基于scrapy爬虫框架的应用案例之一：百度阅读(无登录爬虫)

Love The Way You Lie 2021-09-18 03:32 233阅读 0赞

### **基于python的Scrapy框架做爬虫确实简单（别人造好的轮子，我们来组装即可）** ###

### **无需登录的** ###

items.py模块
    
    from scrapy  import Item,Field
    
    
    class BookItem(Item):
        title = Field()
        price = Field()
        tags = Field()
        author = Field()
        copyright = Field()
        score = Field()

### **简单爬虫三部曲：** ###

** Scrapy爬虫第一步：获取所有的链接,并用yield关键字创建一个所有链接的生成器**

** Scrapy爬虫第二步：根据不同的链接发送Request请求,设定不同的parse解析回调函数逐步处理！**

** Scrapy爬虫第三步：所以的item数据都会经过pipeline类数据处理**

book.py爬虫模块
    
    # -*- coding: utf-8 -*-
    import re
    from scrapy import Spider, Request
    from scrapy_first.items import BookItem
    from scrapy.linkextractors import LinkExtractor
    
    
    class BookSpider(Spider):
        # name属性就是spider_name,【scrapy crawl spider_name】执行的文件名(必须是唯一)
        name = 'book'
        allowed_domains = ['yuedu.baidu.com']
        start_urls = ['https://yuedu.baidu.com/rank/hotsale']
    
        def __init__(self, name=None, **kwargs):
            super().__init__(name=name, **kwargs)
            self.title = ""
            self.price = ""
            self.tags = ""
            self.author = ""
            self.copyright = ""
            self.score = ""
    
        Scrapy爬虫第一步：获取所有的链接,并用yield关键字创建一个所有链接的生成器
        def parse(self, response):
    
            url = response.urljoin(response.xpath("//div[@class='pager-inner']/a/@href").get())
            if url:
                yield Request(url=url, callback=self.parse)
    
            extractor = LinkExtractor(restrict_css=".book .al.title-link")
            for link in extractor.extract_links(response):
                yield Request(url=link.url, callback=self.parse_book)
    
        Scrapy爬虫第二步：根据不同的链接发送Request请求,设定不同的parse解析回调函数逐步处理！
        def parse_book(self, response):
            for book in response.css(".doc-info-bd.clearfix .content-block"):
                item = BookItem()
                item["title"] = book.xpath(".//h1[@class='book-title']/text()").get()
                item["price"] = book.xpath(".//span[@class='numeric']/text()").get()
                item["author"] = book.xpath(".//a[@class='doc-info-field-val doc-info-author-link']/text()").get()
                item["copyright"] = book.xpath(".//a[@class='doc-info-field-val']/text()").get()
                item["tags"] = [value.get() for value in book.xpath(".//a[@class='tag-item doc-info-field-val mb5']/text()")]
                item["score"] = book.xpath(".//span[@class='doc-info-read-count']/text()").get()
                yield item

pipeline数据处理模块
    
    
     Scrapy爬虫第三步：所以的item数据都会经过pipeline类数据处理
    
    class BookPipeline:
    
        # from_crawler返回一个实例对象,初始化配置文件读取的数据
        def __init__(self,count):
            self.count = count
    
        # 所有的数据都会经过这个方法，这个方法会自动重复调用(items列表组数)
        # 如果要过滤某些数据，在这个函数中过滤条件抛出DropItem异常即可！
        def process_item(self, item, spider):
            # 数据全部去空格和\n
            item["title"] = item.get("title").replace(r"\n","").strip()
            return item
    
        @classmethod
        def from_crawler(cls, crawler):
            return cls(
                # 从./settings.py文件中读取字段的方法
                count = crawler.settings["BOOK_FILTER_COUNT"]
            )
    
        def open_spider(self, spider):
            self.file = open('items.jl', 'w')
    
        def close_spider(self, spider):
            self.file.close()

./settings.py配置文件 
    
    pipeline模块执行配置
    
    ITEM_PIPELINES = {
       'scrapy_first.pipelines.BookPipeline': 1,
    }
    
    
    自定义变量：
    
    BOOK_FILTER_COUNT = 3
    
    
    数据输出配置：
    
    # 文件导出的路径当前目录下的export_data文件下
    FEED_URI = "export_data/%(name)s_%(time)s.json"
    
    # 输出文件的格式类型(JSON、CSV、XML)
    FEED_FORMAT = "json"
    
    # 输出文本编码格式(json默认格式不是utf-8)
    FEED_EXPORT_ENCODING = "UTF-8"