【2019.05】python 爬取拉钩数据（静态+动态）

ゞ浴缸里的玫瑰 2022-02-04 05:53 283阅读 0赞

拉钩这个网站有点意思，页面加载有两种方法  
点击首页的标签，页面是静态加载的  
直接搜索，页是动态加载的  
两种爬取方式不一样，这里都实现了。

*  动态爬取时会出现 ‘操作太频繁，请稍后再试’字样，出现这种情况是cookie的问题，解决方案详见代码

# 静态爬取 #

> [https://www.lagou.com/zhaopin/jiqixuexi/?labelWords=label][https_www.lagou.com_zhaopin_jiqixuexi_labelWords_label]

#!/usr/bin/env python
    # -*- coding: utf-8 -*-
    # @Time    : 2019/5/6 21:12
    # @Author  : Paulson
    # @File    : Spider_jingtai.py
    # @Software: PyCharm
    # @define  : function
    
    
    import random
    import time
    
    import pandas as pd
    import requests
    from lxml import etree
    
    # 真实cookie
    # Cookie = 'xx‘’
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36',
        # 'Cookie': Cookie
    }
    
    
    for i in range(1, 2):
        time.sleep(random.randint(3, 10))
        url = 'https://www.lagou.com/zhaopin/jiqixuexi/{}/?filterOption=2'.format(i)
        print(url)
        print('正在抓取第{}页数据...'.format(i))
    
        # 请求网页并解析
        con = etree.HTML(requests.get(url, headers=headers).text)
    
        # 使用xpath表达式抽取各个字段信息
        job_name = [i for i in con.xpath('//a[@class="position_link"]/h3/text()')]
        print(job_name)
        job_address = [i for i in con.xpath("//a[@class='position_link']/span/em/text()")]
        job_company = [i for i in con.xpath("//div[@class='company_name']/a/text()")]
        job_salary = [i for i in con.xpath("//span[@class='money']/text()")]
        job_exp_edu = [i for i in con.xpath("//div[@class='li_b_l']/text()")]
        job_exp_edu2 = [i for i in [i.strip() for i in job_exp_edu] if i != '']
        job_industry = [i for i in con.xpath("//div[@class='industry']/text()")]
        job_tempation = [i for i in con.xpath("//div[@class='list_item_bot']/div[@class='li_b_r']/text()")]
        job_links = [i for i in con.xpath("//div[@class='p_top']/a/@href")]
        print(job_links)
        # 获取详情页链接后采集详情页岗位描述信息
        job_des = []
        for link in job_links:
            time.sleep(random.randint(3, 10))
            con2 = etree.HTML(requests.get(url=link, headers=headers).text)
            des = [[i.xpath('string(.)') for i in con2.xpath("//dd[@class='job_bt']/div/p")]]
            print(des)
            job_des += des
    
    # 对数据进行字典封装
        dataset = {
            '岗位名称': job_name,
            '工作地址': job_address,
            '公司': job_company,
            '薪资': job_salary,
            '经验学历': job_exp_edu2,
            '所属行业': job_industry,
            '岗位福利': job_tempation,
            '任职要求': job_des
        }
    
    # 转化为数据框并存为csv
        data = pd.DataFrame(dataset)
        data.to_csv('machine_learning_hz_job2.csv')

### 结果 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70]

# 动态爬取 #

[https://www.lagou.com/jobs/positionAjax.json?needAddtionalResult=false][https_www.lagou.com_jobs_positionAjax.json_needAddtionalResult_false]

#!/usr/bin/env python
    # -*- coding: utf-8 -*-
    # @Time    : 2019/5/6 22:11
    # @Author  : Paulson
    # @File    : Spider_dongtai.py
    # @Software: PyCharm
    # @define  : function
    
    
    import json
    import random
    import time
    import requests
    from bs4 import BeautifulSoup
    import pandas as pd
    
    #定义抓取主函数
    def lagou_dynamic_crawl():
        headers = {
            'Accept': 'application/json, text/javascript, */*; q=0.01',
            'Referer': 'https://www.lagou.com/jobs/list_%E8%BF%90%E7%BB%B4?city=%E6%88%90%E9%83%BD&cl=false&fromSearch=true&labelWords=&suginput=',
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36'
        }
    
        # 创建一个职位列表容器
        positions = []
        for page in range(1, 31):
            print('正在抓取{}页数据...'.format(page))
            URL_ = 'https://www.lagou.com/jobs/list_%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0?city=%E5%85%A8%E5%9B%BD&cl=false&fromSearch=true&labelWords=&suginput='
            URL = 'https://www.lagou.com/jobs/positionAjax.json?needAddtionalResult=false'
            # 构建请求保单参数
            params = {
                'first': 'false',
                'pn': page,
                'kd': '机器学习'
            }
    
            # 构造请求并返回结果
            s = requests.Session()
            s.get(URL_, headers=headers, timeout=3)  # 请求首页获取cookies
            cookie = s.cookies  # 为此次获取的cookies
            result = s.post(URL, headers=headers, data=params, cookies=cookie, timeout=3)
            print(result.text)
    
            # 将请求结果转化为json
            json_result = result.json()
    
            # 解析json数据获取目标信息
            try:
                position_info = json_result['content']['positionResult']['result']
            except:
                position_info = json_result['msg']
                if '您操作太频繁' in position_info:
                    print('操作太频繁')
                    assert False
    
            # 循环当前页每一个职位信息，再去爬职位详情页面
            for position in position_info:
                # 把我们要爬取的信息放入字典
                position_dict = {
                    'position_name': position['positionName'],
                    'work_year': position['workYear'],
                    'education': position['education'],
                    'salary': position['salary'],
                    'city': position['city'],
                    'company_name': position['companyFullName'],
                    'address': position['businessZones'],
                    'label': position['companyLabelList'],
                    'stage': position['financeStage'],
                    'size': position['companySize'],
                    'advantage': position['positionAdvantage'],
                    'industry': position['industryField'],
                    'industryLables': position['industryLables']
                }
                # 找到职位id
                position_id = position['positionId']
    
                # 根据职位id调用岗位描述函数获取职位描述
    
                position_dict['position_detail'] = recruit_detail(position_id)
                positions.append(position_dict)
    
            time.sleep(random.randint(3, 6))
        print('全部数据采集完毕...')
        return positions
    
    # 定义抓取岗位描述函数
    def recruit_detail(position_id):
        headers = {
            "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3",
            "Accept-Encoding": "gzip, deflate, br",
            "Accept-Language": "zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7",
            "Cache-Control": "max-age=0",
            "Connection": "keep-alive",
            "Host": "www.lagou.com",
            "Referer": "https://www.lagou.com/jobs/list_%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0?labelWords=&fromSearch=true&suginput=",
            "Upgrade-Insecure-Requests": "1",
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36",
        }
        url = 'https://www.lagou.com/jobs/%s.html' % position_id
        result = requests.get(url, headers=headers)
        time.sleep(random.randint(1, 4))
    
        # 解析职位要求text
        soup = BeautifulSoup(result.text, 'html.parser')
        job_bt = soup.find(class_='job_bt')
    
        # 通过尝试发现部分记录描述存在空的情况
        # 所以这里需要判断处理一下
        if job_bt != None:
            job_bt = job_bt.text
        else:
            job_bt = 'null'
        return job_bt
    
    
    if __name__ == '__main__':
        positions = lagou_dynamic_crawl()
        data = pd.DataFrame(positions)
        data.to_csv('machine_learning_hz_job3.csv')

### 结果 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 1]

*  **感谢star**  
    [点击前往GitHub项目][GitHub]  
    [点击前往 jupyter notebook 文件][jupyter notebook]

[https_www.lagou.com_zhaopin_jiqixuexi_labelWords_label]: https://www.lagou.com/zhaopin/jiqixuexi/?labelWords=label
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70]: /images/20220204/5b5c84c4e8754078ba5123173a138daf.png
[https_www.lagou.com_jobs_positionAjax.json_needAddtionalResult_false]: https://www.lagou.com/jobs/positionAjax.json?needAddtionalResult=false
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NTM0MTA3_size_16_color_FFFFFF_t_70 1]: /images/20220204/0852b0be3385483e8736504ee3fdee60.png
[GitHub]: https://github.com/ybsdegit/Machine_Learning_Introduction/tree/master/%E4%B8%89%E6%95%B0%E6%8D%AE%E9%87%87%E9%9B%86%E4%B8%8E%E7%88%AC%E8%99%AB
[jupyter notebook]: https://github.com/ybsdegit/Machine_Learning_Introduction/blob/master/%E4%B8%89%E6%95%B0%E6%8D%AE%E9%87%87%E9%9B%86%E4%B8%8E%E7%88%AC%E8%99%AB/%E6%95%B0%E6%8D%AE%E9%87%87%E9%9B%86%E4%B8%8E%E7%88%AC%E8%99%AB.ipynb