确定网页的加载方式是JavaScript加载
通过谷歌浏览器开发者工具分析和寻找网页的真实请求，确定真实数据在position.Ajax开头的链接里，请求方式是POST
使用requests的post方法获取数据，发现并没有返回想要的数据，说明需要加上headers和每隔多长时间爬取
我们可以看到拉勾网列表页的信息一般js加载的都在xhr和js中，通过发送ajax加载POST请求，获取页面信息。
这个是ajax的头信息，通过Form Data中的的信息获取页面
下面是scrapy爬虫的 代码部分
1 importscrapy2 importjson3 from lagou.items importLagouItem4 classLagoupositionSpider(scrapy.Spider):5 name = 'lagouposition'
6 allowed_domains = ['']7 kd = input('请输入你要搜索的职位信息:')8 ct =input('请输入要搜索的城市信息')9 page=1
10 start_urls = ["/jobs/list_"+str(kd)+"&city="+str(ct)]11 headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36",12 'Referer': '/jobs/list_'+str(kd)+'?labelWords=&fromSearch=true&suginput=',13 'Cookie':'_ga=GA1.2.1036647455.1532143907; user_trace_token=0721113217-aacd6291-8c96-11e8-a020-525400f775ce; LGUID=0721113217-aacd667e-8c96-11e8-a020-525400f775ce; index_location_city=%E5%8C%97%E4%BA%AC; _gid=GA1.2.1320510576.1532272161; WEBTJ-ID=0723084204-164c4960832159-09bf89fcd2732e-5e442e19-1049088-164c496083348; JSESSIONID=ABAAABAABEEAAJAC7D58B57D1CAE4616ED47AACF945615E; _gat=1; LGSID=0723203627-04b27de6-8e75-11e8-9ee6-5254005c3644; PRE_UTM=; PRE_HOST=; PRE_SITE=https%3A%2F%%2Flink%3Furl%3DYhfCtaCVlOHCdncJxMCMMS3PB1wGlwfw9Yt2c_FXqgu%26wd%3D%26eqid%3D8f013ed00002f4c7000000035b55cbc4; PRE_LAND=https%3A%2F%%2F; Hm_lvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1532306722,1532306725,1532306732,1532349358; SEARCH_ID=cdd7822cf3e2429fbc654720657d5873; LGRID=0723203743-3221dec8-8e75-11e8-a35a-525400f775ce; Hm_lpvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1532349434; TG-TRACK-CODE=search_code'
14 }15
16
17 defparse(self, response):18 with open('lagou.html','w') as f:19 f.write(response.text)20 url="/jobs/positionAjax.json?needAddtionalResult=false"
21 formdata={'first':'true','kd':str(self.kd),'pn':'1','city':str(self.ct)}22 yield scrapy.FormRequest(url,formdata=formdata,callback=self.parse_detail,headers=self.headers)23
24 defparse_detail(self,response):25 text=json.loads(response.text)26 res=[]27 try:28 res = text["content"]["positionResult"]["result"]29 print(res)30 except:31 pass
32 if len(res)>0:33 item =LagouItem()34 for position inres:35 try:36 item['title']=position['positionName']37 item['education']=position['education']38 item['company']=position['companyFullName']39 item['experience']=position['workYear']40 item['location']=position['city']41 item['salary'] = position['salary']42 print(item)43 except:44 pass
45 yielditem46 self.page+=1
47 url='/jobs/positionAjax.json?needAddtionalResult=false
48 formdata={'first':'false','kd':str(self.kd),'pn':str(self.page),'city':str(self.ct)}49 print('===========================',formdata)50 yield scrapy.FormRequest(url, callback=self.parse_detail, formdata=formdata,headers=self.headers)51 else:52 print("爬取结束！")
注意拉钩网有反爬措施， 我们在Formreqest提交POST请求消息必须携带kd等键值对，在setting中也许设置
1 DOWNLOAD_DELAY = 20
2 #设置爬取时间
3 ROBOTSTXT_OBEY =False4 #是否遵循发爬虫协议
5 DEFAULT_REQUEST_HEADERS ={6 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',7 'Accept-Language': 'zh-CN,zh;q=0.8',8 'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',9 'Host': '',10 'Origin': '',11 'Referer': '/jobs',12 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36',13 'X-Anit-Forge-Code': '0',14 'X-Anit-Forge-Token': 'None',15 'X-Requested-With': 'XMLHttpRequest'
16 }17 #请求头信息headers
接下来就是在items中设置爬取信息的字段
1 importscrapy2
3
4 classLagouItem(scrapy.Item):5 #define the fields for your item here like:
6 #name = scrapy.Field()
7 #pass
8
9 education=scrapy.Field()10 company=scrapy.Field()11 experience=scrapy.Field()12 location=scrapy.Field()13 salary=scrapy.Field()14 title= scrapy.Field()
在Pipeline.py文件中设置保存爬取文件的格式等
1 importjson2 classLagouPipeline(object):3 defopen_spider(self,spider):4 self.file=open('pythonposition.json','w',encoding='utf-8')5 defprocess_item(self, item, spider):6 python_dict=dict(item)7 content=json.dumps(python_dict,ensure_ascii=False)+'\n'
8 self.file.write(content)9 returnitem10 defclose_spider(self,spider):11 self.file.close()
注意一定要把setting中的ITEM_PIPELINES解注释，接下来就是跑起我们的项目，通过input输入想要爬取的职位和城市，
上面就是爬取到的信息总共是855条招聘消息，接下来就是用jumpter-notebook打开爬取到的csv文件用pandas,numpy,和mupltlib进行分析
1 importpandas as pd2 importnumpy as np3 importseaborn as sns4 lagou=pd.read_csv('./examples/lagou.csv')5 lagou.info()6 #查看缺失值情况
通过读取文件并显示出855条招聘信息是否有缺失值
1 city=lagou['location']2 city=pd.DataFrame(city.unique())3 city
通过上面可以看到招聘python职位的城市，总共有38城市
1 education=lagou['education']2 education=pd.DataFrame(education.unique())3 lagou['education'] = lagou['education'].replace('不限','unlimited')4 lagou['education'] = lagou['education'].replace('大专','junior')5 lagou['education'] = lagou['education'].replace('本科','regular')6 lagou['education'] = lagou['education'].replace('硕士','master')7 lagou['education'] = lagou['education'].replace('博士','doctor')8 #seaborn不支持中文需将对应的中文替换
9 importseaborn as sns10 sns.set_style('whitegrid')11 sns.countplot(x='education',data=lagou,palette='Greens_d')
通过上图可以看到大多数的Python职位招聘还是本科学历为主
1 experience=lagou['experience']2 experience=pd.DataFrame(experience.unique())3 lagou['experience'] = lagou['experience'].replace('不限','unlimited')4 lagou['experience'] = lagou['experience'].replace('3-5年','3-5')5 lagou['experience'] = lagou['experience'].replace('1-3年','1-3')6 lagou['experience'] = lagou['experience'].replace('5-','5-10')7 lagou['experience'] = lagou['experience'].replace('1年以下','<1')8 lagou['experience'] = lagou['experience'].replace('应届毕业生','intern')9 experience10 sns.countplot(x="experience", data=lagou,palette="Blues_d")
上图是招聘的工作经验的人数分布图，可以看到3-5年的Python工程师比较抢手，其次就是1-3年工作经验的
1 importmatplotlib.pyplot as plt2 %matplotlib inline3 f, ax1= plt.subplots(figsize=(20,20))4 sns.countplot(y='salary', data=lagou, ax=ax1)5 ax1.set_title('Python salary distribute',fontsize=15)6 #薪资分布
7 ax1.set_xlabel('salary')8 #薪资
9 ax1.set_ylabel('level')10 plt.show()
同过下图可以看到拉勾网上的pyhong工程师薪资待遇，其中待遇重要分布在10-40K之间，其中给出15-30K工资待遇的企业最多
Python工程师还是很有前景的，
