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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