Python爬取前程无忧十万条招聘数据
前言:本文是介绍利用代理IP池以及多线程完成前程无忧网站的是十万条招聘信息的采集工作,已适当控制采集频率,采集数据仅为了学习使用,采集十万条招聘信息大概需要十个小时。
起因是在知乎上看到另一个程序猿写的前程无忧的爬虫代码,对于他的一些反反爬虫处理措施抱有一丝怀疑态度,于是在他的代码的基础上进行改造,优化了线程的分配以及页面访问的频率,并加入了代理IP池的处理,优化了爬虫效率。
原始代码文章链接:https://zhuanlan.zhihu.com/p/146425439
首先,奉上本文依赖的基础的爬虫代码
def getdata(bot,top):
for i in range(bot,top):
print("正在爬取第" + str(i) + "页的数据")
url0 = "https://search.51job.com/list/000000,000000,0000,00,9,99,%25E6%2595%25B0%25E6%258D%25AE,2,"
url_end = ".html?"
url = url0 + str(i) + url_end
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36'
}
html = requests.get(url, headers=headers)
html.encoding = "gbk"
etree = etree.HTML(html.text)
# ①岗位名称
JobName = etree.xpath('//div[@class="dw_table"]/div[@class="el"]//p/span/a[@target="_blank"]/@title')
# ②公司名称
CompanyName = etree.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t2"]/a[@target="_blank"]/@title')
# ③工作地点
Address = etree.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t3"]/text()')
# ④工资
sal = etree.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t4"]')
salary = [i.text for i in sal]
# ⑤发布时间
ShowTime = etree.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t5"]/text()')
# ⑥获取职位详情url
DetailUrl = etree.xpath('//div[@class="dw_table"]/div[@class="el"]//p/span/a[@target="_blank"]/@href')
OthersInfo = []
JobDescribe = []
CompanyType = []
CompanySize = []
Industry = []
for i in range(len(DetailUrl)):
htmlInfo = requests.get(DetailUrl[i], headers=headers)
htmlInfo.encoding = "gbk"
etreeInfo = etree.HTML(htmlInfo.text)
# ⑦经验、学历信息等其他信息
otherinfo = etreeInfo.xpath('//div[@class="tHeader tHjob"]//div[@class="cn"]/p[@class="msg ltype"]/text()')
# ⑧岗位详情
JobDescibe = etreeInfo.xpath('//div[@class="tBorderTop_box"]//div[@class="bmsg job_msg inbox"]/p/text()')
# ⑨公司类型
CompanyType = etreeInfo.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[1]/@title')
# ⑩公司规模(人数)
CompanySize = etreeInfo.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[2]/@title')
# ⑪所属行业(公司)
industry = etreeInfo.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[3]/@title')
#将上述信息存入列表中
OthersInfo.append(otherinfo)
JobDescribe.append(JobDescibe)
CompanyType.append(CompanyType)
CompanySize.append(CompanySize)
Industry.append(industry)
# 休眠
time.sleep(0.5)
# 一边爬取一边写入
data = pd.DataFrame()
data["岗位名称"] = JobName
data["工作地点"] = Address
data["公司名称"] = CompanyName
data["工资"] = salary
data["发布日期"] = ShowTime
data["经验、学历"] = OthersInfo
data["所属行业"] = Industry
data["公司类型"] = CompanyType
data["公司规模"] = CompanySize
data["岗位描述"] = JobDescribe
# 有些网页会跳转到公司官网,会返回空值,所以将其忽略
try:
data.to_csv("job_info.csv", mode="a+", header=None, index=None, encoding="gbk")
except:
print("跳转官网,无数据")
time.sleep(1)
print("数据爬取完成!!!!")
经过实验,发现这段代码存在以下几个问题,1.爬虫的效率低;2.爬虫的过程中报错有点频繁;3.访问网页的延时时间都是固定的,这样很容易被网站识别到
首先,解决第一个问题,原作者的解决方案是以多线程的方式处理,代码如下
import requests,time,warnings,threading
import pandas as pd
from lxml import etree
warnings.filterwarnings("ignore")
def getdata(bot,top):
for i in range(bot,top):
print("正在爬取第" + str(i) + "页的数据")
url0 = "https://search.51job.com/list/000000,000000,0000,00,9,99,%25E6%2595%25B0%25E6%258D%25AE,2,"
url_end = ".html?"
url = url0 + str(i) + url_end
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36'
}
html = requests.get(url, headers=headers)
html.encoding = "gbk"
Html = etree.HTML(html.text)
# ①岗位名称
JobName = Html.xpath('//div[@class="dw_table"]/div[@class="el"]//p/span/a[@target="_blank"]/@title')
# ②公司名称
CompanyName = Html.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t2"]/a[@target="_blank"]/@title')
# ③工作地点
Address = Html.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t3"]/text()')
# ④工资
sal = Html.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t4"]')
salary = [i.text for i in sal]
# ⑤发布时间
ShowTime = Html.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t5"]/text()')
# ⑥获取职位详情url
DetailUrl = Html.xpath('//div[@class="dw_table"]/div[@class="el"]//p/span/a[@target="_blank"]/@href')
OthersInfo = []
JobDescribe = []
CompanyType = []
CompanySize = []
Industry = []
for i in range(len(DetailUrl)):
HtmlInfo = requests.get(DetailUrl[i], headers=headers)
HtmlInfo.encoding = "gbk"
HtmlInfo = etree.HTML(HtmlInfo.text)
# ⑦经验、学历信息等其他信息
otherinfo = HtmlInfo.xpath('//div[@class="tHeader tHjob"]//div[@class="cn"]/p[@class="msg ltype"]/text()')
# ⑧岗位详情
JobDescibe = HtmlInfo.xpath('//div[@class="tBorderTop_box"]//div[@class="bmsg job_msg inbox"]/p/text()')
# ⑨公司类型
ComType = HtmlInfo.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[1]/@title')
# ⑩公司规模(人数)
ComSize = HtmlInfo.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[2]/@title')
# ⑪所属行业(公司)
industry = HtmlInfo.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[3]/@title')
#将上述信息存入列表中
OthersInfo.append(otherinfo)
JobDescribe.append(JobDescibe)
CompanyType.append(ComType)
CompanySize.append(ComSize)
Industry.append(industry)
# 休眠
time.sleep(0.5)
# 一边爬取一边写入
data = pd.DataFrame()
data["岗位名称"] = JobName
data["工作地点"] = Address
data["公司名称"] = CompanyName
data["工资"] = salary
data["发布日期"] = ShowTime
data["经验、学历"] = OthersInfo
data["所属行业"] = Industry
data["公司类型"] = CompanyType
data["公司规模"] = CompanySize
data["岗位描述"] = JobDescribe
# 有些网页会跳转到公司官网,会返回空值,所以将其忽略
try:
data.to_csv("job_info.csv", mode="a+", header=None, index=None, encoding="gbk")
except:
print("跳转官网,无数据")
time.sleep(1)
print("数据爬取完成!!!!")
threads = []
t1 = threading.Thread(target=getdata,args=(1,125))
threads.append(t1)
t2 = threading.Thread(target=getdata,args=(125,250))
threads.append(t2)
t3 = threading.Thread(target=getdata,args=(250,375))
threads.append(t3)
t4 = threading.Thread(target=getdata,args=(375,500))
threads.append(t4)
t5 = threading.Thread(target=getdata,args=(500,625))
threads.append(t5)
t6 = threading.Thread(target=getdata,args=(625,750))
threads.append(t6)
t7 = threading.Thread(target=getdata,args=(750,875))
threads.append(t7)
t8 = threading.Thread(target=getdata,args=(875,1000))
threads.append(t8)
t9 = threading.Thread(target=getdata,args=(1000,1125))
threads.append(t9)
t10 = threading.Thread(target=getdata,args=(1125,1250))
threads.append(t10)
t11 = threading.Thread(target=getdata,args=(1250,1375))
threads.append(t11)
t12 = threading.Thread(target=getdata,args=(1375,1500))
threads.append(t12)
if __name__ == "__main__":
for t in threads:
t.setDaemon(True)
t.start()
确实增加了爬虫的速度,但这样做会有一个问题,就是爬虫的质量变差了,准确的说就是出错的几率提高了,被反爬虫策略识别到的次数增加了
首先从代码生成的角度,我优化了一下多线程的生成方法,允许用户自定义线程数作为参数传递,通过总的页数进行均分,如下所示
# 分配线程任务
def start_spider(num):
start = 1
end = 0
count = 2000
size = count//(num-1)
print(size)
while num > 1:
end = start+size
t = threading.Thread(target=getdata,args=(start,end))
start = end+1
t.start()
num = num-1
# 分配剩下的任务给新的线程
if(end < count):
start = end+1
end = count
t = threading.Thread(target=getdata,args=(start,end))
t.start()
代码优化了之后,我们调整下爬虫时页面访问的延迟,改为一个随机数
Industry.append(industry)
# 休眠
time.sleep(random.uniform(0.1,1))
# 一边爬取一边写入
data = pd.DataFrame()
data["岗位名称"] = JobName
data["工作地点"] = Address
data["公司名称"] = CompanyName
data["工资"] = salary
data["发布日期"] = ShowTime
data["经验、学历"] = OthersInfo
data["所属行业"] = Industry
data["公司类型"] = CompanyType
data["公司规模"] = CompanySize
data["岗位描述"] = JobDescribe
# 有些网页会跳转到公司官网,会返回空值,所以将其忽略
try:
data.to_csv("job_info.csv", mode="a+", header=None, index=None, encoding="gbk")
except:
print("跳转官网,无数据")
time.sleep(random.uniform(0.2,0.5))
最后利用代理IP池的方式来提高爬虫的质量
这里我分享一个很好用的代理IP池项目:https://github.com/jhao104/proxy_pool
这个项目在我等会分享的gitee开源项目中也拷贝了一份:https://gitee.com/chengrongkai/OpenSpiders
配置IP代理池的方法就参考这个项目的readme就行了
下面我奉上我对这个项目的代码改造
# 利用代理IP请求
def getHtml(url):
# ....
retry_count = 5
proxy = get_proxy().get("proxy")
while retry_count > 0:
try:
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36'
}
print("代理信息:{}".format(proxy))
html = requests.get(url,headers=headers, proxies={"http": "http://{}".format(proxy)})
# 使用代理访问
return html
except Exception:
retry_count -= 1
# 出错5次, 删除代理池中代理
delete_proxy(proxy)
return None
def getdata(bot,top):
for i in range(bot,top):
print("正在爬取第" + str(i) + "页的数据")
url0 = "https://search.51job.com/list/000000,000000,0000,00,9,99,%25E6%2595%25B0%25E6%258D%25AE,2,"
url_end = ".html?"
url = url0 + str(i) + url_end
html = getHtml(url)
if(html == None):
continue
html.encoding = "gbk"
Html = etree.HTML(html.text)
# ①岗位名称
JobName = Html.xpath('//div[@class="dw_table"]/div[@class="el"]//p/span/a[@target="_blank"]/@title')
# ②公司名称
CompanyName = Html.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t2"]/a[@target="_blank"]/@title')
# ③工作地点
Address = Html.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t3"]/text()')
# ④工资
sal = Html.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t4"]')
salary = [i.text for i in sal]
# ⑤发布时间
ShowTime = Html.xpath('//div[@class="dw_table"]/div[@class="el"]/span[@class="t5"]/text()')
# ⑥获取职位详情url
DetailUrl = Html.xpath('//div[@class="dw_table"]/div[@class="el"]//p/span/a[@target="_blank"]/@href')
OthersInfo = []
JobDescribe = []
CompanyType = []
CompanySize = []
Industry = []
for i in range(len(DetailUrl)):
HtmlInfo = getHtml(DetailUrl[i])
HtmlInfo.encoding = "gbk"
HtmlInfo = etree.HTML(HtmlInfo.text)
if(HtmlInfo == None):
continue
# ⑦经验、学历信息等其他信息
otherinfo = HtmlInfo.xpath('//div[@class="tHeader tHjob"]//div[@class="cn"]/p[@class="msg ltype"]/text()')
# ⑧岗位详情
JobDescibe = HtmlInfo.xpath('//div[@class="tBorderTop_box"]//div[@class="bmsg job_msg inbox"]/p/text()')
# ⑨公司类型
ComType = HtmlInfo.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[1]/@title')
# ⑩公司规模(人数)
ComSize = HtmlInfo.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[2]/@title')
# ⑪所属行业(公司)
industry = HtmlInfo.xpath('//div[@class="tCompany_sidebar"]//div[@class="com_tag"]/p[3]/@title')
#将上述信息存入列表中
OthersInfo.append(otherinfo)
JobDescribe.append(JobDescibe)
CompanyType.append(ComType)
CompanySize.append(ComSize)
Industry.append(industry)
# 休眠
time.sleep(random.uniform(0.1,1))
# 一边爬取一边写入
data = pd.DataFrame()
data["岗位名称"] = JobName
data["工作地点"] = Address
data["公司名称"] = CompanyName
data["工资"] = salary
data["发布日期"] = ShowTime
data["经验、学历"] = OthersInfo
data["所属行业"] = Industry
data["公司类型"] = CompanyType
data["公司规模"] = CompanySize
data["岗位描述"] = JobDescribe
# 有些网页会跳转到公司官网,会返回空值,所以将其忽略
try:
data.to_csv("job_info.csv", mode="a+", header=None, index=None, encoding="gbk")
except:
print("跳转官网,无数据")
time.sleep(random.uniform(0.2,0.5))
print("数据爬取完成!!!!")
我自己的机器测试了下,8个线程爬取了一个半小时,采集了一万五的数据,这里我有意的降慢了速度,大家可以根据实际情况进行调整,比如代理IP的重试可以去掉,如果出现无法采集就直接删除代理IP池中的该IP即可,另外线程数也可以按照电脑配置适当增加,在不计质量的情况下,应该可以达到一个小时一万五左右的采集量,单机的情况下,如果有更好的解决方案,欢迎留言,下篇文章将讲述如何对获取到的数据进行清洗以及数据分析。
采集到的数据如下
本文所有代码均开源在https://gitee.com/chengrongkai/OpenSpiders
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