初识爬虫

昨晚，有了试试爬虫项目的想法。

总感觉光学基础语法知识不实战，有点纸上谈兵。既然想到了，那么就说干就干，在网上找了一些爬虫的资料，仔细阅读一番，算是做了初步了解。现在及时把这些想法记录下来，以备日后完善。

所谓爬虫，简单的说，就是利用计算机程序代替人，自动获取想要的互联网信息，并保存下来。简言之，就是网络机器人。

今天整理的是一个简易爬取拉钩网职位信息的程序。也就模仿书上的程序，自己做了一点总结，估计连入门都还不算，任重道远啊。

以下是拉钩网Python职位信息截图：

从拉钩网网站截图可以看出，Python职位信息一共有30页，那么我们就将这30页中标注了的数据将其保存至EXCEL中，以便我们对比，进行数据分析。

以下是程序：

# Author: Lucas w
#Python3.4 
import requests
import xlwt
headers = {
"User-Agent":"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36",#访问终端信息
"Referer":"https://www.lagou.com/jobs/list_python?city=%E5%8C%97%E4%BA%AC&cl=false&fromSearch=true&labelWords=&suginput=",#从哪个页面跳转过去
"Cookie":"_gat=1; user_trace_token=20170923145813-910b5327-a02c-11e7-9257-5254005c3644; PRE_UTM=m_cf_cpt_baidu_pc; PRE_HOST=bzclk.baidu.com; PRE_SITE=http%3A%2F%2Fbzclk.baidu.com%2Fadrc.php%3Ft%3D06KL00c00f7Ghk60t8km0FNkUs0KK_uu00000aFBTNb00000LJ63sM.THL0oUh11x60UWdBmy-bIfK15yRsuyn1P1F9nj0sPW6vPWR0IHY4wHD3wbRkfYc3nHDYPWNjPYm1PRR4PRfLnHnYnH-jf6K95gTqFhdWpyfqn10YP1c3rjT4PiusThqbpyfqnHm0uHdCIZwsT1CEQLILIz4_myIEIi4WUvYE5LNYUNq1ULNzmvRqUNqWu-qWTZwxmh7GuZNxTAn0mLFW5HmYPjDz%26tpl%3Dtpl_10085_15730_11224%26l%3D1500602914%26attach%3Dlocation%253D%2526linkName%253D%2525E6%2525A0%252587%2525E9%2525A2%252598%2526linkText%253D%2525E3%252580%252590%2525E6%25258B%252589%2525E5%25258B%2525BE%2525E7%2525BD%252591%2525E3%252580%252591%2525E5%2525AE%252598%2525E7%2525BD%252591-%2525E4%2525B8%252593%2525E6%2525B3%2525A8%2525E4%2525BA%252592%2525E8%252581%252594%2525E7%2525BD%252591%2525E8%252581%25258C%2525E4%2525B8%25259A%2525E6%25259C%2525BA%2526xp%253Did%28%252522m26faf71c%252522%29%25252FDIV%25255B1%25255D%25252FDIV%25255B1%25255D%25252FDIV%25255B1%25255D%25252FDIV%25255B1%25255D%25252FH2%25255B1%25255D%25252FA%25255B1%25255D%2526linkType%253D%2526checksum%253D139%26ie%3Dutf-8%26f%3D3%26tn%3Dbaidu%26wd%3D%25E6%258B%2589%25E9%2592%25A9%25E7%25BD%2591%26rqlang%3Dcn%26inputT%3D8920%26prefixsug%3D%2525E6%25258B%252589%2525E9%252592%2525A9%2525E7%2525BD%252591%26rsp%3D0; PRE_LAND=https%3A%2F%2Fwww.lagou.com%2F%3Futm_source%3Dm_cf_cpt_baidu_pc; LGUID=20170923145813-910b5b3d-a02c-11e7-9257-5254005c3644; JSESSIONID=ABAAABAACDBAAIA11C89460F812ACB58846CB390ADC7396; _putrc=AAD00F92B2209827; login=true; unick=%E7%8E%8B%E7%90%9B; showExpriedIndex=1; showExpriedCompanyHome=1; showExpriedMyPublish=1; hasDeliver=0; TG-TRACK-CODE=index_search; _ga=GA1.2.1599245716.1506149896; _gid=GA1.2.950954961.1506149896; Hm_lvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1506149897; Hm_lpvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1506150019; LGSID=20170923145813-910b586a-a02c-11e7-9257-5254005c3644; LGRID=20170923150015-da0be6de-a02c-11e7-9257-5254005c3644; SEARCH_ID=8e1052bf383f45b5a37cc5c103f4b78c; index_location_city=%E5%8C%97%E4%BA%AC"#cookie
}
def getjoblist(page):
    data={
        "first": "false",
        "pn": page,
        "kd": "python"
    }#数据信息 page代表页数
    res = requests.post("https://www.lagou.com/jobs/positionAjax.json?city=%E5%8C%97%E4%BA%AC&needAddtionalResult=false&isSchoolJob=0",headers=headers,data=data)
    result = res.json()
    jobs=result["content"]["positionResult"]["result"]
    return jobs

w=xlwt.Workbook()
sheet1=w.add_sheet("sheet1",cell_overwrite_ok=True)
n=0
for page in range(1,31):
    for job in getjoblist(page=page):
        sheet1.write(n, 0, job["positionName"])
        sheet1.write(n, 1, job["companyFullName"])
        sheet1.write(n, 2, job["businessZones"])
        sheet1.write(n, 3, job["education"])
        sheet1.write(n, 4, job["workYear"])
        sheet1.write(n, 5, job["salary"])
        n+=1
w.save("job.xls")