抓取时空输出文件

Question

我知道我已经问过类似的问题，但它是一个新的蜘蛛，我有同样的问题（Crawling data successfully but cannot scraped or write it into csv）...我把我的另一个蜘蛛放在这里，我应该有一个输出的例子，我通常需要获取输出文件的所有信息......有没有人可以帮助我？我必须在周五完成这个蜘蛛......所以，我很着急！

奇怪的是，我的Fnac.csv被创建，但始终是空的...所以我试图直接运行我的蜘蛛我想抓取的一个页面的例子，我有我需要的所有信息...所以，我不明白...也许问题来自我的Rules什么的？

我的蜘蛛：

# -*- coding: utf-8 -*- 
# Every import is done for a specific use 
import scrapy          # Once you downloaded scrapy, you have to import it in your code to use it. 
import re           # To use the .re() function, which extracts just a part of the text you crawl. It's using regex (regular expressions) 
import numbers          # To use mathematics things, in this case : numbers. 
from fnac.items import FnacItem      # To return the items you want. Each item has a space allocated in the momery, created in the items.py file, which is in the second cdiscount_test directory. 
from urllib.request import urlopen     # To use urlopen, which allow the spider to find the links in a page that is in the actual page. 
from scrapy.spiders import CrawlSpider, Rule  # To use rules and LinkExtractor, which allowed the spider to follow every url on the page you crawl. 
from scrapy.linkextractors import LinkExtractor  # Look above. 
from bs4 import BeautifulSoup      # To crawl an iframe, which is a page in a page in web prgrammation. 

# Your spider 
class Fnac(CrawlSpider): 
    name = 'FnacCom'        # Name of your spider. You call it in the anaconda prompt. 
    allowed_domains = ['fnac.com']    # Web domains allowed by you, your spider cannot enter on a page which is not in that domain. 
    start_urls = ['https://www.fnac.com/Index-Vendeurs-MarketPlace/A/']  # The first link you crawl. 

    # To allow your spider to follow the urls that are on the actual page. 
    rules = (
     Rule(LinkExtractor(), callback='parse_start_url'), 
    ) 

    # Your function that crawl the actual page you're on. 
    def parse_start_url(self, response): 
     item = FnacItem() # The spider now knowws that the items you want have to be stored in the item variable. 

     # First data you want which are on the actual page. 
     nb_sales = response.xpath('//body//table[@summary="données détaillée du vendeur"]/tbody/tr/td/span/text()').re(r'([\d]*) ventes') 
     country = response.xpath('//body//table[@summary="données détaillée du vendeur"]/tbody/tr/td/text()').re(r'([A-Z].*)') 

     # To store the data in their right places. 
     item['nb_sales'] = ''.join(nb_sales).strip() 
     item['country'] = ''.join(country).strip() 

     # Find a specific link on the actual page and launch this function on it. It's the place where you will find your two first data. 
     test_list = response.xpath('//a/@href') 
     for test_list in response.xpath('.//div[@class="ProductPriceBox-item detail"]'): 
      temporary = response.xpath('//div[@class="ProductPriceBox-item detail"]/div/a/@href').extract() 
      for i in range(len(temporary)): 
       scrapy.Request(temporary[i], callback=self.parse_start_url, meta={'dont_redirect': True, 'item': item}) 

     # To find the iframe on a page, launch the next function. 
     yield scrapy.Request(response.url, callback=self.parse_iframe, meta={'dont_redirect': True, 'item': item}) 

    # Your function that crawl the iframe on a page 
    def parse_iframe(self, response): 
     f_item1 = response.meta['item'] # Just to use the same item location you used above. 

     # Find all the iframe on a page. 
     soup = BeautifulSoup(urlopen(response.url), "lxml") 
     iframexx = soup.find_all('iframe') 

     # If there's at least one iframe, launch the next function on it 
     if (len(iframexx) != 0): 
      for iframe in iframexx: 
       yield scrapy.Request(iframe.attrs['src'], callback=self.extract_or_loop, meta={'dont_redirect': True, 'item': f_item1}) 

     # If there's no iframe, launch the next function on the link of the page where you looked after the potential iframe. 
     else: 
      yield scrapy.Request(response.url, callback=self.extract_or_loop, meta={'dont_redirect': True, 'item': f_item1}) 

    # Function to find the other data. 
    def extract_or_loop(self, response): 
     f_item2 = response.meta['item'] # Just to use the same item location you used above. 

     # The rest of the data you want. 
     address = response.xpath('//body//div/p/text()').re(r'.*Adresse \: (.*)\n?.*') 
     email = response.xpath('//body//div/ul/li[contains(text(),"@")]/text()').extract() 
     name = response.xpath('//body//div/p[@class="customer-policy-label"]/text()').re(r'Infos sur la boutique \: ([a-zA-Z0-9]*\s*)') 
     phone = response.xpath('//body//div/p/text()').re(r'.*Tél \: ([\d]*)\n?.*') 
     siret = response.xpath('//body//div/p/text()').re(r'.*Siret \: ([\d]*)\n?.*') 
     vat = response.xpath('//body//div/text()').re(r'.*TVA \: (.*)') 

     # If the name of the seller exist, then return the data. 
     if (len(name) != 0): 
      f_item2['name'] = ''.join(name).strip() 
      f_item2['address'] = ''.join(address).strip() 
      f_item2['phone'] = ''.join(phone).strip() 
      f_item2['email'] = ''.join(email).strip() 
      f_item2['vat'] = ''.join(vat).strip() 
      f_item2['siret'] = ''.join(siret).strip() 
      yield f_item2 

     # If not, there was no data on the page and you have to find all the links on your page and launch the first function on them. 
     else: 
      for sel in response.xpath('//html/body'): 
       list_urls = sel.xpath('//a/@href').extract() 
       list_iframe = response.xpath('//div[@class="ProductPriceBox-item detail"]/div/a/@href').extract() 
       if (len(list_iframe) != 0): 
        for list_iframe in list_urls: 
         yield scrapy.Request(list_iframe, callback=self.parse_start_url, meta={'dont_redirect': True}) 
       for url in list_urls: 
        yield scrapy.Request(response.urljoin(url), callback=self.parse_start_url, meta={'dont_redirect': True}) 

我的设置：

BOT_NAME = 'fnac' 

SPIDER_MODULES = ['fnac.spiders'] 
NEWSPIDER_MODULE = 'fnac.spiders' 
DOWNLOAD_DELAY = 2 
COOKIES_ENABLED = False 
ITEM_PIPELINES = { 
    'fnac.pipelines.FnacPipeline': 300, 
} 

我的管道：

# -*- coding: utf-8 -*- 
from scrapy import signals 
from scrapy.exporters import CsvItemExporter 

# Define your item pipelines here 
# 
# Don't forget to add your pipeline to the ITEM_PIPELINES setting 
# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html 

# Define your output file. 
class FnacPipeline(CsvItemExporter): 
    def __init__(self): 
     self.files = {} 

    @classmethod 
    def from_crawler(cls, crawler): 
     pipeline = cls() 
     crawler.signals.connect(pipeline.spider_opened, signals.spider_opened) 
     crawler.signals.connect(pipeline.spider_closed, signals.spider_closed) 
     return pipeline 

    def spider_opened(self, spider): 
     f = open('..\\..\\..\\..\\Fnac.csv', 'w').close() 
     file = open('..\\..\\..\\..\\Fnac.csv', 'wb') 
     self.files[spider] = file 
     self.exporter = CsvItemExporter(file) 
     self.exporter.start_exporting() 

    def spider_closed(self, spider): 
     self.exporter.finish_exporting() 
     file = self.files.pop(spider) 
     file.close() 

    def process_item(self, item, spider): 
     self.exporter.export_item(item) 
     return item 

我的项目：

# -*- coding: utf-8 -*- 
import scrapy 

# Define here the models for your scraped items 

# See documentation in: 
# http://doc.scrapy.org/en/latest/topics/items.html 

class FnacItem(scrapy.Item): 
    # define the fields for your items : 
    # name = scrapy.Field() 
    name = scrapy.Field() 
    nb_sales = scrapy.Field() 
    country = scrapy.Field() 
    address = scrapy.Field() 
    siret = scrapy.Field() 
    vat = scrapy.Field() 
    phone = scrapy.Field() 
    email = scrapy.Field() 

我在提示符下运行蜘蛛写的命令是：

scrapy crawl FnacCom

输出的一个例子是：

2017-08-08 10:21:54 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-Panasonic/TV-par-marque/nsh474980/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:21:56 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-Philips/TV-par-marque/nsh474981/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:21:58 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-Sony/TV-par-marque/nsh475001/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:01 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-LG/TV-par-marque/nsh474979/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:03 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-Samsung/TV-par-marque/nsh474984/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:06 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-Television/TV-par-marque/shi474972/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:08 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-Television/TV-par-prix/shi474946/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:11 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-Television/TV-par-taille-d-ecran/shi474945/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:12 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-Television/TV-par-Technologie/shi474944/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:15 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/Smart-TV-TV-connectee/TV-par-Technologie/nsh474953/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:18 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-QLED/TV-par-Technologie/nsh474948/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:21 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-4K-UHD/TV-par-Technologie/nsh474947/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:23 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/Toutes-les-TV/TV-Television/nsh474940/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:26 [scrapy.extensions.logstats] INFO: Crawled 459 pages (at 24 pages/min), scraped 0 items (at 0 items/min) 
2017-08-08 10:22:26 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-Television/shi474914/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:28 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/partner/canalplus#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:34 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/Meilleures-ventes-TV/TV-Television/nsh474942/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:37 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/Toutes-nos-Offres/Offres-de-remboursement/shi159784/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:38 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/Offres-Adherents/Toutes-nos-Offres/nsh81745/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:41 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/labofnac#bl=MMtvh#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:44 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/Lecteur-et-Enregistreur-DVD-Blu-Ray/Lecteur-DVD-Blu-Ray/shi475063/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:46 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/TV-OLED/TV-par-Technologie/nsh474949/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:49 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/Lecteur-DVD-Portable/Lecteur-et-Enregistreur-DVD-Blu-Ray/nsh475064/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:52 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/Home-Cinema/Home-Cinema-par-marque/shi475116/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:52 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/Univers-TV/Univers-Ecran-plat/cl179/w-4#bl=MMtvh> (referer: https://www.fnac.com) 
2017-08-08 10:22:55 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.fnac.com/Casque-TV-HiFi/Casque-par-usage/nsh450507/w-4#bl=MMtvh> (referer: https://www.fnac.com) 

非常感谢你的帮助！

Answer 1

我写了一个小的代码重构炫耀如何蜘蛛可以明确写入而不使用crawlspider使用常用成语scrapy：

class Fnac(Spider): 
    name = 'fnac.com' 
    allowed_domains = ['fnac.com'] 
    start_urls = ['https://www.fnac.com/Index-Vendeurs-MarketPlace/0/'] # The first link you crawl. 

    def parse(self, response): 
     # parse sellers 
     sellers = response.xpath("//h1[contains(selftext(),'MarketPlace')]/following-sibling::ul/li/a/@href").extract() 
     for url in sellers: 
      yield Request(url, callback=self.parse_seller) 

     # parse other pages A-Z 
     pages = response.css('.pagerletter a::attr(href)').extract() 
     for url in pages: 
      yield Request(url, callback=self.parse) 

    def parse_seller(self, response): 
     nb_sales = response.xpath('//body//table[@summary="données détaillée du vendeur"]/tbody/tr/td/span/text()').re(r'([\d]*) ventes') 
     country = response.xpath('//body//table[@summary="données détaillée du vendeur"]/tbody/tr/td/text()').re(r'([A-Z].*)') 
     item = FnacItem() 
     # To store the data in their right places. 
     item['nb_sales'] = ''.join(nb_sales).strip() 
     item['country'] = ''.join(country).strip() 
     # go to details page now 
     details_url = response.xpath("//iframe/@src[contains(.,'retour')]").extract_first() 
     yield Request(details_url, self.parse_seller_details, 
         meta={'item': item}) # carry over our item to next response 

    def parse_seller_details(self, response): 
     item = response.meta['item'] # get item that's got filled in `parse_seller` 
     address = response.xpath('//body//div/p/text()').re(r'.*Adresse \: (.*)\n?.*') 
     email = response.xpath('//body//div/ul/li[contains(text(),"@")]/text()').extract() 
     # parse here 
     yield item