一个完整的爬虫需要有以下几个部分组成:
一、网页下载器,既然是爬取网上的,就需要有一个抓取一个个网页的的工具,这就是网页下载器,有很多Python包都提供了相应功能,比如下面实例中的urllib2工具包。
二、网页解析器,当我们爬去下来一个个页面的时候,其实就是一些HTML代码包裹起来一些数据,比如文字,图片等,要想获取这些数据,就需要解析这些网页啦,这就是网页解析器的作用。另外,这也是爬虫最难的一部分,因为需要解析各种不同情况的标签,还要大量用到正则表达式,但是也有一些包比如Beautiful Soup帮助我们简化工作,想进一步学习可以参考:
https://www.crummy.com/software/BeautifulSoup/bs4/doc/index.zh.html
三、URL管理器,由于我们的爬虫一次需要爬取很多页面,这就需要保存起来已经爬到的页面,以供网页解析器来解读这些页面,也可以防止重复抓取页面和剔除无效的URL链接。
四、爬虫调度器,顾名思义就是管理整个爬虫,使我们的爬虫按流程工作。
#url管理器
# coding:utf8
class UrlManager(object):
def __init__(self):
# 初始化待爬取url集合和已爬取url集合
self.new_urls = set()
self.old_urls = set()
# 添加一个新的url到new_urls
def add_new_url(self, url):
if url is None:
return
if url not in self.new_urls and url not in self.old_urls:
self.new_urls.add(url)
# 获取一个待爬取的url,并将此url添加到old_urls
def get_new_url(self):
new_url = self.new_urls.pop()
self.old_urls.add(new_url)
return new_url
# 判断是否还有待爬取的url
def has_new_url(self):
return len(self.new_urls) != 0
# 添加多个url到new_urls
def add_new_urls(self, urls):
if urls is None or len(urls) == 0:
return
for url in urls:
self.add_new_url(url)
#网页下载器
# coding:utf8
import urllib2
class HtmlDownloader(object):
# 使用urllib2最简单的方法下载url页面内容
def download(self, url):
if url is None:
return None
resp = urllib2.urlopen(url)
if resp.getcode() != 200:
return None
return resp.read()
#网页解析器
# coding:utf8
import re
import urlparse
from bs4 import BeautifulSoup
class HtmlParser(object):
# 得到页面相关的url
def _get_new_urls(self, page_url, soup):
new_urls = set()
# /view/123.htm
links = soup.find_all('a', href=re.compile(r'/view/\d+\.htm'))
for link in links:
new_url = link['href']
# 将/view/123.htm补充完整:http://baike.baidu.com/view/123.htm
new_full_url = urlparse.urljoin(page_url, new_url)
# 将解析到的unicode编码的网址转化为utf-8格式
new_urls.add(new_full_url.encode('utf-8'))
return new_urls
# 得到页面标题和简介
def _get_new_data(self, page_url, soup):
res_data = {}
# url
res_data['url'] = page_url
# 得到标题节点
# <dd class="lemmaWgt-lemmaTitle-title"> <h1 >Python</h1>
title_node = soup.find('dd', class_='lemmaWgt-lemmaTitle-title').find('h1')
res_data['title'] = title_node.get_text()
# 得到简介节点
# <div class="lemma-summary" label-module="lemmaSummary">
summary_node = soup.find('div', class_='lemma-summary')
res_data['summary'] = summary_node.get_text()
return res_data
# 对下载页面内容进行解析
def parse(self, page_url, html_cont):
if page_url is None or html_cont is None:
return None
soup = BeautifulSoup(html_cont, 'html.parser', from_encoding='utf-8')
new_urls = self._get_new_urls(page_url, soup)
new_data = self._get_new_data(page_url, soup)
return new_urls, new_data
#数据输出器
# coding:utf8
class HtmlOutputer(object):
def __init__(self):
self.datas = []
# 收集数据
def collect_data(self, data):
if data is None:
return None
self.datas.append(data)
# 将收集到的数据生成一个HTML页面输出
def output_html(self):
fout = open('output.html', 'w')
fout.write('<html>')
fout.write('<head>')
fout.write('<meta charset="UTF-8"></meta>')
fout.write('</head>')
fout.write('<body>')
fout.write('<table>')
for data in self.datas:
fout.write('<tr>')
fout.write('<td>%s</td>' % data['url'].encode('utf-8'))
fout.write('<td>%s</td>' % data['title'].encode('utf-8'))
fout.write('<td>%s</td>' % data['summary'].encode('utf-8'))
fout.write('</tr>')
fout.write('</table>')
fout.write('</body>')
fout.write('</html>')
fout.close()
#爬虫调度器
# coding:utf8
from baike1 import url_manager, html_downloader, html_parser, html_outputer
class SpiderMain(object):
def __init__(self):
self.urls = url_manager.UrlManager()
self.downloader = html_downloader.HtmlDownloader()
self.parser = html_parser.HtmlParser()
self.outputer = html_outputer.HtmlOutputer()
def craw(self, url):
count = 1
self.urls.add_new_url(url)
while self.urls.has_new_url():
try:
new_url = self.urls.get_new_url()
print 'craw %d:%s' % (count, new_url)
html_cont = self.downloader.download(new_url)
new_urls, new_data = self.parser.parse(new_url, html_cont)
self.urls.add_new_urls(new_urls)
self.outputer.collect_data(new_data)
if count == 100:
break
count += 1
except:
print 'craw failed'
self.outputer.output_html()
if __name__ == "__main__":
root_url = 'http://baike.baidu.com/view/21087.htm'
obj_spider = SpiderMain()
obj_spider.craw(root_url)