爬虫基础_03——xpath

作者: 王小鱼鱻 | 来源:发表于2017-06-11 18:22 被阅读139次

爬虫基础_03——xpath
xpath 资料
[爬虫基础] XPath 入门
scrapy实战笔记
爬虫解析库XPath使用
2019-05-29 图书比价工具
数据分析课程笔记 - 08 - 爬虫（二）
Python爬虫(十三)_案例：使用XPath的爬虫
XPath轴方法 - 捕捉
标签内容但不捕获
下的<
Python-爬虫基础-Xpath-爬取百度搜索列表（获取标题和

今天是利用xpath爬取网址：简书首页
包括：标题，作者，发表时间，内容，阅读量，评论数，点赞数，打赏数，所投专题
主要思想：利用xpath获取网页中的数据，然后存到本地的csv
下面了解一下xpath的用法
首先必须要导入 lxml 库
Python爬虫利器三之Xpath语法与lxml库的用法

1、首先是爬的第一页的数据
运行代码：

#coding: utf-8
import requests
from lxml import etree
import csv


user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36"
header = {'User-Agent': user_agent}
html = requests.get('http://www.jianshu.com/', headers = header).content

selector = etree.HTML(html)
infos = selector.xpath('//div[@id="list-container"]/ul/li/div')
a = []
# 第一页数据的匹配
for info in infos:
    titles = info.xpath('a/text()')[0]
    authors = info.xpath('div[1]/div/a/text()')[0]
    times = info.xpath('div[1]/div/span/@data-shared-at')[0]
    contents = info.xpath('p/text()')[0].strip()
    try:
        read_counts = info.xpath('div[2]/a[2]/text()')[1].strip()
    except:
        read_counts = '0'
    try:
        comment_counts = info.xpath('div[2]/a[3]/text()')[1].strip()
    except:
        comment_counts = '0'
    try:
        vote_counts = info.xpath('//div/div[2]/span[1]/text()')[0].strip()
    except:
        vote_counts = '0'
    try:
        reward_counts = info.xpath('div[2]/span[2]/text()')[0]
    except:
        reward_counts = '0'
    try:
        subjects = info.xpath('div[2]/a[1]/text()')[0]
    except:
        subjects = '暂未收录专题'
    #print(titles, authors, times, contents, read_counts, comment_counts, vote_counts, reward_counts, subjects)

    data = {
        '文章标题': titles,
        '作者': authors,
        '发表时间': times,
        '内容': contents,
        '阅读量': read_counts,
        '评论数': comment_counts,
        '点赞数': vote_counts,
        '打赏数': reward_counts,
        '主题': subjects,

    }
    a.append(data)
#print(a)

#把爬到的数据存储到csv
csv_name = ['文章标题', '作者', '发表时间', '内容', '阅读量', '评论数', '点赞数', '打赏数', '主题']
with open('jianshu_xpath.csv', 'w', newline = '',encoding='utf-8')as csvfile:
    write = csv.DictWriter(csvfile, fieldnames = csv_name)
    write.writeheader()
    write.writerows(a)
    csvfile.close()

运行结果：

第一页的文章信息.png

第一页比较容易，主要是每个数据爬取路径的选取，还有循环点的选取；
2.爬取简书首页前15页的数据
a、首先要分析一下每页的加载方式，通过点击更多，可以发现url并没有变化，所以是异步加载，下面要抓包分析一下后面每页请求的url有什么共同点。

Paste_Image.png

上面每页的id都可以在上一页找到，而且是会累加的，

Paste_Image.png

Paste_Image.png
具体的分析可以参看liang的文章http://www.jianshu.com/p/9afef50a8cc7，写的很详细，就不多说了；
运行代码：

#coding: utf-8
import requests
from lxml import etree
import csv


user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36"
header = {'User-Agent': user_agent,
          'cookie': 'remember_user_token=W1szNjE3MDgyXSwiJDJhJDEwJDMuQTVNeHVYTkUubFQvc1ZPM0V5UGUiLCIxNDk3MTcyNDA2Ljk2ODQ2NjMiXQ%3D%3D--56522c2190961ce284b1fe108b267ae0cd5bf32a; _session_id=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%3D%3D--6fb5c178053ee287201628ee5d7b2b61c170e994'}

a = []
params = []
#获取每一页的url
for p in range(1,16):
    url_data = '&'.join(params)
    url = 'http://www.jianshu.com/?' + url_data + '&page={}'.format(p)
    #获取每页的数据
    html = requests.get(url, headers = header).text
    selector = etree.HTML(html)
    li_pages = selector.xpath('//div[@id="list-container"]/ul/li')
    for li_page in li_pages:
        li_page = 'seen_snote_ids[]=' + li_page.xpath('@data-note-id')[0]
        params.append(li_page)
    #print(len(params))
    infos = selector.xpath('//div[@id="list-container"]/ul/li/div')
    for info in infos:
        titles = info.xpath('a/text()')[0]
        authors = info.xpath('div[1]/div/a/text()')[0]
        times = info.xpath('div[1]/div/span/@data-shared-at')[0]
        contents = info.xpath('p/text()')[0].strip()
        try:
            read_counts = info.xpath('div[2]/a[2]/text()')[1].strip()
        except:
            read_counts = '0'
        try:
            comment_counts = info.xpath('div[2]/a[3]/text()')[1].strip()
        except:
            comment_counts = '0'
        try:
            vote_counts = info.xpath('//div/div[2]/span[1]/text()')[0].strip()
        except:
            vote_counts = '0'
        try:
                reward_counts = info.xpath('div[2]/span[2]/text()')[0]
        except:
                reward_counts = '0'
        try:
            subjects = info.xpath('div[2]/a[@class="collection-tag"]/text()')[0]
        except:
            subjects = '暂未收录专题'

        #print(titles, authors, times, contents, read_counts, comment_counts, vote_counts, reward_counts, subjects)
        data = {
            '文章标题': titles,
            '作者': authors,
            '发表时间': times,
            '内容': contents,
            '阅读量': read_counts,
            '评论数': comment_counts,
            '点赞数': vote_counts,
            '打赏数': reward_counts,
            '主题': subjects,
            }
        a.append(data)

        #存储数据
        csv_name = ['文章标题', '作者', '发表时间', '内容', '阅读量', '评论数', '点赞数', '打赏数', '主题']
        with open('jianshu_xpath2.csv', 'w', newline='', encoding='utf-8')as csvfile:
            write = csv.DictWriter(csvfile, fieldnames=csv_name)
            write.writeheader()
            write.writerows(a)

运行结果：

简书首页所有的文章信息.png

注意这里取cookie的时候一定要登录后再取cookie，否则只能爬到重复第一页的数据；
最后再把整个代码整理封装一下，稍微好看一点：

#coding: utf-8
import requests
from lxml import etree
import csv

class Jianshu():

    user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36"
    header = {'User-Agent': user_agent,
              'cookie': 'remember_user_token=W1szNjE3MDgyXSwiJDJhJDEwJDMuQTVNeHVYTkUubFQvc1ZPM0V5UGUiLCIxNDk3MTcyNDA2Ljk2ODQ2NjMiXQ%3D%3D--56522c2190961ce284b1fe108b267ae0cd5bf32a; _session_id=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%3D%3D--6fb5c178053ee287201628ee5d7b2b61c170e994'}

    a = []
    params = []
    def __init__(self):
        pass
    #获取每一页的url
    def total_page(self):
        for p in range(1,16):
            url_data = '&'.join(self.params)
            url = 'http://www.jianshu.com/?' + url_data + '&page={}'.format(p)
            self.get_data(url)
    #获取每页的数据
    def get_data(self, url):
        html = requests.get(url, headers = self.header).text
        selector = etree.HTML(html)
        li_pages = selector.xpath('//*[@id="list-container"]/ul/li')
            #print(li_pages)
        for info in li_pages:
            info = 'seen_snote_ids%5B%5D=' + info.xpath('@data-note-id')[0]
            self.params.append(info)
        infos = selector.xpath('//div[@id="list-container"]/ul/li/div')
        for info in infos:
            titles = info.xpath('a/text()')[0]
            authors = info.xpath('div[1]/div/a/text()')[0]
            times = info.xpath('div[1]/div/span/@data-shared-at')[0]
            contents = info.xpath('p/text()')[0].strip()
            try:
                read_counts = info.xpath('div[2]/a[2]/text()')[1].strip()
            except:
                read_counts = '0'
            try:
                comment_counts = info.xpath('div[2]/a[3]/text()')[1].strip()
            except:
                comment_counts = '0'
            try:
                vote_counts = info.xpath('//div/div[2]/span[1]/text()')[0].strip()
            except:
                vote_counts = '0'
            try:
                reward_counts = info.xpath('div[2]/span[2]/text()')[0]
            except:
                reward_counts = '0'
            try:
                subjects = info.xpath('div[2]/a[@class="collection-tag"]/text()')[0]
            except:
                subjects = '暂未收录专题'

            #print(titles, authors, times, contents, read_counts, comment_counts, vote_counts, reward_counts, subjects)
            data = {
                '文章标题': titles,
                '作者': authors,
                '发表时间': times,
                '内容': contents,
                '阅读量': read_counts,
                '评论数': comment_counts,
                '点赞数': vote_counts,
                '打赏数': reward_counts,
                '主题': subjects,
                }
            self.a.append(data)
            #print(self.a)
            #存储数据
            csv_name = ['文章标题', '作者', '发表时间', '内容', '阅读量', '评论数', '点赞数', '打赏数', '主题']
            with open('jianshu_xpath2.csv', 'w', newline='', encoding='utf-8')as csvfile:
                write = csv.DictWriter(csvfile, fieldnames=csv_name)
                write.writeheader()
                write.writerows(self.a)

if __name__ == '__main__':
    jian = Jianshu()
    jian.total_page()

小结：
1、这里用xpath爬取网页的内容，是不是很方便？
虽然用正则、BeautifulSoup和Xpath都可以获取网页的内容，但是要学会灵活应用，有时遇到某一种方法获取不到就要用另外的方法（比如正则，只要你的正则表达式没写错，基本都是可以获取网页数据）
2、这里爬取多页是通过自己手动分析网页加载方式去构造每页的url，然后爬取全部的数据；对于这种异步加载的网页，后面还会介绍其他的方法；

爬虫基础_03——xpath
今天是利用xpath爬取网址：简书首页包括：标题，作者，发表时间，内容，阅读量，评论数，点赞数，打赏数，所投专题...
xpath 资料
xpath路径表达式笔记学爬虫利器XPath,看这一篇就够了有感4月份，看过的python基础和爬虫相关的知识...
[爬虫基础] XPath 入门
XPath是什么？XPath 的全称为 XML Path LanguageXPath使用路径形式的格式来标识XML...
scrapy实战笔记
一，基础 scrapy优势：异步IO，性能强；内置功能丰富（css,xpath）；易扩展爬虫简要步骤：URL分析，...
爬虫解析库XPath使用
爬虫解析库XPath使用 1.XPath简介 XPath 是一门在 XML 文档中查找信息的语言。XPath 用于...
2019-05-29 图书比价工具
基础知识：1.python操作json数据2.Xpath 知识点讲解3.爬虫基础知识4.requests 库的使用...
数据分析课程笔记 - 08 - 爬虫（二）
大家好呀~这节课我们继续学习爬虫的基础知识。上节课我们学习了resquest、response 和 Xpath 的...
Python爬虫(十三)_案例：使用XPath的爬虫
本篇是使用XPath的案例，更多内容请参考:Python学习指南案例：使用XPath的爬虫现在我们用XPath...
XPath轴方法 - 捕捉
标签内容但不捕获
下的<
XPath轴(XPath Axes)可定义某个相对于当前节点的节点集： 1. 爬虫xpath遇到的问题：抓取
Python-爬虫基础-Xpath-爬取百度搜索列表（获取标题和
接着之前的MonkeyLei：Python-爬虫基础-Xpath-爬取百度风云榜旗下热点继续搞。也是项目需要（需...