内容简介:网贷数据爬取及分析
关于数据来源
数据爬取
抓包分析
抓包 工具 主要使用chrome的开发者工具 网络一栏,网贷之家的数据全部是ajax返回json数据,而人人贷既有ajax返回数据也有html页面直接生成数据。
请求实例
从数据中可以看到请求数据的方式(GET或者POST),请求头以及请求参数。
从请求数据中可以看到返回数据的格式(此例中为json)、数据结构以及具体数据。
注:这是现在网贷之家的API请求后台的接口,爬虫编写的时候与数据接口与如今的请求接口不一样,所以网贷之家的数据爬虫部分已无效。
构造请求
根据抓包分析得到的结果,构造请求。在本项目中,使用Python的 requests库模拟http请求
具体代码:
import requests class SessionUtil(): def __init__(self,headers=None,cookie=None): self.session=requests.Session() if headers is None: headersStr={"Accept":"application/json, text/javascript, */*; q=0.01", "X-Requested-With":"XMLHttpRequest", "User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36", "Accept-Encoding":"gzip, deflate, sdch, br", "Accept-Language":"zh-CN,zh;q=0.8" } self.headers=headersStr else: self.headers=headers self.cookie=cookie //发送get请求 def getReq(self,url): return self.session.get(url,headers=self.headers).text def addCookie(self,cookie): self.headers['cookie']=cookie //发送post请求 def postReq(self,url,param): return self.session.post(url, param).text
在设置请求头的时候,关键字段只设置了”User-Agent”,网贷之家和人人贷的没有反爬措施,甚至不用设置”Referer”字段来防止跨域错误。
爬虫实例
以下是一个爬虫实例
import json import time from databaseUtil import DatabaseUtil from sessionUtil import SessionUtil from dictUtil import DictUtil from logUtil import LogUtil import traceback def handleData(returnStr): jsonData=json.loads(returnStr) platData=jsonData.get('data').get('platOuterVo') return platData def storeData(jsonOne,conn,cur,platId): actualCapital=jsonOne.get('actualCapital') aliasName=jsonOne.get('aliasName') association=jsonOne.get('association') associationDetail=jsonOne.get('associationDetail') autoBid=jsonOne.get('autoBid') autoBidCode=jsonOne.get('autoBidCode') bankCapital=jsonOne.get('bankCapital') bankFunds=jsonOne.get('bankFunds') bidSecurity=jsonOne.get('bidSecurity') bindingFlag=jsonOne.get('bindingFlag') businessType=jsonOne.get('businessType') companyName=jsonOne.get('companyName') credit=jsonOne.get('credit') creditLevel=jsonOne.get('creditLevel') delayScore=jsonOne.get('delayScore') delayScoreDetail=jsonOne.get('delayScoreDetail') displayFlg=jsonOne.get('displayFlg') drawScore=jsonOne.get('drawScore') drawScoreDetail=jsonOne.get('drawScoreDetail') equityVoList=jsonOne.get('equityVoList') experienceScore=jsonOne.get('experienceScore') experienceScoreDetail=jsonOne.get('experienceScoreDetail') fundCapital=jsonOne.get('fundCapital') gjlhhFlag=jsonOne.get('gjlhhFlag') gjlhhTime=jsonOne.get('gjlhhTime') gruarantee=jsonOne.get('gruarantee') inspection=jsonOne.get('inspection') juridicalPerson=jsonOne.get('juridicalPerson') locationArea=jsonOne.get('locationArea') locationAreaName=jsonOne.get('locationAreaName') locationCity=jsonOne.get('locationCity') locationCityName=jsonOne.get('locationCityName') manageExpense=jsonOne.get('manageExpense') manageExpenseDetail=jsonOne.get('manageExpenseDetail') newTrustCreditor=jsonOne.get('newTrustCreditor') newTrustCreditorCode=jsonOne.get('newTrustCreditorCode') officeAddress=jsonOne.get('officeAddress') onlineDate=jsonOne.get('onlineDate') payment=jsonOne.get('payment') paymode=jsonOne.get('paymode') platBackground=jsonOne.get('platBackground') platBackgroundDetail=jsonOne.get('platBackgroundDetail') platBackgroundDetailExpand=jsonOne.get('platBackgroundDetailExpand') platBackgroundExpand=jsonOne.get('platBackgroundExpand') platEarnings=jsonOne.get('platEarnings') platEarningsCode=jsonOne.get('platEarningsCode') platName=jsonOne.get('platName') platStatus=jsonOne.get('platStatus') platUrl=jsonOne.get('platUrl') problem=jsonOne.get('problem') problemTime=jsonOne.get('problemTime') recordId=jsonOne.get('recordId') recordLicId=jsonOne.get('recordLicId') registeredCapital=jsonOne.get('registeredCapital') riskCapital=jsonOne.get('riskCapital') riskFunds=jsonOne.get('riskFunds') riskReserve=jsonOne.get('riskReserve') riskcontrol=jsonOne.get('riskcontrol') securityModel=jsonOne.get('securityModel') securityModelCode=jsonOne.get('securityModelCode') securityModelOther=jsonOne.get('securityModelOther') serviceScore=jsonOne.get('serviceScore') serviceScoreDetail=jsonOne.get('serviceScoreDetail') startInvestmentAmout=jsonOne.get('startInvestmentAmout') term=jsonOne.get('term') termCodes=jsonOne.get('termCodes') termWeight=jsonOne.get('termWeight') transferExpense=jsonOne.get('transferExpense') transferExpenseDetail=jsonOne.get('transferExpenseDetail') trustCapital=jsonOne.get('trustCapital') trustCreditor=jsonOne.get('trustCreditor') trustCreditorMonth=jsonOne.get('trustCreditorMonth') trustFunds=jsonOne.get('trustFunds') tzjPj=jsonOne.get('tzjPj') vipExpense=jsonOne.get('vipExpense') withTzj=jsonOne.get('withTzj') withdrawExpense=jsonOne.get('withdrawExpense') sql='insert into problemPlatDetail (actualCapital,aliasName,association,associationDetail,autoBid,autoBidCode,bankCapital,bankFunds,bidSecurity,bindingFlag,businessType,companyName,credit,creditLevel,delayScore,delayScoreDetail,displayFlg,drawScore,drawScoreDetail,equityVoList,experienceScore,experienceScoreDetail,fundCapital,gjlhhFlag,gjlhhTime,gruarantee,inspection,juridicalPerson,locationArea,locationAreaName,locationCity,locationCityName,manageExpense,manageExpenseDetail,newTrustCreditor,newTrustCreditorCode,officeAddress,onlineDate,payment,paymode,platBackground,platBackgroundDetail,platBackgroundDetailExpand,platBackgroundExpand,platEarnings,platEarningsCode,platName,platStatus,platUrl,problem,problemTime,recordId,recordLicId,registeredCapital,riskCapital,riskFunds,riskReserve,riskcontrol,securityModel,securityModelCode,securityModelOther,serviceScore,serviceScoreDetail,startInvestmentAmout,term,termCodes,termWeight,transferExpense,transferExpenseDetail,trustCapital,trustCreditor,trustCreditorMonth,trustFunds,tzjPj,vipExpense,withTzj,withdrawExpense,platId) values ("'+actualCapital+'","'+aliasName+'","'+association+'","'+associationDetail+'","'+autoBid+'","'+autoBidCode+'","'+bankCapital+'","'+bankFunds+'","'+bidSecurity+'","'+bindingFlag+'","'+businessType+'","'+companyName+'","'+credit+'","'+creditLevel+'","'+delayScore+'","'+delayScoreDetail+'","'+displayFlg+'","'+drawScore+'","'+drawScoreDetail+'","'+equityVoList+'","'+experienceScore+'","'+experienceScoreDetail+'","'+fundCapital+'","'+gjlhhFlag+'","'+gjlhhTime+'","'+gruarantee+'","'+inspection+'","'+juridicalPerson+'","'+locationArea+'","'+locationAreaName+'","'+locationCity+'","'+locationCityName+'","'+manageExpense+'","'+manageExpenseDetail+'","'+newTrustCreditor+'","'+newTrustCreditorCode+'","'+officeAddress+'","'+onlineDate+'","'+payment+'","'+paymode+'","'+platBackground+'","'+platBackgroundDetail+'","'+platBackgroundDetailExpand+'","'+platBackgroundExpand+'","'+platEarnings+'","'+platEarningsCode+'","'+platName+'","'+platStatus+'","'+platUrl+'","'+problem+'","'+problemTime+'","'+recordId+'","'+recordLicId+'","'+registeredCapital+'","'+riskCapital+'","'+riskFunds+'","'+riskReserve+'","'+riskcontrol+'","'+securityModel+'","'+securityModelCode+'","'+securityModelOther+'","'+serviceScore+'","'+serviceScoreDetail+'","'+startInvestmentAmout+'","'+term+'","'+termCodes+'","'+termWeight+'","'+transferExpense+'","'+transferExpenseDetail+'","'+trustCapital+'","'+trustCreditor+'","'+trustCreditorMonth+'","'+trustFunds+'","'+tzjPj+'","'+vipExpense+'","'+withTzj+'","'+withdrawExpense+'","'+platId+'")' cur.execute(sql) conn.commit() conn,cur=DatabaseUtil().getConn() session=SessionUtil() logUtil=LogUtil("problemPlatDetail.log") cur.execute('select platId from problemPlat') data=cur.fetchall() print(data) mylist=list() print(data) for i in range(0,len(data)): platId=str(data[i].get('platId')) mylist.append(platId) print mylist for i in mylist: url='http://wwwservice.wdzj.com/api/plat/platData30Days?platId='+i try: data=session.getReq(url) platData=handleData(data) dictObject=DictUtil(platData) storeData(dictObject,conn,cur,i) except Exception,e: traceback.print_exc() cur.close() conn.close
整个过程中 我们 构造请求,然后把解析每个请求的响应,其中json返回值使用json库进行解析,html页面使用BeautifulSoup库进行解析(结构复杂的html的页面推荐使用lxml库进行解析),解析到的结果存储到 mysql 数据库中。
爬虫代码
爬虫代码地址 (注:爬虫使用代码Python2与python3都可运行,本人把爬虫代码部署在阿里云服务器上,使用Python2 运行)
数据分析
数据分析主要使用Python的numpy、pandas、matplotlib进行数据分析,同时辅以海致BDP。
时间序列分析
数据读取
一般采取把数据读取pandas的DataFrame中进行分析。
以下就是读取问题平台的数据的例子
problemPlat=pd.read_csv('problemPlat.csv',parse_dates=True)#问题平台
数据结构
时间序列分析
eg 问题平台数量随时间变化
problemPlat['id']['2012':'2017'].resample('M',how='count').plot(title='P2P发生问题')#发生问题P2P平台数量 随时间变化趋势
图形化展示
地域分析
使用海致BDP完成(Python绘制地图分布轮子比较复杂,当时还未学习)
各省问题平台数量
各省平台成交额
规模分布分析
eg 全国六月平台成交额分布
代码
juneData['amount'].hist(normed=True) juneData['amount'].plot(kind='kde',style='k--')#六月份交易量概率分布
核密度图形展示
成交额取对数核密度分布
np.log10(juneData['amount']).hist(normed=True) np.log10(juneData['amount']).plot(kind='kde',style='k--')#取 10 对数的 概率分布
图形化展示
可看出取10的对数后分布更符合正常的金字塔形。
相关性分析
eg.陆金所交易额与所有平台交易额的相关系数变化趋势
lujinData=platVolume[platVolume['wdzjPlatId']==59] corr=pd.rolling_corr(lujinData['amount'],allPlatDayData['amount'],50,min_periods=50).plot(title='陆金所交易额与所有平台交易额的相关系数变化趋势')
图形化展示
分类比较
车贷平台与全平台成交额数据对比
carFinanceDayData=carFinanceData.resample('D').sum()['amount'] fig,axes=plt.subplots(nrows=1,ncols=2,sharey=True,figsize=(14,7)) carFinanceDayData.plot(ax=axes[0],title='车贷平台交易额') allPlatDayData['amount'].plot(ax=axes[1],title='所有p2p平台交易额')
趋势预测
eg预测陆金所成交量趋势(使用Facebook Prophet库完成)
lujinAmount=platVolume[platVolume['wdzjPlatId']==59] lujinAmount['y']=lujinAmount['amount'] lujinAmount['ds']=lujinAmount['date'] m=Prophet(yearly_seasonality=True) m.fit(lujinAmount) future=m.make_future_dataframe(periods=365) forecast=m.predict(future) m.plot(forecast)
趋势预测图形化展示
数据分析代码
数据分析代码地址 (注:数据分析代码智能运行在Python3 环境下)
代码运行后样例 (无需安装Python环境 也可查看具体代码解图形化展示)
后记
这是本人从 Java web转向数据方向后自己写的第一项目,也是自己的第一个Python项目,在整个过程中,也没遇到多少坑,整体来说,爬虫和数据分析以及Python这门语言门槛都是非常低的。
如果想入门Python爬虫,推荐《Python网络数据采集》
如果想入门Python数据分析,推荐 《利用Python进行数据分析》
以上就是本文的全部内容,希望本文的内容对大家的学习或者工作能带来一定的帮助,也希望大家多多支持 码农网
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