爬虫之爬汽车之家
一、话说爬虫
先说说爬虫,爬虫常被用来抓取特定网站网页的HTML数据,定位在后端数据的获取,而对于网站而言,爬虫给网站带来流量的同时,一些设计不好的爬虫由于爬得太猛,导致给网站来带很大的负担,当然再加上一些网站并不希望被爬取,所以就出现了许许多多的反爬技术。
二、安装模块
1. requests
模块安装方法:
pip3 install requests
2、beautisoup模块
软件安装方法:
pip3 install beautifulsoup4 或 pip3 install bs4
3、lxml模块
#必须先安装whell依赖 (请换成国内pip源进行安装,否则容易报错)
pip install wheel
#在cmd中,输入python进入python。
然后输入import pip;print(pip.pep425tags.get_supported()),界面上输出当前python的版本信息,如图。
再跟据上面查到的版本信息,找到下面对应的版本进行安装。
#下载地址:https://pypi.python.org/pypi/lxml/3.7.3 (网站打不开,请FQ,就可以打开)
#python3.5就选择cp3m版本 lxml-3.7.3-cp35-cp35m-win32.whl
#安装方法
pip3 install lxml-3.6.4-cp35-cp35m-win_amd64.whl
进入python3,输入import lxml,未报错,即表示安装成功。
三、requests模块用法
Python标准库中提供了:urllib、urllib2、httplib等模块以供Http请求,但是,它的 API 太渣了。它是为另一个时代、另一个互联网所创建的。它需要巨量的工作,甚至包括各种方法覆盖,来完成最简单的任务。
Requests 是使用 Apache2 Licensed 许可证的 基于Python开发的HTTP 库,其在Python内置模块的基础上进行了高度的封装,从而使得Pythoner进行网络请求时,变得美好了许多,使用Requests可以轻而易举的完成浏览器可有的任何操作。
1、GET请求
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# 1、无参数实例 import requests ret = requests.get( \'https://github.com/timeline.json\' ) print ret.url print ret.text # 2、有参数实例 import requests payload = { \'key1\' : \'value1\' , \'key2\' : \'value2\' } ret = requests.get( "http://httpbin.org/get" , params = payload) print ret.url print ret.text |
2、POST请求
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# 1、基本POST实例 import requests payload = { \'key1\' : \'value1\' , \'key2\' : \'value2\' } ret = requests.post( "http://httpbin.org/post" , data = payload) print ret.text # 2、发送请求头和数据实例 import requests import json url = \'https://api.github.com/some/endpoint\' payload = { \'some\' : \'data\' } headers = { \'content-type\' : \'application/json\' } ret = requests.post(url, data = json.dumps(payload), headers = headers) print ret.text print ret.cookies |
3、requests属性
response = requests.get(\'URL\')
response.text # 获取文本内容
response.content # 获取文本内容,字节
response.encoding # 设置返回结果的编码
response.aparent_encoding # 获取网站原始的编码
response.status_code # 状态码
response.cookies.get_dict() # cookies
4、关系和方法
- 方法关系
requests.get(url, params
=
None
,
*
*
kwargs)
requests.post(url, data
=
None
, json
=
None
,
*
*
kwargs)
requests.put(url, data
=
None
,
*
*
kwargs)
requests.head(url,
*
*
kwargs)
requests.delete(url,
*
*
kwargs)
requests.patch(url, data
=
None
,
*
*
kwargs)
requests.options(url,
*
*
kwargs)
- 在此方法的基础上构建
requests.request(method, url,
*
*
kwargs)
- method: 提交方式
- url: 提交地址
- params: 在URL中传递的参数,GET
requests.request(
method=\'GET\',
url= \'http://www.nulige.com\',
params = {\'k1\':\'v1\',\'k2\':\'v2\'}
)
# http://www.nulige.com?k1=v1&k2=v2
- data: 在请求体里传递的数据
requests.request(
method=\'POST\',
url= \'http://www.nulige.com\',
params = {\'k1\':\'v1\',\'k2\':\'v2\'},
data = {\'use\':\'alex\',\'pwd\': \'123\',\'x\':[11,2,3}
)
请求头:
content-type: application/url-form-encod.....
请求体:
use=alex&pwd=123
- json 在请求体里传递的数据
requests.request(
method=\'POST\',
url= \'http://www.oldboyedu.com\',
params = {\'k1\':\'v1\',\'k2\':\'v2\'},
json = {\'use\':\'alex\',\'pwd\': \'123\'}
)
请求头:
content-type: application/json
请求体:
"{\'use\':\'alex\',\'pwd\': \'123\'}"
PS: 字典中嵌套字典时
- headers 请求头
requests.request(
method=\'POST\',
url= \'http://www.oldboyedu.com\',
params = {\'k1\':\'v1\',\'k2\':\'v2\'},
json = {\'use\':\'alex\',\'pwd\': \'123\'},
headers={
\'Referer\': \'http://dig.chouti.com/\',
\'User-Agent\': "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36"
}
)
- cookies Cookies
- files 上传文件
- auth 基本认证(headers中加入加密的用户名和密码)
- timeout 请求和响应的超时时间
- allow_redirects 是否允许重定向
- proxies 代理 (nginx反向代理模块)
- verify 是否忽略证书
- cert 证书文件
- stream 流的方式迭代下载
- session: 用于保存客户端历史访问信息
参数用法示例:
def param_method_url(): # requests.request(method=\'get\', url=\'http://127.0.0.1:8000/test/\') # requests.request(method=\'post\', url=\'http://127.0.0.1:8000/test/\') pass def param_param(): # - 可以是字典 # - 可以是字符串 # - 可以是字节(ascii编码以内) # requests.request(method=\'get\', # url=\'http://127.0.0.1:8000/test/\', # params={\'k1\': \'v1\', \'k2\': \'水电费\'}) # requests.request(method=\'get\', # url=\'http://127.0.0.1:8000/test/\', # params="k1=v1&k2=水电费&k3=v3&k3=vv3") # requests.request(method=\'get\', # url=\'http://127.0.0.1:8000/test/\', # params=bytes("k1=v1&k2=k2&k3=v3&k3=vv3", encoding=\'utf8\')) # 错误 # requests.request(method=\'get\', # url=\'http://127.0.0.1:8000/test/\', # params=bytes("k1=v1&k2=水电费&k3=v3&k3=vv3", encoding=\'utf8\')) pass def param_data(): # 可以是字典 # 可以是字符串 # 可以是字节 # 可以是文件对象 # requests.request(method=\'POST\', # url=\'http://127.0.0.1:8000/test/\', # data={\'k1\': \'v1\', \'k2\': \'水电费\'}) # requests.request(method=\'POST\', # url=\'http://127.0.0.1:8000/test/\', # data="k1=v1; k2=v2; k3=v3; k3=v4" # ) # requests.request(method=\'POST\', # url=\'http://127.0.0.1:8000/test/\', # data="k1=v1;k2=v2;k3=v3;k3=v4", # headers={\'Content-Type\': \'application/x-www-form-urlencoded\'} # ) # requests.request(method=\'POST\', # url=\'http://127.0.0.1:8000/test/\', # data=open(\'data_file.py\', mode=\'r\', encoding=\'utf-8\'), # 文件内容是:k1=v1;k2=v2;k3=v3;k3=v4 # headers={\'Content-Type\': \'application/x-www-form-urlencoded\'} # ) pass def param_json(): # 将json中对应的数据进行序列化成一个字符串,json.dumps(...) # 然后发送到服务器端的body中,并且Content-Type是 {\'Content-Type\': \'application/json\'} requests.request(method=\'POST\', url=\'http://127.0.0.1:8000/test/\', json={\'k1\': \'v1\', \'k2\': \'水电费\'}) def param_headers(): # 发送请求头到服务器端 requests.request(method=\'POST\', url=\'http://127.0.0.1:8000/test/\', json={\'k1\': \'v1\', \'k2\': \'水电费\'}, headers={\'Content-Type\': \'application/x-www-form-urlencoded\'} ) def param_cookies(): # 发送Cookie到服务器端 requests.request(method=\'POST\', url=\'http://127.0.0.1:8000/test/\', data={\'k1\': \'v1\', \'k2\': \'v2\'}, cookies={\'cook1\': \'value1\'}, ) # 也可以使用CookieJar(字典形式就是在此基础上封装) from http.cookiejar import CookieJar from http.cookiejar import Cookie obj = CookieJar() obj.set_cookie(Cookie(version=0, name=\'c1\', value=\'v1\', port=None, domain=\'\', path=\'/\', secure=False, expires=None, discard=True, comment=None, comment_url=None, rest={\'HttpOnly\': None}, rfc2109=False, port_specified=False, domain_specified=False, domain_initial_dot=False, path_specified=False) ) requests.request(method=\'POST\', url=\'http://127.0.0.1:8000/test/\', data={\'k1\': \'v1\', \'k2\': \'v2\'}, cookies=obj) def param_files(): # 发送文件 # file_dict = { # \'f1\': open(\'readme\', \'rb\') # } # requests.request(method=\'POST\', # url=\'http://127.0.0.1:8000/test/\', # files=file_dict) # 发送文件,定制文件名 # file_dict = { # \'f1\': (\'test.txt\', open(\'readme\', \'rb\')) # } # requests.request(method=\'POST\', # url=\'http://127.0.0.1:8000/test/\', # files=file_dict) # 发送文件,定制文件名 # file_dict = { # \'f1\': (\'test.txt\', "hahsfaksfa9kasdjflaksdjf") # } # requests.request(method=\'POST\', # url=\'http://127.0.0.1:8000/test/\', # files=file_dict) # 发送文件,定制文件名 # file_dict = { # \'f1\': (\'test.txt\', "hahsfaksfa9kasdjflaksdjf", \'application/text\', {\'k1\': \'0\'}) # } # requests.request(method=\'POST\', # url=\'http://127.0.0.1:8000/test/\', # files=file_dict) pass def param_auth(): from requests.auth import HTTPBasicAuth, HTTPDigestAuth ret = requests.get(\'https://api.github.com/user\', auth=HTTPBasicAuth(\'wupeiqi\', \'sdfasdfasdf\')) print(ret.text) # ret = requests.get(\'http://192.168.1.1\', # auth=HTTPBasicAuth(\'admin\', \'admin\')) # ret.encoding = \'gbk\' # print(ret.text) # ret = requests.get(\'http://httpbin.org/digest-auth/auth/user/pass\', auth=HTTPDigestAuth(\'user\', \'pass\')) # print(ret) # def param_timeout(): # ret = requests.get(\'http://google.com/\', timeout=1) # print(ret) # ret = requests.get(\'http://google.com/\', timeout=(5, 1)) # print(ret) pass def param_allow_redirects(): ret = requests.get(\'http://127.0.0.1:8000/test/\', allow_redirects=False) print(ret.text) def param_proxies(): # proxies = { # "http": "61.172.249.96:80", # "https": "http://61.185.219.126:3128", # } # proxies = {\'http://10.20.1.128\': \'http://10.10.1.10:5323\'} # ret = requests.get("http://www.proxy360.cn/Proxy", proxies=proxies) # print(ret.headers) # from requests.auth import HTTPProxyAuth # # proxyDict = { # \'http\': \'77.75.105.165\', # \'https\': \'77.75.105.165\' # } # auth = HTTPProxyAuth(\'username\', \'mypassword\') # # r = requests.get("http://www.google.com", proxies=proxyDict, auth=auth) # print(r.text) pass def param_stream(): ret = requests.get(\'http://127.0.0.1:8000/test/\', stream=True) print(ret.content) ret.close() # from contextlib import closing # with closing(requests.get(\'http://httpbin.org/get\', stream=True)) as r: # # 在此处理响应。 # for i in r.iter_content(): # print(i) def requests_session(): import requests session = requests.Session() ### 1、首先登陆任何页面,获取cookie i1 = session.get(url="http://dig.chouti.com/help/service") ### 2、用户登陆,携带上一次的cookie,后台对cookie中的 gpsd 进行授权 i2 = session.post( url="http://dig.chouti.com/login", data={ \'phone\': "8615131255089", \'password\': "xxxxxx", \'oneMonth\': "" } ) i3 = session.post( url="http://dig.chouti.com/link/vote?linksId=8589623", ) print(i3.text)
参考:http://cn.python-requests.org/zh_CN/latest/user/quickstart.html#id4
四、BeautifulSoup
该模块用于接收一个HTML或XML字符串,然后将其进行格式化,之后遍可以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。
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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\' )
# 找到所有的a标签 tag2 = soup.find_all(name = \'a\' )
# 找到id=link2的标签 tag3 = soup.select( \'#link2\' )
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使用示例:
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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" )
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1. name,标签名称
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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
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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,所有子标签
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# body = soup.find(\'body\') # v = body.children |
4. children,所有子子孙孙标签
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# body = soup.find(\'body\') # v = body.descendants |
5. clear,将标签的所有子标签全部清空(保留标签名)
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# tag = soup.find(\'body\') # tag.clear() # print(soup) |
6. decompose,递归的删除所有的标签
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# body = soup.find(\'body\') # body.decompose() # print(soup) |
7. extract,递归的删除所有的标签,并获取删除的标签
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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)
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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,获取匹配的所有标签
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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,检查标签是否具有该属性
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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,是否是空标签(是否可以是空)或者自闭合标签,
判断是否是如下标签:\’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)
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19. 标签的内容
1
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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 # 递归内部获取所有标签的文本 # 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. 创建标签之间的关系
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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/
五、示例
把下面代码,加入到代码中,可以下载网站源码到本地分析
with open(\'weixin.html\',\'wb\') as f:
f.write(wx_login_page.content)
1、爬取汽车之家新闻频道页面里面的图片
#!/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查找所有符合的对象,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\'
)
# 找到所有的a标签
tag2
=
soup.find_all(name
=
\'a\'
)
# 找到id=link2的标签
tag3
=
soup.select(
\'#link2\'
)
2、自动登陆抽屉网
#!/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/\', ) # 获取第一次登录获取的cookie内容 firstget_cookie_dict = first_request_response.cookies.get_dict() # 登录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) # 获取个人信息 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\') # 找到之后可以做任何处理,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) # 更新自己在抽屉上的个人信息 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方式登录抽屉######################### session = requests.Session() # 先登陆一下抽屉网 i1 = session.get( url=\'http://dig.chouti.com/\' ) # 模拟抽屉登录 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, )
3、自动登陆GitHub
#!/usr/bin/env python
# -*- coding: utf8 -*-
# date: 2017/5/10 16:32
import requests
from bs4 import BeautifulSoup
# GitHub是基于authenticity_token,具有预防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请求中进行登录,而且要加上cookie
login_res = requests.post(
url=\'https://github.com/session\',
data=form_data,
cookies=cookies1
)
# print(login_res.text)
# 拿到页面中的自己的项目列表
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()
4、自动登录cnblog
博客园站用了一个rsa算法的加密模块,所以安装加密模块。才能验证登录。
pip3 install rsa
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模块对账号和密码加密
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,作为公钥加载
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,需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,因为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\'
}
)
# 登录账号设置页
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(\'你的账号名为:\',name)
5、自动登录知乎
#!/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数据的时候需要提供
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,发现需要传入以下三个参数
# 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\' # 使用不同的语言,cn最为复杂,不加的话,最容易识别,en为立体的英文也不好识别
},
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,可以查看啦
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\', # 如果为中文的验证码比较复杂
\'phone_num\': \'***********\', #填手机号码登录
# \'email\':"sddasd@123.com" # 邮箱登录的方式
}
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录
# 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)
运行程序后,输入验证码。登录成功后,搜索用户名称,能找到我多个相同的用户名称,就说明登录成功。