requests之爬虫
requests模块
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。
安装：
个人推荐使用pip安装
pip install requests
也可以使用easy_install安装
easy_install requests
安装完成后，在pythonIDE中，import requests，如果未报错，则表示安装成功
requests快速入门
#HTTP请求类型,实质上所有请求都可以用requests.request来代替，请求方式在参数method里面定义
#get类型
r = requests.get('https://github.com/timeline.json')
#post类型
r = requests.post("http://m.ctrip.com/post")
#put类型
r = requests.put("http://m.ctrip.com/put")
#delete类型
r = requests.delete("http://m.ctrip.com/delete")
#head类型
r = requests.head("http://m.ctrip.com/head")
#options类型
r = requests.options("http://m.ctrip.com/get")
#获取响应内容
print （r.content） #以字节的方式去显示，中文显示为字符
print （r.text） #以文本的方式去显示
#URL传递参数
payload = {'keyword': '日本', 'salecityid': '2'}
r = requests.get("http://m.ctrip.com/webapp/tourvisa/visa_list", params=payload)
print（ r.url） #示例为http://m.ctrip.com/webapp/tourvisa/visa_list?salecityid=2&keyword=日本
#获取/修改网页编码
r = requests.get('https://github.com/timeline.json')
print r.encoding
r.encoding = 'utf-8' 或者r.encoding = r.apparent_encoding(调用网页自身编码)
#json处理
r = requests.get('https://github.com/timeline.json')
print （r.json()） #需要先import json
#定制请求头
url = 'http://m.ctrip.com'
headers = {'User-Agent' : 'Mozilla/5.0 (Linux; Android 4.2.1; en-us; Nexus 4 Build/JOP40D) AppleWebKit/535.19 (KHTML, like Gecko) Chrome/18.0.1025.166 Mobile Safari/535.19'}
r = requests.post(url, headers=headers)
print （r.request.headers）
#复杂post请求
url = 'http://m.ctrip.com'
payload = {'some': 'data'}
r = requests.post(url, data=json.dumps(payload)) #如果传递的payload是string而不是dict，需要先调用dumps方法格式化一下
#响应状态码
r = requests.get('http://m.ctrip.com')
print（ r.status_code）
#响应头
r = requests.get('http://m.ctrip.com')
print （r.headers）
print （r.headers['Content-Type']）
print (r.headers.get('content-type')) #访问响应头部分内容的两种方式
#Cookies
url = 'http://example.com/some/cookie/setting/url'
r = requests.get(url)
r.cookies['example_cookie_name'] #读取cookies
url = 'http://m.ctrip.com/cookies'
cookies = dict(cookies_are='working')
r = requests.get(url, cookies=cookies) #发送cookies
#设置超时时间
r = requests.get('http://m.ctrip.com', timeout=0.001)
#设置访问代理
proxies = {
"http": "http://10.10.10.10:8888",
"https": "http://10.10.10.100:4444",
}
r = requests.get('http://m.ctrip.com', proxies=proxies)
#发送文件
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)
BeautifulSoup
BeautifulSoup是一个模块，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。
安装beautifulsoup 可以利用 pip 或者 easy_install 来安装，以下两种方法均可 easy_install beautifulsoup4 & pip3 install beautifulsoup4
Beautiful Soup支持Python标准库中的HTML解析器,还支持一些第三方的解析器，如果我们不安装它，则 Python 会使用 Python默认的解析器，lxml 解析器更加强大，速度更快，推荐安装。
| 解析器 | 使用方法 | 优势 | 劣势 |
|---|---|---|---|
| Python标准库 | BeautifulSoup(markup, “html.parser”) |
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| lxml HTML 解析器 | BeautifulSoup(markup, “lxml”) |
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| lxml XML 解析器 | BeautifulSoup(markup, [“lxml”, “xml”])BeautifulSoup(markup, “xml”) |
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| html5lib | BeautifulSoup(markup, “html5lib”) |
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创建 Beautiful Soup 对象
首先必须要导入 bs4 库
from bs4 import BeautifulSoup
创建beautifulsoup对象
soup = BeautifulSoup(html)
另外，我们还可以用本地 HTML 文件来创建对象，例如
soup = BeautifulSoup(open('index.html'))
使用示例：
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')
1. name，标签名称
1 # tag = soup.find('a') 2 # name = tag.name # 获取 3 # print(name) 4 # tag.name = 'span' # 设置 5 # print(soup)
2. attr，标签属性
# 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
# body = soup.find('body') # v = body.children
4. children,所有子子孙孙标签
# 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）
# tag = soup.find('body') # tag.clear() # print(soup)
6. decompose,递归的删除所有的标签
# 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
# 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）
# 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）
# 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
# 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
# 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
# tag = soup.find('a') # v = tag.has_attr('id') # print(v)
13. get_text,获取标签内部文本内容
# tag = soup.find('a') # v = tag.get_text('id') # print(v)
14. index,检查标签在某标签中的索引位置
# 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'
# tag = soup.find('br') # v = tag.is_empty_element # print(v)
16. 当前的关联标签
# 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
# 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
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
# 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
# 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
# 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
# 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
# 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
# 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
# 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
# tag = soup.find('a') # v = tag.unwrap() # print(soup)
基于requests实现自动登录示例
1.自动登录Github
from bs4 import BeautifulSoup import requests import re session = requests.session() head = { 'User-Agent':'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36' } r1 = session.get('https://github.com/login',headers = head) r1.encoding = r1.apparent_encoding token_tag = re.findall('<input name="authenticity_token" type="hidden" value="(.*)" />',r1.text,) response1 = session.post( url='https://github.com/session', verify=False, data={ 'commit':'Sign in', 'utf8':'✓', 'authenticity_token':token_tag[0], 'login':'account', 'password':'password' } ) print(response1.text)
2.登录知乎
import time import requests from bs4 import BeautifulSoup session = requests.Session() i1 = 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', } ) soup1 = BeautifulSoup(i1.text, 'lxml') xsrf_tag = soup1.find(name='input', attrs={'name': '_xsrf'}) xsrf = xsrf_tag.get('value') current_time = time.time() i2 = session.get( url='https://www.zhihu.com/captcha.gif', params={'r': current_time, 'type': 'login'}, 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', }) with open('zhihu.gif', 'wb') as f: f.write(i2.content) captcha = input('请打开zhihu.gif文件，查看并输入验证码：') form_data = { "_xsrf": xsrf, 'password': 'xxooxxoo', "captcha": 'captcha', 'email': '424662508@qq.com' } i3 = session.post( url='https://www.zhihu.com/login/email', 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', } ) i4 = session.get( url='https://www.zhihu.com/settings/profile', 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', } ) soup4 = BeautifulSoup(i4.text, 'lxml') tag = soup4.find(id='rename-section') nick_name = tag.find('span',class_='name').string print(nick_name)
