Python爬虫入门教程 35-100 知乎网全站用户爬虫 scrapy

时间 2019-11-16

原文原文链接

爬前叨叨

全站爬虫有时候作起来其实比较容易，由于规则相对容易创建起来，只须要作好反爬就能够了，今天我们爬取知乎。继续使用scrapy固然对于这个小需求来讲，使用scrapy确实用了牛刀，不过毕竟本博客这个系列到这个阶段须要不断使用scrapy进行过分，so，我写了一会就写完了。html

你第一步找一个爬取种子，算做爬虫入口正则表达式

https://www.zhihu.com/people/zhang-jia-wei/followingmongodb

咱们须要的信息以下，全部的框图都是咱们须要的信息。json

获取用户关注名单

经过以下代码获取网页返回数据，会发现数据是由HTML+JSON拼接而成，增长了不少解析成本数组

class ZhihuSpider(scrapy.Spider):
    name = 'Zhihu'
    allowed_domains = ['www.zhihu.com']
    start_urls = ['https://www.zhihu.com/people/zhang-jia-wei/following']

    def parse(self, response):
        all_data = response.body_as_unicode()
        print(all_data)

首先配置一下基本的环境，好比间隔秒数，爬取的UA，是否存储cookies,启用随机UA的中间件DOWNLOADER_MIDDLEWAREScookie

middlewares.py 文件app

from zhihu.settings import USER_AGENT_LIST # 导入中间件
import random

class RandomUserAgentMiddleware(object):
    def process_request(self, request, spider):
        rand_use  = random.choice(USER_AGENT_LIST)
        if rand_use:
            request.headers.setdefault('User-Agent', rand_use)

setting.py 文件dom

BOT_NAME = 'zhihu'

SPIDER_MODULES = ['zhihu.spiders']
NEWSPIDER_MODULE = 'zhihu.spiders'
USER_AGENT_LIST=[  # 能够写多个，测试用，写了一个
    "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36"
]
# Obey robots.txt rules
ROBOTSTXT_OBEY = False
# See also autothrottle settings and docs
DOWNLOAD_DELAY = 2
# Disable cookies (enabled by default)
COOKIES_ENABLED = False
# Override the default request headers:
DEFAULT_REQUEST_HEADERS = {
  'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
  'Accept-Language': 'en',
}
# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html
DOWNLOADER_MIDDLEWARES = {
    'zhihu.middlewares.RandomUserAgentMiddleware': 400,
}
# Configure item pipelines
# See https://doc.scrapy.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
   'zhihu.pipelines.ZhihuPipeline': 300,
}

主要爬取函数,内容说明scrapy

start_requests 用来处理首次爬取请求，做为程序入口
下面的代码主要处理了2种状况，一种是HTML部分，一种是JSON部分
JSON部分使用re模块进行匹配，在经过json模块格式化
extract_first() 获取xpath匹配数组的第一项
dont_filter=False scrapy URL去重

# 起始位置
    def start_requests(self):
        for url in self.start_urls:
            yield scrapy.Request(url.format("zhang-jia-wei"), callback=self.parse)

    def parse(self, response):

        print("正在获取 {} 信息".format(response.url))
        all_data = response.body_as_unicode()

        select = Selector(response)

        # 全部知乎用户都具有的信息
        username = select.xpath("//span[@class='ProfileHeader-name']/text()").extract_first()       # 获取用户昵称
        sex = select.xpath("//div[@class='ProfileHeader-iconWrapper']/svg/@class").extract()
        if len(sex) > 0:
            sex = 1 if str(sex[0]).find("male") else 0
        else:
            sex = -1
        answers = select.xpath("//li[@aria-controls='Profile-answers']/a/span/text()").extract_first()
        asks = select.xpath("//li[@aria-controls='Profile-asks']/a/span/text()").extract_first()
        posts = select.xpath("//li[@aria-controls='Profile-posts']/a/span/text()").extract_first()
        columns = select.xpath("//li[@aria-controls='Profile-columns']/a/span/text()").extract_first()
        pins = select.xpath("//li[@aria-controls='Profile-pins']/a/span/text()").extract_first()
        # 用户有可能设置了隐私，必须登陆以后看到，或者记录cookie！
        follwers = select.xpath("//strong[@class='NumberBoard-itemValue']/@title").extract()



        item = ZhihuItem()
        item["username"] = username
        item["sex"] = sex
        item["answers"] = answers
        item["asks"] = asks
        item["posts"] = posts
        item["columns"] = columns
        item["pins"] = pins
        item["follwering"] = follwers[0] if len(follwers) > 0 else 0
        item["follwers"] = follwers[1] if len(follwers) > 0 else 0

        yield item



        # 获取第一页关注者列表
        pattern = re.compile('<script id=\"js-initialData\" type=\"text/json\">(.*?)<\/script>')
        json_data = pattern.search(all_data).group(1)
        if json_data:
            users = json.loads(json_data)["initialState"]["entities"]["users"]
        for user in users:
            yield scrapy.Request(self.start_urls[0].format(user),callback=self.parse, dont_filter=False)

在获取数据的时候，我绕开了一部分数据，这部分数据能够经过正则表达式去匹配。
ide

数据存储，采用的依旧是mongodb