', 'linkedin']

        for word in bad_words:
            # if any bad word is found in the current page url flag is assigned to 1
            if word in str(response.url):
                flag = 1
                break

        # if flag is 1 then no need to get email from that url/page
        if (flag != 1):

            html_text = str(response.text)
            # regular expression used for search emails
            email_list = re.findall('[\w\.-]+@[\w\.-]+', html_text)
            # set of email_list to get unique
            email_list = set(email_list)
            uniqueemail = set() #creamos lista de emails unicos con set
            if (len(email_list) != 0):
                for i in email_list:
                    # adding email to final uniqueemail
                    uniqueemail.add(i)

        # parse_link function is called till
        # if condition satisfy
        # else move to parsed function
        if (len(links) > 0):
            l = links[0]
            links.pop(0)
       
            yield SplashRequest(
                url=l,
                callback=self.parse_link,
                dont_filter=True,
                meta={'links': links, 'company' : company},
                #args={'wait': 2},
            )
        else:
            yield SplashRequest(
                url=response.url,
                callback=self.parsed,
                dont_filter=True,
                meta={'company': company, 'uniqueemail' : uniqueemail},
                #meta={'links': links, 'company': company},
                # args={'wait': 2},
            )


    def parsed(self, response):
        # emails list of uniqueemail set

        uniqueemail = response.meta.get('uniqueemail')
        company = response.meta.get('company')
        emails = list(uniqueemail)
        finalemail = []

        for email in emails:
            with open("extensionesweb.txt") as f:
                [finalemail.append(email) for line in f if email.split('.')[-1] in line]

        # Que nos muestre la web original(no de donde ha salido)
        items = {
            'company' : company,
            'web': response.url.split('/')[2],
            'email': finalemail
        }

        uniqueemail.clear() #reseteamos el contenido de uniqueemail

        self.features.append(items)
       
        with open('allData.json', 'w') as json_file:
            json_file.write(json.dumps(self.features, indent=2) + '\n')

        with open('allData.json') as f:
            datos = json.load(f)

        #usando pandas agrupamos por empresa
        df = pd.DataFrame(datos).groupby(['company'])\
            .agg({'web':lambda x: list(set(x)), 'email':lambda x: list(x)})\
            .reset_index()\
            .to_dict('r')
        #si queremos que se vean todos los enlaces, quitamos el set() de 'web'
        #y al definir items{} elegimos la que no corte la web y se quede con la primera parte

        #agrupamos los emails y separamos los que tienen en una lista(data)
        # y los que no en otra (fail)
        data = []
        fail = []
        for i in df:
            h = i['email']
            n = []
            for j in range(len(h)):
                n.extend(h[j])
                o = list(set(n))
            i['email'] = o
            i['web'] = ''.join(i['web']) #quitamos el modo 'lista' [www.balbalbla.com]
            if i['email'] == []:
                fail.append(i)
            else:
                data.append(i)

        # if items['email'] == []:
        #     self.fail.append(items)

        with open('fail.json', 'w') as json_file:
            json_file.write(json.dumps(fail, indent=2) + '\n')

        with open('good.json', 'w') as json_file:
            json_file.write(json.dumps(data, indent=2) + '\n')

Now we have two files, one with those who have managed to extract some email (good.json) and one with those who have not (fail.json).:

good.json:

[
  {
    "company": "Jopsons Jewellers",
    "web": "jopsonsjewellers.co.uk",
    "email": [
      "en*******@jo**************.uk"
    ]
  },
  {
    "company": "Joyer\u00eda Libremol",
    "web": "www.joyerialibremol.com",
    "email": [
      "in**@jo*************.com"
    ]
  },
  {
    "company": "Juwelier Bektas e.K.",
    "web": "juwelier-bektas.de",
    "email": [
      "in**@ju*************.de"
    ]
  },
.........................................................
.........................................................
.........................................................
   {
    "company": "luxurywatches",
    "web": "luxurywatchesnewyork.com",
    "email": [
      "in**@lu******************.com",
      "in**@lu*****************.de"
    ]
  },
  {
    "company": "van Houten Uhren - Gregory van Houten",
    "web": "www.vanhouten-uhren.de",
    "email": [
      "in**@va*************.de"
    ]
  }
]

fail.json:

[
  {
    "company": "CJ Charles Jewelers",
    "web": "www.cjcharles.com",
    "email": []
  },
  {
    "company": "CORELLO",
    "web": "corello.es",
    "email": []
  },
  {
    "company": "CRONOM\u00c9TRICA",
    "web": "www.linguee.de",
    "email": []
  },
  {
    "company": "Chronoshop s.r.o.",
    "web": "www.seikowatches.com",
    "email": []
  },
.........................................................
.........................................................
.........................................................
   {
    "company": "Luxusboerse Zurich Exclusive",
    "web": "www.luxusboerse.ch",
    "email": []
  },
  {
    "company": "M.P Preziosi",
    "web": "www.mpreziosi.it",
    "email": []
  },
  {
    "company": "Mika\u00ebl Dan",
    "web": "www.mikaeldan.com",
    "email": []
  }
]

4 – Review the results and complete missing emails

– We finally have all emails for these type of stores

[
  {
    "company":"Beverly Hills Watch Company",
    "web":"www.beverlyhillswatch.com",
    "email":[
      "in**@be***************.com"
    ]
  },
  {
    "company":"COMMIT",
    "web":"www.commit-watch.co.jp",
    "email":[
      "in**@co**********.jp"
    ]
  },
  {
    "company":"CORTI GIOIELLI",
    "web":"corti-gioielli.it",
    "email":[
      "co***********@li****.it"
    ]
  },
.........................................................
.........................................................
.........................................................
  {
    "company":"Almar",
    "web":"www.almarwatches.com",
    "email":[
      "in**@al**********.uk"
    ]
  },
  {
    "company":"Alward Luxury Watches",
    "web":"luxurywatches.ie",
    "email":[
      "in**@lu***********.ie"
    ]
  },
  {
    "company":"Antike Uhren Eder",
    "web":"www.uhreneder.de",
    "email":[
      "in**@uh*******.de"
    ]
  }
]

1 – Extract info from web

– First we need to import libraries:

-Creating a class with diferents methods inside like this:

-I get all the names of the stores with physical presence and their country.:

2 – Crawl google search with this directions

– First we need to import libraries:

-I get all the names of the stores(key_words) with physical presence their possible link:

3 – Spider to extract emails

– First we need to import libraries:

Now we have two files, one with those who have managed to extract some email (good.json) and one with those who have not (fail.json).:

good.json:

fail.json:

4 – Review the results and complete missing emails

– We finally have all emails for these type of stores

Like this: