P2P平臺數據爬取分析

時間  2019-11-15
標籤 p2p 平臺 數據 分析 欄目 硅谷 简体版
原文   原文鏈接

關於數據來源

本項目寫於2017年七月初，主要使用Python爬取網貸之家以及人人貸的數據進行分析。
網貸之家是國內最大的P2P數據平臺，人人貸國內排名前二十的P2P平臺。
源碼地址javascript

數據爬取

抓包分析

抓包工具主要使用chrome的開發者工具 網絡一欄，網貸之家的數據所有是ajax返回json數據，而人人貸既有ajax返回數據也有html頁面直接生成數據。html

請求實例

QQ截圖20180123205633.png
從數據中能夠看到請求數據的方式（GET或者POST），請求頭以及請求參數。
抓包詳情
從請求數據中能夠看到返回數據的格式（此例中爲json）、數據結構以及具體數據。
注：這是如今網貸之家的API請求後臺的接口，爬蟲編寫的時候與數據接口與現在的請求接口不同，因此網貸之家的數據爬蟲部分已無效。java

構造請求

根據抓包分析獲得的結果，構造請求。在本項目中，使用Python的 requests庫模擬http請求
具體代碼：python

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
    //發送get請求
    def getReq(self,url):
        return self.session.get(url,headers=self.headers).text
    def addCookie(self,cookie):
        self.headers['cookie']=cookie
    //發送post請求
    def postReq(self,url,param):
        return self.session.post(url, param).text

在設置請求頭的時候，關鍵字段只設置了"User-Agent"，網貸之家和人人貸的沒有反爬措施，甚至不用設置"Referer"字段來防止跨域錯誤。mysql

爬蟲實例

如下是一個爬蟲實例git

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=''+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

整個過程當中 咱們 構造請求，而後把解析每一個請求的響應，其中json返回值使用json庫進行解析，html頁面使用BeautifulSoup庫進行解析（結構複雜的html的頁面推薦使用lxml庫進行解析），解析到的結果存儲到mysql數據庫中。github

爬蟲代碼

爬蟲代碼地址(注：爬蟲使用代碼Python2與python3均可運行，本人把爬蟲代碼部署在阿里雲服務器上，使用Python2 運行）web

數據分析

數據分析主要使用Python的numpy、pandas、matplotlib進行數據分析，同時輔以海致BDP。ajax

時間序列分析

數據讀取

通常採起把數據讀取pandas的DataFrame中進行分析。
如下就是讀取問題平臺的數據的例子sql

problemPlat=pd.read_csv('problemPlat.csv',parse_dates=True)#問題平臺

數據結構
QQ截圖20180123212641.png

時間序列分析

eg 問題平臺數量隨時間變化

problemPlat['id']['2012':'2017'].resample('M',how='count').plot(title='P2P發生問題')#發生問題P2P平臺數量 隨時間變化趨勢

圖形化展現
QQ截圖20180123212803.png

地域分析

使用海致BDP完成（Python繪製地圖分佈輪子比較複雜，當時還未學習）

各省問題平臺數量

下載.png

各省平臺成交額

整年成交額全國各省對比.png

規模分佈分析

eg 全國六月平臺成交額分佈
代碼

juneData['amount'].hist(normed=True)
juneData['amount'].plot(kind='kde',style='k--')#六月份交易量機率分佈

核密度圖形展現
QQ截圖20180123213700.png
成交額取對數核密度分佈

np.log10(juneData['amount']).hist(normed=True)
np.log10(juneData['amount']).plot(kind='kde',style='k--')#取 10 對數的 機率分佈

圖形化展現
QQ截圖20180123213901.png
可看出取10的對數後分布更符合正常的金字塔形。

相關性分析

eg.陸金所交易額與全部平臺交易額的相關係數變化趨勢

lujinData=platVolume[platVolume['wdzjPlatId']==59]
corr=pd.rolling_corr(lujinData['amount'],allPlatDayData['amount'],50,min_periods=50).plot(title='陸金所交易額與全部平臺交易額的相關係數變化趨勢')

圖形化展現
QQ截圖20180123214114.png

分類比較

車貸平臺與全平臺成交額數據對比

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='車貸平臺交易額')
allPlatDayData['amount'].plot(ax=axes[1],title='全部p2p平臺交易額')

QQ截圖20180123214359.png

趨勢預測

eg預測陸金所成交量趨勢（使用Facebook Prophet庫完成）

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)

趨勢預測圖形化展現
QQ截圖20180123214653.png

數據分析代碼

數據分析代碼地址(注：數據分析代碼智能運行在Python3 環境下）
代碼運行後樣例(無需安裝Python環境 也可查看具體代碼解圖形化展現)

後記

這是本人從 Java web轉向數據方向後本身寫的第一項目，也是本身的第一個Python項目，在整個過程當中，也沒遇到多少坑，總體來講，爬蟲和數據分析以及Python這門語言門檻都是很是低的。
若是想入門Python爬蟲，推薦《Python網絡數據採集》
s29086659.jpg
若是想入門Python數據分析，推薦 《利用Python進行數據分析》
30adcbef76094b360e72e763a9cc7cd98c109d58.jpg

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