标签：path urllib2 input method gps base out ice contents
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、无参数实例 import requests ret = requests.get(‘https://github.com/timeline.json‘) # 2、有参数实例 import requests payload = {‘key1‘: ‘value1‘, ‘key2‘: ‘value2‘} ret = requests.get("http://httpbin.org/get", params=payload)
# 1、基本POST实例 import requests payload = {‘key1‘: ‘value1‘, ‘key2‘: ‘value2‘} ret = requests.post("http://httpbin.org/post", data=payload) # 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)
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‘)) 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‘} # 设置编码 # requests.post(init_url,data=json.dumps(post_data),headers={‘Content-Type‘:‘application/json;charset=utf-8‘}) 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)
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。
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‘)
安装：
pip3 install beautifulsoup4
1. name，标签名称
# tag = soup.find(‘a‘) # name = tag.name # 获取 # print(name) # tag.name = ‘span‘ # 设置 # 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
标签：path urllib2 input method gps base out ice contents
原文地址：http://www.cnblogs.com/wangyufu/p/7749360.html
