04_爬取策略&bs4

一. 爬取策略

​ 在爬虫系统中,待抓取URL队列是很重要的一部分。待抓取URL队列中的URL以什么样的顺序排列也是一个很重要的问题,因为这涉及到先抓取哪个页面,后抓取哪个页面。而决定这些URL排列顺序的方法,叫做抓取策略。下面重点介绍几种常见的抓取策略:

  • 深度(递归)优先遍历策略
    深度优先遍历策略是指网络爬虫会从起始页开始,一个链接一个链接跟踪下去,处理完这条线路之后再转入下一个起始页,继续跟踪链接。
import re
import requests

header = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36"}

hrefre = "<a.*href=\"(https?://.*?)\".*>"

def getPage(url):
    '''
    获取html
    :param url:
    :return: html源码
    '''
    html = requests.get(url, headers=header)
    return html.text

def getUrl(url):
    '''
    获取url
    :param url:
    :return: URLList
    '''
    html = getPage(url)
    urllist = re.findall(hrefre, html)
    return urllist

def deepSpider(url, depth):
    '''
    深度爬虫
    :param url:
    :param depth:深度控制
    :return:
    '''
    print("\t\t\t" * depthDict[url], "爬取了第%d级页面:%s" % (depthDict[url], url))

    if depthDict[url] > depth:
        return  # 超出深度则跳出
    sonlist = getUrl(url)
    for i in sonlist:
        if i not in depthDict:
            depthDict[i] = depthDict[url] + 1  # 层级+1
            deepSpider(i, depth)

if __name__ == '__main__':
    depthDict = {}  # 爬虫层级控制
    # 起始url
    startUrl = "https://www.baidu.com/s?ie=utf-8&f=8&rsv_bp=1&tn=baidu&wd=岛国邮箱"
    depthDict[startUrl] = 1
    deepSpider(startUrl, 4)

  • 广度(队列)优先遍历策略
    宽度优先遍历策略的基本思路是,将新下载网页中发现的链接直接**待抓取URL队列的末尾。也就是指网络爬虫会先抓取起始网页中链接的所有网页,然后再选择其中的一个链接网页,继续抓取在此网页中链接的所有网页。还是以上面的图为例:遍历路径:A-B-C-D-E-F-G-H-I
import re
import requests

header = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36"}

hrefre = "<a.*href=\"(https?://.*?)\".*>"

def getUrl(url):
    '''
    获取网页的全部url
    :param url:
    :return: url列表
    '''
    html = getPage(url)
    '''
    <a data-click="{}" href="http://www.baidu.com/" fasdf>...</a>
    '''
    urlre = "<a.*href=\"(https?://.*?)\".*>"
    urllist = re.findall(urlre, html)
    return urllist

def getPage(url):
    '''
    抓取网页html
    :param url:
    :return: HTML源码
    '''
    html = requests.get(url, headers=header).text
    return html

def vastSpider(depth):
    while len(urlList) > 0:
        url = urlList.pop(0)  # 弹出首个url
        print("\t\t\t" * depthDict[url], "抓取了第%d级页面:%s" % (depthDict[url], url))

        if depthDict[url] < depth:
            sonList = getUrl(url)
            for s in sonList:
                if s not in depthDict: # 去重
                    depthDict[s] = depthDict[url] + 1
                    urlList.append(s)

if __name__ == '__main__':
    # 去重
    urlList = []  # url列表

    depthDict = {}
    starUrl = "https://www.baidu.com/s?ie=utf-8&f=8&rsv_bp=1&tn=baidu&wd=岛国邮箱"
    depthDict[starUrl] = 1
    urlList.append(starUrl)
    vastSpider(4)

二. 页面解析和数据提取

一般来讲对我们而言,需要抓取的是某个网站或者某个应用的内容,提取有用的价值。内容一般分为两部分,非结构化的数据 和 结构化的数据。

  • 非结构化数据:先有数据,再有结构,
  • 结构化数据:先有结构、再有数据

不同类型的数据,我们需要采用不同的方式来处理。

  • 非结构化的数据处理
HTML
  • 结构化的数据处理
JSON
XML

Beautiful Soup 4.2.0 文档

https://www.crummy.com/software/BeautifulSoup/bs4/doc/index.zh.html

示例:爬取前程无忧招聘岗位数量

from bs4 import BeautifulSoup
import requests

def download(url):
    headers = {"User-Agent": "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0);"}
    response = requests.get(url, headers=headers)
    html = response.content.decode('gbk')
    
    soup = BeautifulSoup(html, 'lxml')
    # 获取岗位数量的多种查找方式
    # 方式1: 使用find_all
    jobnum = soup.find_all('div', class_='rt')
    print(jobnum[0].text)
    
    # 方式2: 使用select
    jobnum = soup.select('.rt')[0].string
    print(jobnum.strip())  # 去掉首尾空格

    # 方式3:正则匹配re
    # jobnum_re = '<div class="rt">(.*?)</div>'
    # jobnum_comp = re.compile(jobnum_re, re.S)
    # jobnums = jobnum_comp.findall(html)
    # print(jobnums[0])

download(url = "https://search.51job.com/list/000000,000000,0000,00,9,99,python,2,1.html?lang=c&stype=&postchannel=0000&workyear=99&cotype=99&degreefrom=99&jobterm=99&companysize=99&providesalary=99&lonlat=0%2C0&radius=-1&ord_field=0&confirmdate=9&fromType=&dibiaoid=0&address=&line=&specialarea=00&from=&welfare=")

示例:爬取股票基金

import urllib
from urllib import request
from bs4 import BeautifulSoup

stockList = []

def download(url):
    headers = {"User-Agent": "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0);"}
    request = urllib.request.Request(url, headers=headers)  # 请求,修改,模拟http.
    data = urllib.request.urlopen(request).read()  # 打开请求,抓取数据
    
    soup = BeautifulSoup(data, "html5lib", from_encoding="gb2312")
    mytable = soup.select("#datalist")
    for line in mytable[0].find_all("tr"):
        print(line.get_text())  # 提取每一个行业
        print(line.select("td:nth-of-type(3)")[0].text) # 提取具体的某一个

if __name__ == '__main__':
    download("http://quote.stockstar.com/fund/stock_3_1_2.html")

示例:爬取腾讯岗位说明

import urllib
from urllib import request
from bs4 import BeautifulSoup

def download(url):
    headers = {"User-Agent": "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0);"}
    request = urllib.request.Request(url, headers=headers) # 请求,修改,模拟http.
    data = urllib.request.urlopen(request).read() # 打开请求,抓取数据
    
    soup = BeautifulSoup(data, "html5lib")
    print(soup)
    data = soup.find_all("ul", class_="squareli")
    for dataline in data:
        for linedata in dataline.find_all("li"):
            print(linedata.string)
        
    data = soup.select('ul[class="squareli"]')
    for dataline in data:
        for linedata in dataline.select("li"):
            print(linedata.get_text())

download("https://hr.tencent.com/position_detail.php?id=43940&keywords=%E7%88%AC%E8%99%AB&tid=0&lid=0")

示例:获取腾讯岗位列表

import urllib
from urllib import request
from bs4 import BeautifulSoup

def download(url):
    headers = {"User-Agent": "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0);"}
    request = urllib.request.Request(url, headers=headers) # 请求,修改,模拟http.
    data = urllib.request.urlopen(request).read() # 打开请求,抓取数据
    soup = BeautifulSoup(data, "lxml")
    
    data = soup.find_all("table", class_="tablelist")
    for line in data[0].find_all("tr", class_=["even", "odd"]):
        print(line.find_all("td")[0].a["href"])
        for data in line.find_all("td"):
            print(data.string)

download("https://hr.tencent.com/position.php?keywords=python&lid=0&tid=0#a")

存入数据库

import pymysql

## 存入数据库
def save_job(tencent_job_list):

    # 连接数据库
    db = pymysql.connect(host="127.0.0.1", port=3306, user='root', password="root",database='tencent1', charset='utf8')
    # 游标
    cursor = db.cursor()

    # 遍历,插入job
    for job in tencent_job_list:
        sql = 'insert into job(name, address, type, num) VALUES("%s","%s","%s","%s") ' % (job["name"], job["address"], job["type"], job["num"])
        cursor.execute(sql)
        db.commit()

    cursor.close()
    db.close()

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