Python简单爬虫

轻量级爬虫

  • 不需要登录
  • 静态网页 -- 数据不是异步加载

爬虫:一段自动抓取互联网信息的程序

URL管理器

管理对象

  • 将要抓取的url
  • 已经抓取过的url

作用

  • 防止重复抓取
  • 防止循环抓取

实现方式:

1、内存

python内存

待爬取URL集合:set()

已爬取URL集合:set()

2、关系型数据库

MySQL

数据表urls(url, is_crawled)

3、缓存数据库

redis

待爬取URL集合:set()

已爬取URL集合:set()

网页下载器

将获取到的网页下载到本地进行分析的工具

类型

1、urllib2

Python 官方基础 展模块

2、requests

第三方包,更强大

urllib2下载网页

1、方法一:最简单的方法

import urllib2

# 直接请求
response = urllib2.urlopen('http://www.baidu.com')

# 获取状态码,如果是200表示获取成功
print response.getcode()

# 读取内容
cont = response.read()

2、方法二:添加data、http header

import urllib2

# 创建Request对象
request urllib2.Request(url)

# 添加数据
request.add_data('a', '1')

# 添加http的header, 模拟Mozilla浏览器
response.add_header('User-Agent', 'Mozilla/5.0')

3、方法三:添加特殊情景的处理器

  • HTTPCookieProcessor:对于需要用户登录的网页

  • ProxyHandler:对于需要代理才能访问的网页

  • HTTPSHandler:对于https协议的网页

  • HTTPRedirectHandler:对于设置了自动跳转的网页

import urllib2, cookielib

# 创建cookie容器
cj = cookielib.CookieJar()

# 创建1个opener
opener = urllib2.build_opener(urllib2.HTTPCookieProcessor(cj))

# 给urllib2安装opener
urllib2.install_opener(opener)

# 使用带有cookie的urllib2访问网页
response = urllib2.urlopen("http://www.baidu.com")

实例代码

# coding:utf8
import urllib2, cookielib

url = "http://www.baidu.com"

print("一种方法:")
response1 = urllib2.urlopen(url)
print(response1.getcode())
print(len(response1.read()))

print('第二种方法:')
request = urllib2.Request(url)
request.add_header("user-agent", 'Mozilla/5.0')
response1 = urllib2.urlopen(url)
print(response1.getcode())
print(len(response1.read()))

print('第三种方法:')
cj = cookielib.CookieJar()
opener = urllib2.build_opener(urllib2.HTTPCookieProcessor(cj))
urllib2.install_opener(opener)
response3 = urllib2.urlopen(request)
print(response3.getcode())
print(cj)
print(response3.read())

注:以上是Python2的写法,以下是Python3的写法

# coding:utf8
import urllib.request
import http.cookiejar

url = "http://www.baidu.com"

print("一种方法:")
response1 = urllib.request.urlopen(url)
print(response1.getcode())
print(len(response1.read()))

print('第二种方法:')
request = urllib.request.Request(url)
request.add_header("user-agent", 'Mozilla/5.0')
response1 = urllib.request.urlopen(url)
print(response1.getcode())
print(len(response1.read()))

print('第三种方法:')
cj = http.cookiejar.CookieJar()
opener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor(cj))
urllib.request.install_opener(opener)
response3 = urllib.request.urlopen(request)
print(response3.getcode())
print(cj)
print(response3.read())

网页解析器

解析网页,从网页中提取有价值数据的工具

网页解析器(BeautifulSoup)

类型

1、正则表达式(模糊匹配)

2、html.parser(结构化解析)

3、Beautiful Soup(结构化解析)

4、lxml(结构化解析)

结构化解析-DOM(Document Object Model)树

安装并使用 Beautiful Soup4

1、安装

pip install beautifulsoup4

2、使用

  • 创建BeautifulSoup对象
  • 搜索节点(按节点名称、属性、文字)
    • find_all
    • find
  • 访问节点
    • 名称
    • 属性
    • 文字

(1)创建Beautiful Soup对象

from bs4 import BeautifulSoup

# 根据HTML网页字符串创建BeautifulSoup对象
soup = BeautifulSoup(
    html_doc,               # HTML文档字符串
    'html.parser',          # HTML解析器
    from_encoding='utf8'    # HTML文档的编码
)

(2)搜索节点(find_all,find)

# 方法:find_all(name, attrs, string)
 
# 查找所有标签为a的节点
soup.find_all('a')
 
# 查找所有标签为a,链接符合/view/123.html形式的节点
soup.find_all('a', href='/view/123.htm')
soup.find_all('a', href=re.compile(r'/view/\d+\.htm'))

# 查找所有标签为div,class为abs,文字为Python的节点
soup.find_all('div', class_='abc', string='Python')
  • 用class_作为查询类属性的变量名,因为class本身是python的关键字,所以需要加一个下划线来区别

(3)访问节点信息

# 得到节点:<a href="1.html">Python</a>

# 获取查找到的节点的标签名称
node.name

# 获取查找到的a节点的href属性
node['href']

# 获取查找到的a节点的链接文字
node.get_text()

3、实例

# coding:utf8
from bs4 import BeautifulSoup, re
html_doc = """
<html><head><title>The Dormouse's story</title></head>
<body>
<p class="title"><b>The Dormouse's story</b></p>

<p class="story">Once upon a time there were three little sisters; and their names were
<a href="http://example.com/elsie" class="sister" id="link1">Elsie</a>,
<a href="http://example.com/lacie" class="sister" id="link2">Lacie</a> and
<a href="http://example.com/tillie" class="sister" id="link3">Tillie</a>;
and they lived at the bottom of a well.</p>

<p class="story">...</p>
"""

soup = BeautifulSoup(html_doc, 'html.parser')
print('获取所有的链接:')
links = soup.find_all('a')
for link in links:
    print(link.name, link['href'], link.get_text())

print('获取lacie的链接:')
link_node = soup.find('a', href='http://example.com/lacie')
print(link_node.name, link_node['href'], link_node.get_text())


print('正则匹配:')
link_node = soup.find('a', href=re.compile(r"ill"))
print(link_node.name, link_node['href'], link_node.get_text())

print('获取p段落文字:')
p_node = soup.find('p', class_='title')
print(p_node.name, p_node.get_text())

执行后效果:

开发爬虫

分析目标

  • URL格式
  • 数据格式
  • 网页编码

1、目标: 百度百科Python词条相关词条网页 -- 标题和简介

2、入口页

https://baike.baidu.com/item/Python/407313

3、URL格式:

  • 词条页面URL: /item/****

4、数据格式:

  • 标题:
<dd class="lemmaWgt-lemmaTitle-title"><h1>...</h1></dd>
  • 简介:
<div class="lemma-summary" label-module="lemmaSummary">...</div

5、页面编码:UTF-8

项目目录结构

调度主程序

# coding:utf8
from baike_spider import url_manager, html_downloader, html_parser, html_outputer


class SpiderMain(object):
    def __init__(self):
        # url管理器
        self.urls = url_manager.UrlManager()
        # 下载器
        self.downloader = html_downloader.HtmlDownloader()
        # 解析器
        self.parser = html_parser.HtmlParser()
        # 输出器
        self.outputer = html_outputer.HtmlOutputer()

    # 爬虫的调度程序
    def craw(self, root_url):
        count = 1
        self.urls.add_new_url(root_url)
        while self.urls.has_new_url():
            try:
                if count == 1000:
                    break

                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)

                count = count + 1
            except:
                print('craw failed')

        self.outputer.output_html()


if __name__ == "__main__":
    root_url = "https://baike.baidu.com/item/Python/407313"
    obj_spider = SpiderMain()
    obj_spider.craw(root_url)

URL管理器

# coding:utf8
class UrlManager(object):
    def __init__(self):
        self.new_urls = set()
        self.old_urls = set()

    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)

    def add_new_urls(self, urls):
        if urls is None or len(urls) == 0:
            return
        for url in urls:
            self.add_new_url(url)

    def has_new_url(self):
        return len(self.new_urls) != 0

    def get_new_url(self):
        new_url = self.new_urls.pop()
        self.old_urls.add(new_url)

        return new_url

网页下载器

# coding:utf8

import urllib.request


class HtmlDownloader(object):

    def download(self, url):
        if url is None:
            return None

        # request = urllib.request.Request(url)
        # request.add_header("user-agent", 'Mozilla/5.0')
        response = urllib.request.urlopen(url)
        if response.getcode() != 200:
            return None

        return response.read()

网页解析器

# coding:utf8

from bs4 import BeautifulSoup, re
from urllib.parse import urljoin


class HtmlParser(object):

    def _get_new_urls(self, page_url, soup):
        new_urls = set()

        links = soup.find_all('a', href=re.compile(r"/item/"))
        for link in links:
            new_url = link['href']
            new_full_url = urljoin(page_url, new_url)
            new_urls.add(new_full_url)

        return new_urls

    def _get_new_data(self, page_url, soup):
        res_data = {}

        res_data['url'] = page_url

        title_node = soup.find('dd', class_='lemmaWgt-lemmaTitle-title').find('h1')
        res_data['title'] = title_node.get_text()

        summary_node = soup.find('div', class_='lemma-summary')
        res_data['summary'] = summary_node.get_text()

        return res_data

    def parse(self, page_url, html_cont):
        if page_url is None or html_cont is None:
            return

        soup = BeautifulSoup(html_cont, 'html.parser')
        new_urls = self._get_new_urls(page_url, soup)
        new_data = self._get_new_data(page_url, soup)

        return new_urls, new_data

网页输出器

# coding:utf8
class HtmlOutputer(object):
    def __init__(self):
        self.datas = []

    def collect_data(self, data):
        if data is None:
            return
        self.datas.append(data)

    def output_html(self):
        fout = open('output.html', 'w')

        fout.write('<html>')
        fout.write('<body>')
        fout.write('<table>')

        for data in self.datas:
            fout.write('<tr>')
            fout.write('<td>%s</td>' % data['url'])
            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()

高级爬虫:

  • 登录
  • 验证码
  • Ajax
  • 服务器防爬虫
  • 多线程
  • 分布式

学习资料:慕课网-Python开发简单爬虫

posted @ 2019-07-09 10:24  程序小工  阅读(532)  评论(0编辑  收藏  举报