requests模块的高级应用

requests抓取数据报错

- HttpConnectinPool:
    - 原因：
        - 1.短时间内发起了高频的请求导致ip被禁
        - 2.http连接池中的连接资源被耗尽
    - 解决：
        - 1.代理
        - 2.headers中加入Conection：“close”

代理服务器

- 代理：代理服务器，可以接受请求然后将其转发。
- 匿名度
    - 高匿：既不知道请求者使用了代理，也不知道请求者的真实IP
    - 匿名：知道请求者使用了代理，但是不知道请求者的真实IP
    - 透明：知道请求者使用了代理并且知道请求者的真实IP
- 类型：
    - http
    - https
- 免费代理：
    - www.goubanjia.com
    - 快代理
    - 西祠代理
    - http://http.zhiliandaili.cn/
    

在requests.get()方法中使用代理IP

import requests
headers = {
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36'
}
url = 'https://www.baidu.com/s?wd=ip'
page_text = requests.get(url,headers=headers,proxies={'https':'111.231.94.44:8888'}).text
with open('ip.html','w',encoding='utf-8') as fp:
    fp.write(page_text)

手动生成代理池

import random
proxy_list = [
    {'https':'121.231.94.44:8888'},
    {'https':'131.231.94.44:8888'},
    {'https':'141.231.94.44:8888'}
]
url = 'https://www.baidu.com/s?wd=ip'
page_text = requests.get(url,headers=headers,proxies=random.choice(proxy_list)).text
with open('ip.html','w',encoding='utf-8') as fp:
    fp.write(page_text)

从网上抓取代理IP自动生成代理池

from lxml import etree
import random

#从代理精灵中提取代理ip（用于爬取免费代理IP的代理IP是付费的）
ip_url = 'http://t.11jsq.com/index.php/api/entry?method=proxyServer.generate_api_url&packid=1&fa=0&fetch_key=&groupid=0&qty=4&time=1&pro=&city=&port=1&format=html&ss=5&css=&dt=1&specialTxt=3&specialJson=&usertype=2'
page_text = requests.get(ip_url,headers=headers).text
tree = etree.HTML(page_text)
ip_list = tree.xpath('//body//text()')
print(ip_list)

#爬取西祠代理
headers = {
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36',
    'Connection':"close"
}
url = 'https://www.xicidaili.com/nn/%d'
proxy_list_http = []
proxy_list_https = []
for page in range(1,20):
    new_url = format(url%page)
    ip_port = random.choice(ip_list)
    page_text = requests.get(new_url,headers=headers,proxies={'https':ip_port}).text
    tree = etree.HTML(page_text)
    #tbody不可以出现在xpath表达式中
    tr_list = tree.xpath('//*[@id="ip_list"]//tr')[1:]
    for tr in tr_list:
        ip = tr.xpath('./td[2]/text()')[0]
        port = tr.xpath('./td[3]/text()')[0]
        t_type = tr.xpath('./td[6]/text()')[0]
        ips = ip+':'+port
        if t_type == 'HTTP':
            dic = {
                t_type: ips
            }
            proxy_list_http.append(dic)
        else:
            dic = {
                t_type:ips
            }
            proxy_list_https.append(dic)
print(len(proxy_list_http),len(proxy_list_https))

#检测代理IP是否可用
for ip in proxy_list_http:
    response = requests.get('https://www/sogou.com',headers=headers,proxies={'https':ip})
    if response.status_code == '200':
        print('检测到了可用ip')

Cookie

- cookie的处理
    - 手动处理：将cookie封装到headers中
    - 自动处理：session对象。可以创建一个session对象，改对象可以像requests一样进行请求发送。不同之处在于如果在使用session进行请求发送的过程中产生了cookie，则cookie会被自动存储在session对象中。

示例1.1（不携带Cookie访问）

import requests
#对雪球网中的新闻数据进行爬取https://xueqiu.com/
url="https://xueqiu.com/"
headers = {
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36',
}
response_text=requests.get(url,headers).text
response_text

此时是获取不到网页的数据信息，因为如果想要访问页面的数据，需要携带Cookie数据。

示例1.2（手动添加Cookie后访问）

import requests
url="https://xueqiu.com/"
headers = {
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36',
    'Cookie':"aliyungf_tc=AQAAAG6X1z3opwMAkLryeKCukrQNV62H; acw_tc=2760822e15886875892523578ed4228020edfe26c3c0eb41d7d9467d8bf6e3; xq_a_token=48575b79f8efa6d34166cc7bdc5abb09fd83ce63; xqat=48575b79f8efa6d34166cc7bdc5abb09fd83ce63; xq_r_token=7dcc6339975b01fbc2c14240ce55a3a20bdb7873; xq_id_token=eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJ1aWQiOi0xLCJpc3MiOiJ1YyIsImV4cCI6MTU4OTY4MjczMCwiY3RtIjoxNTg4Njg3NTYzMDY1LCJjaWQiOiJkOWQwbjRBWnVwIn0.oXNGRbTOZfgChAFNq-BN9v7Q01-ogPgYI-nNDdasJKwSIF4TpfPgTZzRQ6evFHxCmX22GvrL-N7nCVwYTnWWn-7oB7K9d6dagYPja5uWqBNwI1qL7A5yP_SF4OG0meC2BSOU-gAt7whoE7DC-ChkJL0CJ5ZyqjNnYsl_EJjPUDMvEm0ex6surEHJW3uIfh15iIUYJKrjT5FxxjkyNe_C0KjIZXRgJMK77-rcTxlBxzHJkeCIsEKwpEYjKTWAJJYL4r-gC49wJvT_Y2WrdVOtQ9rXT2Q2_rHStT-zEBb9p55ZZakfHb9uzFadI7J1Zkl6w02ns8DVt-DKKRM5XRBg3A; u=691588687589257; Hm_lvt_1db88642e346389874251b5a1eded6e3=1588687591; device_id=24700f9f1986800ab4fcc880530dd0ed; s=co11ch62mg; __utma=1.206451581.1588687610.1588687610.1588687610.1; __utmc=1; __utmz=1.1588687610.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); __utmt=1; __utmb=1.1.10.1588687610; Hm_lpvt_1db88642e346389874251b5a1eded6e3=1588687614",
}
response_text=requests.get(url,headers=headers).text
response_text=response_text.encode("iso-8859-1").decode("utf-8")
response_text

此时可以获得页面数据信息，但是如果目标网站每次访问的Cookie是动态生成的，手动添加就行不通了。

示例1.3（使用Session对象自动获取并添加Cookie到请求信息中）

import requests
url="https://xueqiu.com/"
headers = {
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36',
}
session=requests.Session()
response_text=session.get(url,headers=headers).text
response_text=response_text.encode("iso-8859-1").decode("utf-8")
response_text

此时也是能够顺利取到网页数据。

自动登录中的图片验证码识别

- 验证码的识别
    - 超级鹰：http://www.chaojiying.com/about.html
        - 注册：（用户中心身份）
        - 登陆：
            - 创建一个软件：899370
            - 下载示例代码
    - 打码兔
    - 云打码

古诗文网登录图片验证码识别

#!/usr/bin/env python
# coding:utf-8

import requests
from hashlib import md5

class Chaojiying_Client(object):

    def __init__(self, username, password, soft_id):
        self.username = username
        password = password.encode('utf-8')
        self.password = md5(password).hexdigest()
        self.soft_id = soft_id
        self.base_params = {
            'user': self.username,
            'pass2': self.password,
            'softid': self.soft_id,
        }
        self.headers = {
            'Connection': 'Keep-Alive',
            'User-Agent': 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 5.1; Trident/4.0)',
        }

    def PostPic(self, im, codetype):
        """
        im: 图片字节
        codetype: 题目类型 参考 http://www.chaojiying.com/price.html
        """
        params = {
            'codetype': codetype,
        }
        params.update(self.base_params)
        files = {'userfile': ('ccc.jpg', im)}
        r = requests.post('http://upload.chaojiying.net/Upload/Processing.php', data=params, files=files, headers=self.headers)
        return r.json()

    def ReportError(self, im_id):
        """
        im_id:报错题目的图片ID
        """
        params = {
            'id': im_id,
        }
        params.update(self.base_params)
        r = requests.post('http://upload.chaojiying.net/Upload/ReportError.php', data=params, headers=self.headers)
        return r.json()

# 返回验证码文本
def transformImg(imgPath,type_code):
    chaojiying = Chaojiying_Client('15922471244', 'sun10387834...', '904968')
    im = open(imgPath, 'rb').read()
    return chaojiying.PostPic(im, type_code)

# 古诗文网验证码识别
from lxml import etree
url="https://so.gushiwen.org/user/login.aspx?from=http://so.gushiwen.org/user/collect.aspx"
response_text=requests.get(url,headers=headers).text
tree=etree.HTML(response_text)
img_src="https://so.gushiwen.org"+tree.xpath('//img[@id="imgCode"]/@src')[0]
img_data=requests.get(url=img_src,headers=headers).content
with open("./code.jpg","wb") as fp:
    fp.write(img_data)
code_text=transformImg("./code.jpg",1902)
print(code_text)

模拟登陆古诗文网

# 模拟登陆
from lxml import etree
import requests
session=requests.Session()
url="https://so.gushiwen.org/user/login.aspx?from=http://so.gushiwen.org/user/collect.aspx"
response_text=session.get(url,headers=headers).text
tree=etree.HTML(response_text)
img_src="https://so.gushiwen.org"+tree.xpath('//img[@id="imgCode"]/@src')[0]
img_data=session.get(url=img_src,headers=headers).content
with open("./code.jpg","wb") as fp:
    fp.write(img_data)
code_text=transformImg("./code.jpg",1902)["pic_str"]
__VIEWSTATE=tree.xpath('//input[@id="__VIEWSTATE"]/@value')[0]
__VIEWSTATEGENERATOR=tree.xpath('//input[@id="__VIEWSTATEGENERATOR"]/@value')[0]
# print(code_text)
# print(__VIEWSTATE)
# print(__VIEWSTATEGENERATOR)

login_url="https://so.gushiwen.org/user/login.aspx?from=http%3a%2f%2fso.gushiwen.org%2fuser%2fcollect.aspx"
data={
    "__VIEWSTATE":__VIEWSTATE,
    "__VIEWSTATEGENERATOR": __VIEWSTATEGENERATOR,
    'from':" http://so.gushiwen.org/user/collect.aspx",
    "email": "15922471244",
    "pwd": "sun10387834...",
    "code": code_text,
    "denglu": "登录",
}
response_content=session.post(login_url,data=data,headers=headers).text
with open("./gushiwen.html","w",encoding="utf-8") as fp:
    fp.write(response_content)

模拟登陆经验总结：

常规的模拟登陆网站流程。
1：用户名 密码 验证码 在发起登录请求时要携带发送到服务端
2：如果登陆不成功，首先考虑data数据中是否有动态变化的请求参数（通常情况下动态变化的请求参数都会被隐藏在前台页面源码中）
3：如果携带动态数据登录还是失败，则需要考虑Cookie情况。可以使用Session对象发起网络请求。

线程池提高爬虫效率

客户端代码

# 使用了线程池的爬虫代码
from multiprocessing.dummy import Pool
import time
start = time.time()
urls = [
    'http://127.0.0.1:5000/bobo',
    'http://127.0.0.1:5000/jay'
]
def get_request(url):
    page_text = requests.get(url).text
    print(page_text)
pool = Pool(3)
pool.map(get_request,urls)

print('总耗时：',time.time()-start)

服务器端代码

from flask import Flask
import time

app = Flask(__name__)

@app.route('/bobo')
def index_bobo():
    time.sleep(2)
    return 'Hello bobo'

@app.route('/jay')
def index_jay():
    time.sleep(2)
    return 'Hello jay'

@app.route('/tom')
def index_tom():
    time.sleep(2)
    return 'Hello tom'

if __name__ == '__main__':
    app.run(threaded=True)

运行后发现原本需要4秒完成的任务，使用了线程池之后2秒就完成了。

单线程+多任务异步协程提高爬虫效率

### 单线程+多任务异步协程
- 协程
    - 在函数（特殊的函数）定义的时候，如果使用了async修饰的话，则改函数调用后会返回一个协程对象，并且函数内部的实现语句不会被立即执行
- 任务对象
    - 任务对象就是对协程对象的进一步封装。任务对象==高级的协程对象==特殊的函数
    - 任务对象时必须要注册到事件循环对象中
    - 给任务对象绑定回调：爬虫的数据解析中
- 事件循环
    - 当做是一个容器，容器中必须存放任务对象。
    - 当启动事件循环对象后，则事件循环对象会对其内部存储任务对象进行异步的执行。
- aiohttp:支持异步网络请求的模块

简单了解几个概念

协程

import asyncio
def callback(task):#作为任务对象的回调函数
    print('i am callback and ',task.result())

async def test():
    print('i am test()')
    return 'bobo'

c = test()
#封装了一个任务对象
task = asyncio.ensure_future(c)
task.add_done_callback(callback)
#创建一个事件循环的对象
loop = asyncio.get_event_loop()
loop.run_until_complete(task)

多任务

import asyncio
import time
start = time.time()
#在特殊函数内部的实现中不可以出现不支持异步的模块代码
async def get_request(url):
    await asyncio.sleep(2)
    print('下载成功:',url)

urls = [
    'www.1.com',
    'www.2.com'
]
tasks = []
for url in urls:
    c = get_request(url)
    # 创建任务
    task = asyncio.ensure_future(c)
    tasks.append(task)

loop = asyncio.get_event_loop()
#注意：挂起操作需要手动处理
loop.run_until_complete(asyncio.wait(tasks))
print(time.time()-start)

示例应用

import requests
import aiohttp
import time
import asyncio
s = time.time()
urls = [
    'http://127.0.0.1:5000/bobo',
    'http://127.0.0.1:5000/jay'
]

# async def get_request(url):
#     page_text = requests.get(url).text
#     return page_text
async def get_request(url):
   async with aiohttp.ClientSession() as s:
       async with await s.get(url=url) as response:
           page_text = await response.text()
           print(page_text)
   return page_text
tasks = []
for url in urls:
    c = get_request(url)
    task = asyncio.ensure_future(c)
    tasks.append(task)

loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.wait(tasks))

print(time.time()-s)

单线程+多任务异步协程实例

import aiohttp
import asyncio
import time
from lxml import etree

start = time.time()
urls = [
    'http://127.0.0.1:5000/bobo',
    'http://127.0.0.1:5000/jay',
    'http://127.0.0.1:5000/tom',
    'http://127.0.0.1:5000/bobo',
    'http://127.0.0.1:5000/jay',
    'http://127.0.0.1:5000/tom',
    'http://127.0.0.1:5000/bobo',
    'http://127.0.0.1:5000/jay',
    'http://127.0.0.1:5000/tom',
    'http://127.0.0.1:5000/bobo',
    'http://127.0.0.1:5000/jay',
    'http://127.0.0.1:5000/tom'
]
#特殊的函数：请求发送和响应数据的捕获
#细节：在每一个with前加上async，在每一个阻塞操作的前边加上await
async def get_request(url):
    async with aiohttp.ClientSession() as s:
        #s.get(url,headers,proxy="http://ip:port",params)
        async with await s.get(url) as response:
            page_text = await response.text()#read()返回的是byte类型的数据
            return page_text
#回调函数
def parse(task):
    page_text = task.result()
    tree = etree.HTML(page_text)
    parse_data = tree.xpath('//li/text()')
    print(parse_data)

tasks = []
for url in urls:
    c = get_request(url)
    task = asyncio.ensure_future(c)
    task.add_done_callback(parse)
    tasks.append(task)

loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.wait(tasks))

print(time.time()-start)

结果发现是可以实现提高效率的效果。