Python爬虫：第三章 数据解析 xpath解析（12）

第三章 数据解析

• xpath 解析

xpath 解析

xpath 解析基础

#!/usr/bin/env python 
# -*- coding:utf-8 -*-
from lxml import etree
if __name__ == "__main__":
    #实例化好了一个etree对象，且将被解析的源码加载到了该对象中
    tree = etree.parse('test.html')
    r = tree.xpath('/html/body/div')
    r = tree.xpath('/html//div')
    r = tree.xpath('//div')
    r = tree.xpath('//div[@class="song"]')
    r = tree.xpath('//div[@class="tang"]//li[5]/a/text()')[0]
    r = tree.xpath('//li[7]//text()')
    r = tree.xpath('//div[@class="tang"]//text()')
    r = tree.xpath('//div[@class="song"]/img/@src')

    print(r)

example1 爬取58二手房中的房源信息

#!/usr/bin/env python 
# -*- coding:utf-8 -*-
import requests
from lxml import etree
#需求：爬取58二手房中的房源信息
if __name__ == "__main__":
    headers = {
        'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36'
    }
    #爬取到页面源码数据
    url = 'https://bj.58.com/ershoufang/'
    page_text = requests.get(url=url,headers=headers).text

    #数据解析
    tree = etree.HTML(page_text)
    #存储的就是li标签对象
    li_list = tree.xpath('//section[@class="list"]/div[@class="property"]')
    fp = open('58.txt','w',encoding='utf-8')
    for li in li_list:
        #局部解析
        title = li.xpath('./a//div[@class="property-content-title"]/h3/text()')[0]
        print(title)
        fp.write(title+'\n')

example2 解析下载图片数据

#!/usr/bin/env python 
# -*- coding:utf-8 -*-
#需求：解析下载图片数据 http://pic.netbian.com/4kmeinv/
import requests
from lxml import etree
import os
if __name__ == "__main__":
    url = 'http://pic.netbian.com/4kmeinv/'
    headers = {
        'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36'
    }
    response = requests.get(url=url,headers=headers)
    #手动设定响应数据的编码格式
    # response.encoding = 'utf-8'
    page_text = response.text

    #数据解析：src的属性值  alt属性
    tree = etree.HTML(page_text)
    li_list = tree.xpath('//div[@class="slist"]/ul/li')


    #创建一个文件夹
    if not os.path.exists('./picLibs'):
        os.mkdir('./picLibs')

    for li in li_list:
        img_src = 'http://pic.netbian.com'+li.xpath('./a/img/@src')[0]
        img_name = li.xpath('./a/img/@alt')[0]+'.jpg'
        #通用处理中文乱码的解决方案
        img_name = img_name.encode('iso-8859-1').decode('gbk')

        # print(img_name,img_src)
        #请求图片进行持久化存储
        img_data = requests.get(url=img_src,headers=headers).content
        img_path = 'picLibs/'+img_name
        with open(img_path,'wb') as fp:
            fp.write(img_data)
            print(img_name,'下载成功！！！')

example3 全国城市名称爬取

#!/usr/bin/env python 
# -*- coding:utf-8 -*-
import requests
from lxml import etree
#项目需求：解析出所有城市名称https://www.aqistudy.cn/historydata/
if __name__ == "__main__":
    # headers = {
    #     'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36'
    # }
    # url = 'https://www.aqistudy.cn/historydata/'
    # page_text = requests.get(url=url,headers=headers).text
    #
    # tree = etree.HTML(page_text)
    # host_li_list = tree.xpath('//div[@class="bottom"]/ul/li')
    # all_city_names = []
    # #解析到了热门城市的城市名称
    # for li in host_li_list:
    #     hot_city_name = li.xpath('./a/text()')[0]
    #     all_city_names.append(hot_city_name)
    #
    # #解析的是全部城市的名称
    # city_names_list = tree.xpath('//div[@class="bottom"]/ul/div[2]/li')
    # for li in city_names_list:
    #     city_name = li.xpath('./a/text()')[0]
    #     all_city_names.append(city_name)
    #
    # print(all_city_names,len(all_city_names))

    headers = {
        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36'
    }
    url = 'https://www.aqistudy.cn/historydata/'
    page_text = requests.get(url=url, headers=headers).text

    tree = etree.HTML(page_text)
    #解析到热门城市和所有城市对应的a标签
    # //div[@class="bottom"]/ul/li/          热门城市a标签的层级关系
    # //div[@class="bottom"]/ul/div[2]/li/a  全部城市a标签的层级关系
    a_list = tree.xpath('//div[@class="bottom"]/ul/li/a | //div[@class="bottom"]/ul/div[2]/li/a')
    all_city_names = []
    for a in a_list:
        city_name = a.xpath('./text()')[0]
        all_city_names.append(city_name)
    print(all_city_names,len(all_city_names))