爬虫环境
python3.7+pycharm
最近发现一个网站，首商网，上面企业信息百万以上，然而网站一点儿反爬机制都没有，这对我们喜欢爬虫的来讲岂不是太爽了，直接拿出撸一套代码，用了三次并发，每次用20条线程，爬了五六个小时，拿下了20万条数据，美滋滋！
还是老规矩，下面直接上代码，所有的注释以及解释都在代码中，可以直接运行：
for k in range(1, 1651, 50):
# -*- coding: utf-8 -*-
# 本项目是原始的异步爬虫，没有封装为函数
import asyncio
import aiohttp
import time
from bs4 import BeautifulSoup
import csv
import requests
from concurrent.futures import ThreadPoolExecutor, wait, ALL_COMPLETED
# 先用并发获取每个页面的子链接
########################################################################################################################
pro = 'zhaoshuang:LINA5201314@ 14.215.44.251:28803'
proxies = {'http://': 'http://' + pro,
'httpS://': 'https://' + pro
}
# 加入请求头
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit'
'/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36'}
wzs = []
def parser(url):
print(url)
try:
response = requests.get(url, headers=headers)
soup1 = BeautifulSoup(response.text, "lxml")
# body > div.list_contain > div.left > div.list_li > ul > li:nth-child(1) > table > tbody > tr > td:nth-child(3) > div.title > a
wz = soup1.select('div.title')
for i in wz:
wzs.append(i.contents[0].get("href"))
time.sleep(1)
except:
print('公司正在审核中')
urls = ['http://www.sooshong.com/c-3p{}'.format(num) for num in range(k, k + 50)]
# 利用并发加速爬取，最大线程为50个，本文章中一共有50个网站，可以加入50个线程
# 建立一个加速器对象，线程数每个网站都不同，太大网站接受不了会造成数据损失
executor = ThreadPoolExecutor(max_workers=10)
# submit()的参数： 第一个为函数， 之后为该函数的传入参数，允许有多个
future_tasks = [executor.submit(parser, url) for url in urls]
# 等待所有的线程完成，才进入后续的执行
wait(future_tasks, return_when=ALL_COMPLETED)
print('子页链接抓取完毕！')
########################################################################################################################
# 使用并发法爬取详细页链接
# 定义函数获取每个网页需要爬取的内容
wzs1 = []
def parser(url):
# 利用正则表达式解析网页
try:
res = requests.get(url, headers=headers)
# 对响应体进行解析
soup = BeautifulSoup(res.text, "lxml")
# 找到页面子链接，进入子页面，对子页面进行抓取
# 用select函数抽取需要的内容，单击需要的内容》检查》copy select
lianjie = soup.select('#main > div.main > div.intro > div.intros > div.text > p > a')
lianjie = lianjie[0].get('href')
wzs1.append(lianjie)
print(lianjie)
except:
print('子页解析失败')
# 利用并发加速爬取，最大线程为50个，本文章中一共有50个网站，可以加入50个线程
# 建立一个加速器对象，线程数每个网站都不同，太大网站接受不了会造成数据损失
executor = ThreadPoolExecutor(max_workers=10)
# submit()的参数： 第一个为函数， 之后为该函数的传入参数，允许有多个
future_tasks = [executor.submit(parser, url) for url in wzs]
# 等待所有的线程完成，才进入后续的执行
wait(future_tasks, return_when=ALL_COMPLETED)
print('详细页链接获取完毕！')
"""
# 使用异步法抓取子页面的链接
########################################################################################################################
async def get_html(sess, ur):
try:
proxy_auth = aiohttp.BasicAuth('zhaoshuang', 'LINA5201314')
html = await sess.get(ur,
headers=headers) # , proxy='http://'+'14.116.200.33:28803', proxy_auth=proxy_auth)
r = await html.text()
return r
except:
print("error")
# f = requests.get('http://211775.sooshong.com', headers=headers)
wzs1 = []
# 解析网页
async def parser(respo):
# 利用正则表达式解析网页
try:
# 对响应体进行解析
soup = BeautifulSoup(respo, "lxml")
# 找到页面子链接，进入子页面，对子页面进行抓取
# 用select函数抽取需要的内容，单击需要的内容》检查》copy select
lianjie = soup.select('#main > div.main > div.intro > div.intros > div.text > p > a')
lianjie = lianjie[0].get('href')
wzs1.append(lianjie)
print(lianjie)
company = soup.select("#main > div.aside > div.info > div.info_c > p:nth-child(1) > strong") # 标题
company = company[0].text
# 匹配电话号码
dianhua = soup.select("#main > div.aside > div.info > div.info_c > p:nth-child(3)") # 地址
dianhua = dianhua[0].text.split("：")[1]
# 匹配手机号码
phone = soup.select("#main > div.aside > div.info > div.info_c > p:nth-child(4)") # 日租价格
phone = phone[0].text.split("：")[1]
# 匹配传真
chuanzhen = soup.select("#main > div.aside > div.info > div.info_c > p:nth-child(5)") # 月租价格
chuanzhen = chuanzhen[0].text.split("：")[1]
# 经营模式
jingying = soup.select("#main > div.aside > div.info > div.info_c > p:nth-child(8)") # 面积大小
jingying = jingying[0].text.split("：")[1]
# 公司地址
address = soup.select('#main > div.aside > div.info > div.info_c > p:nth-child(9)') # 抽取建造年份
address = address[0].text.split("：")[1]
# 公司简介
# introduction = soup.select("#main > div.main > div.intro > div.intros > div.text > p") # 楼层属性
# introduction = introduction[0].text.strip()
data = [company, address, dianhua, phone, chuanzhen, jingying]
print(data)
with open('首富网企业7.csv', 'a+', newline='', encoding='GB2312', errors='ignore') as csvfile:
w1 = csv.writer(csvfile)
w1.writerow(data, [1])
except:
print("出错！")
async def main(loop):
async with aiohttp.ClientSession() as sess:
tasks = []
for ii in wzs:
ur = ii
try:
tasks.append(loop.create_task(get_html(sess, ur)))
except:
print('error')
# 设置0.1的网络延迟增加爬取效率
await asyncio.sleep(0.1)
finished, unfinised = await asyncio.wait(tasks)
for i1 in finished:
await parser(i1.result())
if __name__ == '__main__':
t1 = time.time()
loop = asyncio.get_event_loop()
loop.run_until_complete(main(loop))
print("花费时间", time.time() - t1)
print('详细页链接抓取完毕！')
"""
########################################################################################################################
# 使用并发法获取详细页的内容
########################################################################################################################
# 定义函数获取每个网页需要爬取的内容
def parser(url):
global data
try:
res = requests.get(url, headers=headers)
# 对响应体进行解析
soup = BeautifulSoup(res.text, 'lxml')
# 找到页面子链接，进入子页面，对子页面进行抓取
# 用select函数抽取需要的内容，单击需要的内容》检查》copy select
company = soup.select('#main > div.aside > div.info > div.info_c > p:nth-child(1) > strong')
company = company[0].text
name = soup.select('#main > div.aside > div.info > div.info_c > p:nth-child(2)')
name = name[0].text
dianhua = soup.select('#main > div.aside > div.info > div.info_c > p:nth-child(3)')
dianhua = dianhua[0].text.split('：')[1]
shouji = soup.select('#main > div.aside > div.info > div.info_c > p:nth-child(4)')
shouji = shouji[0].text.split('：')[1]
chuanzhen = soup.select('#main > div.aside > div.info > div.info_c > p:nth-child(5)')
chuanzhen = chuanzhen[0].text.split('：')[1]
product = soup.select('tr:nth-child(1) > td:nth-child(2)')
product = product[0].text
company_type = soup.select('tr:nth-child(2) > td:nth-child(2) > span')
company_type = company_type[0].text.strip()
legal_person = soup.select('tr:nth-child(3) > td:nth-child(2)')
legal_person = legal_person[0].text
main_address = soup.select('tr:nth-child(5) > td:nth-child(2) > span')
main_address = main_address[0].text
brand = soup.select('tr:nth-child(6) > td:nth-child(2) > span')
brand = brand[0].text
area = soup.select('tr:nth-child(9) > td:nth-child(2) > span')
area = area[0].text
industry = soup.select('tr:nth-child(1) > td:nth-child(4)')
industry = industry[0].text.strip()
address = soup.select('#main > div.aside > div.info > div.info_c > p:nth-child(9)')
address = address[0].text.split('：')[1]
jingying = soup.select('#main > div.aside > div.info > div.info_c > p:nth-child(8)')
jingying = jingying[0].text.split('：')[1]
date = soup.select('tr:nth-child(5) > td:nth-child(4) > span')
date = date[0].text
wangzhi = soup.select('tr:nth-child(12) > td:nth-child(4) > p > span > a')
wangzhi = wangzhi[0].text
data = [company, date, name, legal_person, shouji, dianhua, chuanzhen, company_type, jingying, industry,
product, wangzhi, area, brand, address, main_address] # 将以上数据放入列表中打印在命令框
print(data)
with open('服装1.csv', 'a', newline='', encoding='GB2312') as csvfile:
w1 = csv.writer(csvfile)
w1.writerow(data)
except:
with open('服装2.csv', 'a', newline='', encoding='utf-8-sig') as csvfile:
w1 = csv.writer(csvfile)
w1.writerow(data)
print('utf解码成功')
# 利用并发加速爬取，最大线程为50个，本文章中一共有50个网站，可以加入50个线程
# 建立一个加速器对象，线程数每个网站都不同，太大网站接受不了会造成数据损失
executor = ThreadPoolExecutor(max_workers=10)
# submit()的参数： 第一个为函数， 之后为该函数的传入参数，允许有多个
future_tasks = [executor.submit(parser, url) for url in wzs1]
# 等待所有的线程完成，才进入后续的执行
wait(future_tasks, return_when=ALL_COMPLETED)
print('全部信息抓取完毕')
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