Python爬虫爬取解析数据代码操作示例

作者:袖梨 2020-03-27

本篇文章小编给大家分享一下Python爬虫爬取解析数据代码操作示例,通过代码示例分析了操作技巧以及注意事项,小编觉得挺不错的,现在分享给大家供大家参考,有需要的小伙伴们可以来看看。

获取书籍相关信息

面向对象思想

利用不同解析方式和存储方式

引用相关库

import requests
import re
import csv
import pymysql
from bs4 import BeautifulSoup
from lxml import etree
import lxml
from lxml import html

类代码实现部分

class DDSpider(object):
  #对象属性 参数 关键字 页数
  def __init__(self,key='python',page=1):
    self.url = 'http://search.dangdang.com/?key='+key+'&act=input&page_index={}'
    self.page = page
    self.headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36'}

    
  #私有对象方法
  def __my_url(self):
    my_url = []
    if self.page < 1:
      my_page = 2
    else:
      my_page = self.page+1
    #循环遍历每一页
    for i in range(1,my_page):
      my_url.append(self.url.format(i))
    return my_url
  
  #私有对象方法 请求数据
  def __my_request(self,url,parser_type):
    #循环遍历每一页
    response = requests.get(url=url,headers=self.headers)
    if response.status_code == 200:
      return self.__my_parser(response.text,parser_type)
    else:
      return None
    
  #私有对象方法 解析数据 1 利用正则 2 bs4 3 xpath
  def __my_parser(self,html,my_type=1):
    if my_type == 1:
      pattern = re.compile('(.*?)

.*?(.*?).*?.*?',re.S) result = re.findall(pattern,html) elif my_type == 2: soup = BeautifulSoup(html,'lxml') result = [] title_url = soup.find_all('a',attrs={'name':'itemlist-title'}) for i in range(0,len(title_url)): title = soup.find_all('a',attrs={'name':'itemlist-title'})[i].attrs['title'] url = soup.find_all('a',attrs={'name':'itemlist-title'})[i].attrs['href'] price = soup.find_all('span',attrs={'class':'search_now_price'})[i].get_text() author = soup.find_all('a',attrs={'name':'itemlist-author'})[i].attrs['title'] desc = soup.find_all('p',attrs={'class':'detail'})[i].get_text() my_tuple = (title,url,desc,price,author) result.append(my_tuple) else: html = etree.HTML(html) li_all = html.xpath('//div[@id="search_nature_rg"]/ul/li') result = [] for i in range(len(li_all)): title = html.xpath('//div[@id="search_nature_rg"]/ul/li[{}]/p[@class="name"]/a/@title'.format(i+1)) url = html.xpath('//div[@id="search_nature_rg"]/ul/li[{}]/p[@class="name"]/a/@href'.format(i+1)) price = html.xpath('//div[@id="search_nature_rg"]/ul/li[{}]//span[@class="search_now_price"]/text()'.format(i+1)) author_num = html.xpath('//div[@id="search_nature_rg"]/ul/li[{}]/p[@class="search_book_author"]/span[1]/a'.format(i+1)) if len(author_num) != 0: #有作者 a标签 author = html.xpath('//div[@id="search_nature_rg"]/ul/li[{}]/p[@class="search_book_author"]/span[1]/a[1]/@title'.format(i+1)) else: #没有作者 a标签 author = html.xpath('//div[@id="search_nature_rg"]/ul/li[{}]/p[@class="search_book_author"]/span[1]/text()'.format(i+1)) desc = html.xpath('//div[@id="search_nature_rg"]/ul/li[{}]/p[@class="detail"]/text()'.format(i+1)) my_tuple = (" ".join(title)," ".join(url)," ".join(desc)," ".join(price)," ".join(author)) result.append(my_tuple) return result #私有对象方法 存储数据 1 txt 2 csv 3 mysql def __my_save(self,data,save_type=1): #循环遍历 for value in data: if save_type == 1: with open('ddw.txt','a+',encoding="utf-8") as f: f.write('【名称】:{}【作者】:{}【价格】:{}【简介】:{}【链接】:{}'.format(value[0],value[4],value[3],value[2],value[1])) elif save_type == 2: with open('ddw.csv','a+',newline='',encoding='utf-8-sig') as f: writer = csv.writer(f) #转化为列表 存储 writer.writerow(list(value)) else: conn = pymysql.connect(host='127.0.0.1',user='root',passwd='',db='',port=3306,charset='utf8') cursor = conn.cursor() sql = '' cursor.execute(sql) conn.commit() cursor.close() conn.close() #公有对象方法 执行所有爬虫操作 def my_run(self,parser_type=1,save_type=1): my_url = self.__my_url() for value in my_url: result = self.__my_request(value,parser_type) self.__my_save(result,save_type)

调用爬虫类实现数据获取

if __name__ == '__main__':
  #实例化创建对象
  dd = DDSpider('python',0)
  #参数 解析方式 my_run(parser_type,save_type)
  # parser_type 1 利用正则 2 bs4 3 xpath 
  #存储方式 save_type 1 txt 2 csv 3 mysql
  dd.my_run(2,1)

==总结一下: ==

1. 总体感觉正则表达式更简便一些 , 代码也会更简便 , 但是正则部分相对复杂和困难

2. bs4和xpath 需要对html代码有一定了解 , 取每条数据多个值时相对较繁琐。

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