web爬虫，BeautifulSoup

时间  2019-11-12
标签 web 爬虫 beautifulsoup 栏目 HTML 繁體版
原文   原文链接

BeautifulSoup

该模块用于接收一个HTML或XML字符串，而后将其进行格式化，以后遍可使用他提供的方法进行快速查找指定元素，从而使得在HTML或XML中查找指定元素变得简单。html

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from  bs4  import  BeautifulSoup
 
html_doc  =  """
<html><head><title>The Dormouse's story</title></head>
<body>
asdf
     <div class="title">
         <b>The Dormouse's story总共</b>
         <h1>f</h1>
     </div>
<div class="story">Once upon a time there were three little sisters; and their names were
     <a  class="sister0" id="link1">Els<span>f</span>ie</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.</div>
ad<br/>sf
<p class="story">...</p>
</body>
</html>
"""
 
soup  =  BeautifulSoup(html_doc, features = "lxml" )
# 找到第一个a标签
tag1  =  soup.find(name = 'a' )
# 找到全部的a标签
tag2  =  soup.find_all(name = 'a' )
# 找到id＝link2的标签
tag3  =  soup.select( '#link2' )

使用示例：前端

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from  bs4  import  BeautifulSoup
 
html_doc  =  """
<html><head><title>The Dormouse's story</title></head>
<body>
     ...
</body>
</html>
"""
 
soup  =  BeautifulSoup(html_doc, features = "lxml" )

1. name，标签名称python

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# tag = soup.find('a')
# name = tag.name # 获取
# print(name)
# tag.name = 'span' # 设置
# print(soup)

2. attr，标签属性git

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# tag = soup.find('a')
# attrs = tag.attrs    # 获取
# print(attrs)
# tag.attrs = {'ik':123} # 设置
# tag.attrs['id'] = 'iiiii' # 设置
# print(soup)

3. children,全部子标签github

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# body = soup.find('body')
# v = body.children

4. children,全部子子孙孙标签ajax

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# body = soup.find('body')
# v = body.descendants

5. clear,将标签的全部子标签所有清空（保留标签名）算法

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# tag = soup.find('body')
# tag.clear()
# print(soup)

6. decompose,递归的删除全部的标签json

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# body = soup.find('body')
# body.decompose()
# print(soup)

7. extract,递归的删除全部的标签，并获取删除的标签服务器

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# body = soup.find('body')
# v = body.extract()
# print(soup)

8. decode,转换为字符串（含当前标签）；decode_contents（不含当前标签）cookie

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# body = soup.find('body')
# v = body.decode()
# v = body.decode_contents()
# print(v)

9. encode,转换为字节（含当前标签）；encode_contents（不含当前标签）

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# body = soup.find('body')
# v = body.encode()
# v = body.encode_contents()
# print(v)

10. find,获取匹配的第一个标签

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# tag = soup.find('a')
# print(tag)
# tag = soup.find(name='a', attrs={'class': 'sister'}, recursive=True, text='Lacie')
# tag = soup.find(name='a', class_='sister', recursive=True, text='Lacie')
# print(tag)

11. find_all,获取匹配的全部标签

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# tags = soup.find_all('a')
# print(tags)
 
# tags = soup.find_all('a',limit=1)
# print(tags)
 
# tags = soup.find_all(name='a', attrs={'class': 'sister'}, recursive=True, text='Lacie')
# # tags = soup.find(name='a', class_='sister', recursive=True, text='Lacie')
# print(tags)
 
 
# ####### 列表 #######
# v = soup.find_all(name=['a','div'])
# print(v)
 
# v = soup.find_all(class_=['sister0', 'sister'])
# print(v)
 
# v = soup.find_all(text=['Tillie'])
# print(v, type(v[0]))
 
 
# v = soup.find_all(id=['link1','link2'])
# print(v)
 
# v = soup.find_all(href=['link1','link2'])
# print(v)
 
# ####### 正则 #######
import  re
# rep = re.compile('p')
# rep = re.compile('^p')
# v = soup.find_all(name=rep)
# print(v)
 
# rep = re.compile('sister.*')
# v = soup.find_all(class_=rep)
# print(v)
 
# rep = re.compile('http://www.oldboy.com/static/.*')
# v = soup.find_all(href=rep)
# print(v)
 
# ####### 方法筛选 #######
# def func(tag):
# return tag.has_attr('class') and tag.has_attr('id')
# v = soup.find_all(name=func)
# print(v)
 
 
# ## get,获取标签属性
# tag = soup.find('a')
# v = tag.get('id')
# print(v)

12. has_attr,检查标签是否具备该属性

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# tag = soup.find('a')
# v = tag.has_attr('id')
# print(v)

13. get_text,获取标签内部文本内容

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# tag = soup.find('a')
# v = tag.get_text
# print(v)

14. index,检查标签在某标签中的索引位置

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# tag = soup.find('body')
# v = tag.index(tag.find('div'))
# print(v)
 
# tag = soup.find('body')
# for i,v in enumerate(tag):
# print(i,v)

15. is_empty_element,是不是空标签(是否能够是空)或者自闭合标签，

     判断是不是以下标签：'br' , 'hr', 'input', 'img', 'meta','spacer', 'link', 'frame', 'base'

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# tag = soup.find('br')
# v = tag.is_empty_element
# print(v)

16. 当前的关联标签

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# soup.next
# soup.next_element
# soup.next_elements
# soup.next_sibling
# soup.next_siblings
 
#
# tag.previous
# tag.previous_element
# tag.previous_elements
# tag.previous_sibling
# tag.previous_siblings
 
#
# tag.parent
# tag.parents

17. 查找某标签的关联标签

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# tag.find_next(...)
# tag.find_all_next(...)
# tag.find_next_sibling(...)
# tag.find_next_siblings(...)
 
# tag.find_previous(...)
# tag.find_all_previous(...)
# tag.find_previous_sibling(...)
# tag.find_previous_siblings(...)
 
# tag.find_parent(...)
# tag.find_parents(...)
 
# 参数同find_all

18. select,select_one, CSS选择器

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soup.select( "title" )
 
soup.select( "p nth-of-type(3)" )
 
soup.select( "body a" )
 
soup.select( "html head title" )
 
tag  =  soup.select( "span,a" )
 
soup.select( "head > title" )
 
soup.select( "p > a" )
 
soup.select( "p > a:nth-of-type(2)" )
 
soup.select( "p > #link1" )
 
soup.select( "body > a" )
 
soup.select( "#link1 ~ .sister" )
 
soup.select( "#link1 + .sister" )
 
soup.select( ".sister" )
 
soup.select( "[class~=sister]" )
 
soup.select( "#link1" )
 
soup.select( "a#link2" )
 
soup.select( 'a[href]' )
 
soup.select( 'a[href="http://example.com/elsie"]' )
 
soup.select( 'a[href^="http://example.com/"]' )
 
soup.select( 'a[href$="tillie"]' )
 
soup.select( 'a[href*=".com/el"]' )
 
 
from  bs4.element  import  Tag
 
def  default_candidate_generator(tag):
     for  child  in  tag.descendants:
         if  not  isinstance (child, Tag):
             continue
         if  not  child.has_attr( 'href' ):
             continue
         yield  child
 
tags  =  soup.find( 'body' ).select( "a" , _candidate_generator = default_candidate_generator)
print ( type (tags), tags)
 
from  bs4.element  import  Tag
def  default_candidate_generator(tag):
     for  child  in  tag.descendants:
         if  not  isinstance (child, Tag):
             continue
         if  not  child.has_attr( 'href' ):
             continue
         yield  child
 
tags  =  soup.find( 'body' ).select( "a" , _candidate_generator = default_candidate_generator, limit = 1 )
print ( type (tags), tags)

19. 标签的内容

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# tag = soup.find('span')
# print(tag.string)          # 获取
# tag.string = 'new content' # 设置
# print(soup)
 
# tag = soup.find('body')
# print(tag.string)
# tag.string = 'xxx'
# print(soup)
 
# tag = soup.find('body')
# v = tag.stripped_strings  # 递归内部获取全部标签的文本
# print(v)

20.append在当前标签内部追加一个标签

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# tag = soup.find('body')
# tag.append(soup.find('a'))
# print(soup)
#
# from bs4.element import Tag
# obj = Tag(name='i',attrs={'id': 'it'})
# obj.string = '我是一个新来的'
# tag = soup.find('body')
# tag.append(obj)
# print(soup)

21.insert在当前标签内部指定位置插入一个标签

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# from bs4.element import Tag
# obj = Tag(name='i', attrs={'id': 'it'})
# obj.string = '我是一个新来的'
# tag = soup.find('body')
# tag.insert(2, obj)
# print(soup)

22. insert_after,insert_before 在当前标签后面或前面插入

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# from bs4.element import Tag
# obj = Tag(name='i', attrs={'id': 'it'})
# obj.string = '我是一个新来的'
# tag = soup.find('body')
# # tag.insert_before(obj)
# tag.insert_after(obj)
# print(soup)

23. replace_with 在当前标签替换为指定标签

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# from bs4.element import Tag
# obj = Tag(name='i', attrs={'id': 'it'})
# obj.string = '我是一个新来的'
# tag = soup.find('div')
# tag.replace_with(obj)
# print(soup)

24. 建立标签之间的关系

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# tag = soup.find('div')
# a = soup.find('a')
# tag.setup(previous_sibling=a)
# print(tag.previous_sibling)

25. wrap，将指定标签把当前标签包裹起来

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# from bs4.element import Tag
# obj1 = Tag(name='div', attrs={'id': 'it'})
# obj1.string = '我是一个新来的'
#
# tag = soup.find('a')
# v = tag.wrap(obj1)
# print(soup)
 
# tag = soup.find('a')
# v = tag.wrap(soup.find('p'))
# print(soup)

26. unwrap，去掉当前标签，将保留其包裹的标签

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# tag = soup.find('a')
# v = tag.unwrap()
# print(soup)

更多参数官方：http://beautifulsoup.readthedocs.io/zh_CN/v4.4.0/

 

5、示例

把下面代码，加入到代码中，能够下载网站源码到本地分析

with open('weixin.html','wb') as f:
    f.write(wx_login_page.content)

一、爬取汽车之家新闻频道页面里面的图片

复制代码 
#!/usr/bin/env python
# -*- coding:utf-8 -*- 
# Author: nulige

import requests
from bs4 import BeautifulSoup

response = requests.get(
    url='http://www.autohome.com.cn/news/'
)

#解决爬虫乱码问题
response.encoding = response.apparent_encoding  

# 生成Soup对象，
soup = BeautifulSoup(response.text, features='html.parser')


# find查找第一个符合条件的对象
target = soup.find(id='auto-channel-lazyload-article')

#find_all查找全部符合的对象，查找出来的值在列表中
li_list = target.find_all('li')

#循环拿到具体每一个对象
for i in li_list:
    a = i.find('a')

    if a:
        print(a.attrs.get('href'))   #    # .attrs查找到属性
        txt = a.find('h3').text  # 是对象
        img_url = a.find('img').attrs.get('src')
        print(img_url)
        # 再发一个请求
        img_response = requests.get(url=img_url)
        import uuid
        file_name = str(uuid.uuid4()) + '.jpg'
        with open(file_name,'wb') as f:
            f.write(img_response.content)


备注：
　# 找到第一个a标签
　　tag1  =  soup.find(name = 'a' )
 
　　# 找到全部的a标签
　　tag2  =  soup.find_all(name = 'a' )
 
　　# 找到id＝link2的标签
　　tag3  =  soup.select( '#link2' )
复制代码

二、自动登录抽屉网

复制代码 
#!/usr/bin/env python
# -*- coding: utf8 -*-
# __Author: "Skiler Hao"
# date: 2017/5/10 11:06
import requests
from bs4 import BeautifulSoup

# 第一次请求
first_request_response = requests.get(
    url = 'http://dig.chouti.com/',
)
# 获取第一次登陆获取的cookie内容
firstget_cookie_dict = first_request_response.cookies.get_dict()


# 登陆POST请求
post_dict = {
    'phone': '8618811*****', #86+手机号码
    'password': '******',    #密码
    'oneMonth': 1
}
# 发送请求，携带cookie和数据
login_response = requests.post(
    url = 'http://dig.chouti.com/login',
    data = post_dict,
    cookies= firstget_cookie_dict
)


# 点赞请求
dianzan_response = requests.post(
    url = 'http://dig.chouti.com/link/vote?linksId=11832246',
    cookies= firstget_cookie_dict
)
print(dianzan_response.text)


# 取消点赞
cancel_dianzan_response = requests.post(
    url = 'http://dig.chouti.com/vote/cancel/vote.do',
    cookies= firstget_cookie_dict,
    data={'linksId':11832246}
)
print(cancel_dianzan_response.text)


# 获取我的信息
get_person_info_resonse = requests.get(
    url = 'http://dig.chouti.com/profile',
    cookies= firstget_cookie_dict,
)
# 按照某种encoding方式编码
get_person_info_resonse.encoding = get_person_info_resonse.apparent_encoding
# 将其内容放入BS中进行解析
person_info_site = BeautifulSoup(get_person_info_resonse.text,features='html.parser')
# 找到以后能够作任何处理，获取配置中的nickname
nickname_tag = person_info_site.find(id='nick')
nickname = person_info_site.find(id='nick').attrs.get('value')
print('昵称：',nickname)

# 更新本身在抽屉上的我的信息
personal_info = {
    'jid': 'cdu_49017916793',
    'nick': '努力哥',
    'imgUrl': 'http://img2.chouti.com/CHOUTI_90A38B32473A49B7B26A49F46B34268C_W585H359=C60x60.png',
# http://img2.chouti.com/CHOUTI_BAE7F736FE7B48E49D1CEE459020F3B0_W390H390=48x48.jpg
    'sex': True,
    'proveName': '北京',
    'cityName': '澳门',
    'sign': '黑hi呃呃哈发到付'
}
update_person_info_resonse = requests.post(
    url = 'http://dig.chouti.com/profile/update',
    cookies= firstget_cookie_dict,
    data=personal_info
)
print(update_person_info_resonse.text)

#########################Session方式登陆抽屉#########################

session = requests.Session()
# 先登录一下抽屉网
i1 = session.get(
    url='http://dig.chouti.com/'
)
# 模拟抽屉登陆
login_post_dict = {
    'phone': '86188116*****', #86+手机号码
    'password': '******',  #密码
    'oneMonth': 1
}
i2 = session.post(
    url='http://dig.chouti.com/login',
    data=login_post_dict,
)
复制代码

 三、自动登录GitHub

复制代码 
#!/usr/bin/env python
# -*- coding: utf8 -*-
# date: 2017/5/10 16:32

import requests
from bs4 import BeautifulSoup
# GitHub是基于authenticity_token，具备预防csrf_token的功能

# 首先访问页面，获取页面上的authenticity_token
i1 = requests.get('https://github.com/login')
# print(i1.content)
login_page_res = BeautifulSoup(i1.content,features='lxml')
authenticity_token = login_page_res.find(name='input',attrs={'name':'authenticity_token'}).attrs.get('value')
cookies1 = i1.cookies.get_dict()

# print(authenticity_token)
form_data = {
    'commit': 'Sign in',
    'utf8': '✓',
    'authenticity_token': authenticity_token,
    'login': '*****',
    'password': '******',
}

# 将数据封装在post请求中进行登陆，并且要加上cookie
login_res = requests.post(
    url='https://github.com/session',
    data=form_data,
    cookies=cookies1
)
# print(login_res.text)
# 拿到页面中的本身的项目列表
login_page_res = BeautifulSoup(login_res.content,features='lxml')
list_info = login_page_res.select("span .repo")
for i in list_info:
    print(i.text)
cookies1 = i1.cookies.get_dict()
复制代码

四、自动登陆cnblog

博客园站用了一个rsa算法的加密模块，因此安装加密模块。才能验证登陆。

pip3 install rsa

代码：

复制代码 
#!/usr/bin/env python
# -*- coding: utf8 -*-
# date: 2017/5/11 10:51
import re
import json
import base64
import rsa
import requests
from bs4 import BeautifulSoup

# 负责模仿前端js模块对帐号和密码加密
def js_enrypt(text):
    # 先从博客园拿到public key
    public_key = 'MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQCp0wHYbg/NOPO3nzMD3dndwS0MccuMeXCHgVlGOoYyFwLdS24Im2e7YyhB0wrUsyYf0/nhzCzBK8ZC9eCWqd0aHbdgOQT6CuFQBMjbyGYvlVYU2ZP7kG9Ft6YV6oc9ambuO7nPZh+bvXH0zDKfi02prknrScAKC0XhadTHT3Al0QIDAQAB'

    # 将拿到的一串字符，转换成64进制
    der = base64.standard_b64decode(public_key)

    # 再将其转换成数字，做为公钥加载
    pk = rsa.PublicKey.load_pkcs1_openssl_der(der)

    # 运用公钥对传进来的文字进行加密
    v1 = rsa.encrypt(bytes(text,'utf8'),pk)

    # 对加密后的内容进行解码
    value = base64.encodebytes(v1).replace(b'\n',b'')

    value = value.decode('utf8')

    # 将其返回
    return value

session = requests.Session()

# 写个错误的用户名和密码，提交一下。就找到提交数据
post_data = {
    'input1': js_enrypt('******'),
    'input2': js_enrypt('******'),
    'remember': True
}

# 发送一次请求，获取ajax发送post时要发送的VerificationToken，须要将其放在请求头部
login_page = session.get(
    url='https://passport.cnblogs.com/user/signin',
)
VerificationToken = re.compile("'VerificationToken': '(.*)'")
v = re.search(VerificationToken,login_page.text)
VerificationToken = v.group(1)

# 发送请求，注意将数据json序列化，由于Accept:application/json
login_post_res = session.post(
    url='https://passport.cnblogs.com/user/signin',
    data=json.dumps(post_data),
    headers={
        'VerificationToken': VerificationToken,
        'X-Requested-With': 'XMLHttpRequest',
        'Content-Type': 'application/json; charset=UTF-8'
    }
)

# 登陆帐号设置页
setting_page = session.get(
    url='https://home.cnblogs.com/set/account/',
)

soup = BeautifulSoup(setting_page.content,features='lxml')
name = soup.select_one('#loginName_display_block div').get_text().strip()
print('你的帐号名为:',name)
复制代码

五、自动登陆知乎

复制代码 
#!/usr/bin/env python
# -*- coding: utf8 -*-

import requests
from bs4 import BeautifulSoup

session = requests.Session()

# 知乎会查看你的是否有用户客户端信息，没有不会让爬的
signin_page = session.get(
    url='https://www.zhihu.com/#signin',
    headers={
        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36',
    }
)

# 拿到页面的_xrf为了防止csrf攻击，post数据的时候须要提供
signin_page_tag = BeautifulSoup(signin_page.content,features='lxml')
xsrf_code = signin_page_tag.find('input',attrs={'name':'_xsrf'}).attrs.get('value')

# 从知乎服务器获取验证码照片，发送请求POST，发现须要传入如下三个参数
# r:1494416****
# type:login
# lang:cn
import time
current_time = time.time()
yanzhengma = session.get(
    url='https://www.zhihu.com/captcha.gif',
    params={
        'r': current_time,
        'type': 'login',
        # 'lang': 'en' # 使用不一样的语言，cn最为复杂，不加的话，最容易识别，en为立体的英文也很差识别
    },
    headers={
        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36',
    }
)

# 将从服务器收到的验证码写入文件，能够查看啦
with open('zhihu.gif', 'wb') as f:
    f.write(yanzhengma.content)

captcha = input("请打开照片查看验证码：")
form_data = {
    '_xsrf': xsrf_code,
    'password': '********',
    'captcha': captcha,

    # 'captcha': '{"img_size": [200, 44], "input_points": [[40.2, 34.2], [156.2, 28.2], [138.2, 24.2]]}',
    # 'captcha_type': 'cn',  # 若是为中文的验证码比较复杂

    'phone_num': '***********',  #填手机号码登陆
    # 'email':"sddasd@123.com"  # 邮箱登陆的方式
}

login_response = session.post(
    url='https://www.zhihu.com/login/phone_num', #前端会根据你的数据类型选择用邮箱或者手机号码登陆
    # url='https://www.zhihu.com/login/phone_num'
    data=form_data,
    headers = {
        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36',
    }
)
index_page = session.get(
    url='https://www.zhihu.com/',
    headers={
        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36',
    }
)
index_page_tag = BeautifulSoup(index_page.content,features='lxml')
print(index_page_tag)
复制代码

运行程序后，输入验证码。登陆成功后，搜索用户名称，能找到我多个相同的用户名称，就说明登陆成功。

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