read_pdf_plt/xmlSelfParser.py

#!/usr/bin/env Python
# coding=utf-8
import json
from xml.dom import minidom
import consts;
import constsBase
import pandas as pd
import numpy as np;
import time
import log
logger = log.logger
import utils
import loanIndexParser as lip;
import payRcdIndexParser as prp;
import creditCardIndexParser as cip
import queryInfoIndexParser as qip
import requests
import dfParser
import sys
import traceback
import timeit
from mailUtil import MailUtil
from dbController import DbController
#身份信息
identityInfoIndex = '身份信息'
identityInfoDf = pd.DataFrame(columns=consts.identityInfoHeader,index=[identityInfoIndex])
#配偶信息
mateInfoIndex = '配偶信息'
mateInfoDf = pd.DataFrame(columns=consts.mateInfoHeader,index=[mateInfoIndex])
#居住信息
liveInfoIndex = '居住信息'
liveInfoDf = pd.DataFrame(columns=consts.liveInfoHeader,index=[liveInfoIndex])
#职业信息
occupationInfoIndex = '职业信息'
occupationInfoDf = pd.DataFrame(columns=consts.occupationInfoHeader,index=[occupationInfoIndex])

#信贷交易信息提示
loanTradeInfoIndex = '信贷交易信息提示'
briefInfoDf_loanTradeInfo =  pd.DataFrame(columns=consts.briefInfoHeader_loanTradeInfo,index=[loanTradeInfoIndex])
#被追偿信息汇总及呆账信息汇总
recoveryInfoSumIndex = '信贷交易违约信息概要'
briefInfoDf_recoveryInfoSum =  pd.DataFrame(columns=consts.briefInfoHeader_recoveryInfo,index=[recoveryInfoSumIndex])

#呆账信息汇总
badDebtsInfoIndex = '呆账信息汇总'
briefInfoDf_badDebtsInfoSum =  pd.DataFrame(columns=consts.briefInfoHeader_badDebtsInfoSum,index=[badDebtsInfoIndex])


#逾期（透支）信息汇总
overdueInfoSumIndex='逾期（透支）信息汇总'
briefInfoDf_overdueInfoSum =  pd.DataFrame(columns=consts.briefInfoHeader_overdueInfoSum,index=[overdueInfoSumIndex])
#信贷交易授信及负债信息概要
loanTradeCreditInfoIndex='信贷交易授信及负债信息概要'
briefInfoDf_loanTradeCreditInfo =  pd.DataFrame(columns=consts.briefInfoHeader_loanTradeCreditInfo,index=[loanTradeCreditInfoIndex]).fillna(0.0)
#公共信息概要
publicInfoBriefIndex = '公共信息概要'
publicInfoBriefDf =  pd.DataFrame(columns=consts.publicInfoBriefHeader,index=[publicInfoBriefIndex])

#查询记录汇总
queryRecordSumIndex = '查询记录汇总'
queryRecordSumDf =  pd.DataFrame(columns=consts.queryRecordSumHeader,index=[queryRecordSumIndex])

#信贷交易明细-被追偿信息
recoveryInfoIndex='被追偿信息'
creditTradeDetailDf_recoveryInfo = pd.DataFrame(columns=consts.creditTradeDetailHeader_recoveryInfo,index=[recoveryInfoIndex])

#信贷交易明细-特殊交易
specialTradeIndex='特殊交易'
creditTradeDetailHeader_specialTrade = pd.DataFrame(columns=consts.creditTradeDetailHeader_specialTrade,index=[specialTradeIndex])


#信贷交易明细
#非循环贷账户
loanInfoIndex='非循环贷账户'
creditTradeDetailDf_loanAccountInfo = pd.DataFrame(columns=consts.creditTradeDetailHeader_loanAccountInfo,index=[loanInfoIndex])

#循环额度下分账户
cycleCreditAccountInfoIndex='循环额度下分账户'
creditTradeDetailDf_cycleCreditAccountInfo = pd.DataFrame(columns=consts.creditTradeDetailHeader_cycleCreditAccountInfo,index=[cycleCreditAccountInfoIndex])

#循环贷账户
cycleLoanAccountInfoIndex='循环贷账户'
creditTradeDetailDf_cycleLoanAccountInfo = pd.DataFrame(columns=consts.creditTradeDetailHeader_cycleLoanAccountInfo,index=[cycleLoanAccountInfoIndex])

#贷款信息
loanAccountInfoIndex='贷款信息'
loanAccountInfoDf =  pd.DataFrame(columns=consts.loanAccountInfoHeader,index=[loanAccountInfoIndex])
#贷记卡信息
creditCardAccountInfoIndex = '贷记卡账户'
creditCardAccountInfoDf = pd.DataFrame(columns=consts.creditCardAccountInfoHeader,index=[creditCardAccountInfoIndex])

#准贷记卡
creditCardAccountInfoIndexZ = '准贷记卡账户'
creditCardAccountInfoDfZ = pd.DataFrame(columns=consts.creditCardAccountInfoHeaderZ,index=[creditCardAccountInfoIndexZ])

useRateIndex = '使用率'
useRateDf =  pd.DataFrame(columns=consts.creditTradeDetailHeader_useRate,index=[useRateIndex])
openAccountIndex = '开户数'
openAccountDf =  pd.DataFrame(columns=consts.creditTradeDetailHeader_openAccount,index=[openAccountIndex])
payRcdStatusIndex = '24期还款状态'
payRcdStatusDf =  pd.DataFrame(columns=consts.creditTradeDetailHeader_payRcdStatus,index=[payRcdStatusIndex])

#查询记录明细指标
queryRecordDetailIndex = '信贷审批查询记录明细'
queryRecordDetailDf = pd.DataFrame(columns=consts.queryRecordDetailHeader,index=[queryRecordDetailIndex])

#住房公积金
housingFundRcdIndex = '住房公积金参缴记录'
housingFundRcdDf = pd.DataFrame(columns=consts.housingFundRcdHeader,index=[housingFundRcdIndex])

#汇算-其他
otherIndex = '慧算账新增'
otherDf = pd.DataFrame(columns=consts.otherHeader,index=[otherIndex])

sanshouIndex = '恒昌新增'
sanshouDf = pd.DataFrame(columns=consts.sanShouHeader,index=[sanshouIndex])

#0916
juziIndex = '桔子新增'
juziDf = pd.DataFrame(columns=consts.juziHeader,index=[juziIndex])

#基础信息字段补充，从欠税记录到查询记录（已补完），中间略去本人声明和异注声明

#基础身份信息
baseIdentityInfo = "基础身份信息"
baseIdentityInfoDf = pd.DataFrame(columns=constsBase.identityInfo,index=[baseIdentityInfo])

#基础配偶信息
baseMateInfo = "基础配偶信息"
baseMateInfoDf = pd.DataFrame(columns=constsBase.mateInfo,index=[baseMateInfo])

#基础居住信息
baseLiveInfo = "基础居住信息"
baseLiveInfoDf = pd.DataFrame(columns=constsBase.liveInfo,index=[baseLiveInfo])

#基础职业信息
baseOccupationInfo = "基础职业信息"
baseOccupationInfoDf = pd.DataFrame(columns=constsBase.occupationInfo,index=[baseOccupationInfo])

#个人信用报告"数字解读"
personalCreditInfo = "个人信用报告'数字解读'"
personalCreditInfoDf = pd.DataFrame(columns=constsBase.personalCreditInfo,index=[personalCreditInfo])

#信贷交易信息提示
briefInfoHeaderLoanTradeInfo = "信贷交易信息提示"
briefInfoHeaderLoanTradeInfoDf = pd.DataFrame(columns=constsBase.briefInfoHeaderLoanTradeInfo,index=[briefInfoHeaderLoanTradeInfo])

#被追偿信息汇总
recoveryInfoSumIndexY = '信贷交易违约信息概要'
briefInfoDf_recoveryInfoSumY =  pd.DataFrame(columns=constsBase.briefInfoHeader_recoveryInfoY,index=[recoveryInfoSumIndexY])

#呆账信息汇总
badDebtsInfoIndexY = '呆账信息汇总'
briefInfoDf_badDebtsInfoSumY =  pd.DataFrame(columns=constsBase.briefInfoHeader_badDebtsInfoSumY,index=[badDebtsInfoIndexY])


#逾期（透支）信息汇总
overdueInfoSumIndexY='逾期（透支）信息汇总'
briefInfoDf_overdueInfoSumY =  pd.DataFrame(columns=constsBase.briefInfoHeader_overdueInfoSumY,index=[overdueInfoSumIndexY])


#信贷交易授信及负债信息概要
creditTransactionCredit = "信贷交易授信及负债信息概要"
creditTransactionCreditDf = pd.DataFrame(columns=constsBase.creditTransactionCredit,index=[creditTransactionCredit])

#非信贷交易信息概要
nonCreditTransactions = "非信贷交易信息概要"
nonCreditTransactionsDf = pd.DataFrame(columns=constsBase.nonCreditTransactions,index=[nonCreditTransactions])

#公共信息概要
publicInformation = "公共信息概要"
publicInformationDf = pd.DataFrame(columns=constsBase.publicInformation,index=[publicInformation])

#查询记录概要
queryRecord = "查询记录概要"
queryRecordDf = pd.DataFrame(columns=constsBase.queryRecord,index=[queryRecord])

#被追偿信息
recoveryInformation = "被追偿信息"
recoveryInformationDf = pd.DataFrame(columns=constsBase.recoveryInformation,index=[recoveryInformation])

#非循环贷账户
nonRevolvingLoan = "非循环贷账户"
nonRevolvingLoanDf = pd.DataFrame(columns=constsBase.nonRevolvingLoan,index=[nonRevolvingLoan])

#循环额度下分账户
revolvingLoneSubAccount = "循环额度下分账户"
revolvingLoneSubAccountDf = pd.DataFrame(columns=constsBase.revolvingLoneSubAccount,index=[revolvingLoneSubAccount])

#循环贷账户
revolvingLone = "循环贷账户"
revolvingLoneDf = pd.DataFrame(columns=constsBase.revolvingLone,index=[revolvingLone])

#贷记卡账户
creditCard = "贷记卡账户"
creditCardDf = pd.DataFrame(columns=constsBase.creditCard,index=[creditCard])

#准贷记卡账户
quasiCreditCard = "准贷记卡账户"
quasiCreditCardDf = pd.DataFrame(columns=constsBase.quasiCreditCard,index=[quasiCreditCard])

#相关还款责任信息
relevantRepaymentLiability = "相关还款责任信息"
relevantRepaymentLiabilityDf = pd.DataFrame(columns=constsBase.relevantRepaymentLiability,index=[relevantRepaymentLiability])

#授信协议信息
creditAgreement = "授信协议信息"
creditAgreementDf = pd.DataFrame(columns=constsBase.creditAgreement,index=[creditAgreement])

#后付费记录(未开发)
postPaidRecord = "后付费记录"
postPaidRecordDf = pd.DataFrame(columns=constsBase.postPaidRecord,index=[postPaidRecord])

#欠税记录
taxArrearsIndex = '欠税记录'
taxArrearsDf = pd.DataFrame(columns=constsBase.taxArrears,index=[taxArrearsIndex])

#民事判决记录
civilJudgmentIndex = '民事判决记录'
civilJudgmentDf = pd.DataFrame(columns=constsBase.civilJudgment,index=[civilJudgmentIndex])

#强制执行记录
enforceIndex = '强制执行记录'
enforceDf = pd.DataFrame(columns=constsBase.enforce,index=[enforceIndex])

#行政处罚记录
administrativePunishmentIndex = '行政处罚记录'
administrativePunishmentDf = pd.DataFrame(columns=constsBase.administrativePunishment,index=[administrativePunishmentIndex])

#住房公积金参缴记录
housingProvidentFundParticipationIndex = '住房公积金参缴记录'
housingProvidentFundParticipationIndexDf = pd.DataFrame(columns=constsBase.housingProvidentFundParticipation,index=[housingProvidentFundParticipationIndex])

#低保救助记录
subsistenceAllowanceAssistanceIndex = '低保救助记录'
subsistenceAllowanceAssistanceDf = pd.DataFrame(columns=constsBase.subsistenceAllowanceAssistance,index=[subsistenceAllowanceAssistanceIndex])

#执业资格记录
practiceQualificationIndex = '执业资格记录'
practiceQualificationDf = pd.DataFrame(columns=constsBase.practiceQualification,index=[practiceQualificationIndex])

#行政奖励记录
administrativeIncentiveIndex = '行政奖励记录'
administrativeIncentiveDf = pd.DataFrame(columns=constsBase.administrativeIncentive,index=[administrativeIncentiveIndex])

#本人声明
personalDeclaration = "本人声明"
personalDeclarationDf = pd.DataFrame(columns=constsBase.personalDeclaration,index=[personalDeclaration])


#异议标注
objectionMarking =  "异议标注"
objectionMarkingDf = pd.DataFrame(columns=constsBase.objectionMarking,index=[objectionMarking])

#查询记录
queryRecordIndex = '查询记录'
queryRecordDf = pd.DataFrame(columns=constsBase.queryRecord,index=[queryRecordIndex])




#待执行的sql语句
sqlMap = []
class XmlSelfParser(object):
    def getNodeData(self,node, key):
        data = ""
        try:
            if len(node.getElementsByTagName(key)) > 0:
                nodeValue = node.getElementsByTagName(key)[0].childNodes
                if len(nodeValue)>0:
                    data = nodeValue[0].data
        except Exception:
            logger.error("getNodeData error")
            info = sys.exc_info()
            logger.error(info[0])
            logger.error(info[1])
            logger.error(traceback.extract_tb(info[2], 1))
        return data;
    def parse(self, xmlData):
        result = "{"
        try:
            docXml = minidom.parseString(xmlData)
            # print("xmlData" + xmlData)
            # print("docXml" +docXml)

            queryInfo = self.parseQueryInfo(docXml)
            reportTime = queryInfo["reportTime"];
            start = timeit.default_timer();
            logger.info(queryInfo["queryInfoCardId"] + "基本信息解析开始")
            self.parseIdentity(reportTime,docXml)
            self.parseLiveInfo(docXml)
            self.parseOccupationInfoDf(reportTime,docXml)
            self.parseLoanTradeInfo(docXml)
            self.parseRecoveryInfoSum(docXml)
            self.parseBadDebtsInfoSumDf(docXml)
            self.parseOverdueInfoSum(docXml)
            self.parseLoanAccountInfoSum(docXml)
            self.parseCycleCreditAccountInfoSum(docXml);
            self.parseCyleLoanAccountInfoSum(docXml);
            self.parseCreditCardInfoSum(docXml)
            self.parseCreditCardInfoSumZ(docXml)
            self.parseRepaymentSum(docXml)
            self.parsePublicInfoBrief(docXml)
            self.parseQueryRecordSum(docXml)
            self.parseQueryInfoDetail(reportTime,docXml)
            self.parseHousingFundRcd(reportTime,docXml)

            #新增加
            self.parseCivilJudgment(docXml) #yuan

            s = timeit.default_timer() - start
            logger.info(str(s) + " 秒")
            logger.info(queryInfo["queryInfoCardId"] + "基本信息解析完成")
            loanAccountDfs = []
            start = timeit.default_timer();
            logger.info(queryInfo["queryInfoCardId"]+"贷款解析开始")
            # 非循环
            noloopcreditaccountbols = self.mergeLoanDf(reportTime, docXml, "NOLOOPCREDITACCOUNTBOLS")
            # 循环额度
            loopquotaaccountbols = self.mergeLoanDf(reportTime, docXml, "LOOPQUOTAACCOUNTBOLS")
            # 循环贷账户
            loopcreditaccountbols = self.mergeLoanDf(reportTime, docXml, "LOOPCREDITACCOUNTBOLS")
            loanAccountDfs = noloopcreditaccountbols + loopquotaaccountbols + loopcreditaccountbols
            # print(len(loanAccountDfs))
            s = timeit.default_timer() - start
            logger.info(str(s) + " 秒")
            logger.info(queryInfo["queryInfoCardId"] + "贷款解析完成")

            start = timeit.default_timer();
            logger.info(queryInfo["queryInfoCardId"] + "信用卡解析开始")
            creditCardAccountDfs = self.mergeCreditCardDf(reportTime, docXml)
            # print(len(creditCardAccountDfs))
            creditCardAccountDfsZ = self.mergeCreditCardDfZ(reportTime, docXml)
            # print(len(creditCardAccountDfsZ))

            # 合并贷款
            loanMergeDf = pd.DataFrame(columns=dfParser.dfHeaderLoan)
            loanPayRecordMergeDf = pd.DataFrame(columns=dfParser.dfHeaderLoanPayRecord)
            loanSpecialTradeMergeDf = pd.DataFrame(columns=dfParser.dfHeaderLoanSpecialTrade)  # 特殊交易
            loanMergeDfDs = []
            loanPayRecordMergeDfDs = []
            # for loanDfObj in loanAccountDfs:
            #     loanMergeDf = pd.concat([loanMergeDf, loanDfObj["loanDf"]], axis=0, ignore_index=True);
            #     loanPayRecordMergeDf = pd.concat([loanPayRecordMergeDf, loanDfObj["loanPayRecordDf"]], axis=0,ignore_index=True);
            #     loanSpecialTradeMergeDf = pd.concat([loanSpecialTradeMergeDf, loanDfObj["specialTradeDf"]], axis=0,ignore_index=True);
            for loanDfObj in loanAccountDfs:
                loanMergeDfDs.append(loanDfObj["loanDf"])
                if loanDfObj["loanPayRecordDf"]!=None:
                    loanPayRecordMergeDfDs.extend(loanDfObj["loanPayRecordDf"])
                loanSpecialTradeMergeDf = pd.concat([loanSpecialTradeMergeDf, loanDfObj["specialTradeDf"]], axis=0,ignore_index=True);
            if len(loanMergeDfDs)>0:
                loanMergeDf =  pd.DataFrame(loanMergeDfDs)
            if len(loanPayRecordMergeDfDs)>0:
                loanPayRecordMergeDf =  pd.DataFrame(loanPayRecordMergeDfDs)
            # 合并贷记卡
            creditCardMergeDf = pd.DataFrame(columns=dfParser.dfHeaderCreditCard)
            creditCardPayRecordMergeDf = pd.DataFrame(columns=dfParser.dfHeaderCreditCardPayRecord)
            for creditCardDfObj in creditCardAccountDfs:
                creditCardMergeDf = pd.concat([creditCardMergeDf, creditCardDfObj["creditCardDf"]], axis=0,ignore_index=True);
                creditCardPayRecordMergeDf = pd.concat([creditCardPayRecordMergeDf, creditCardDfObj["creditCardPayRecordDf"]], axis=0, ignore_index=True);

            # 准贷记卡合并df
            creditCardMergeDfZ = pd.DataFrame(columns=dfParser.dfHeaderCreditCardZ)
            creditCardPayRecordMergeDfZ = pd.DataFrame(columns=dfParser.dfHeaderCreditCardPayRecordZ)
            for creditCardDfObj in creditCardAccountDfsZ:
                creditCardMergeDfZ = pd.concat([creditCardMergeDfZ, creditCardDfObj["creditCardDfZ"]], axis=0,ignore_index=True);
                creditCardPayRecordMergeDfZ = pd.concat([creditCardPayRecordMergeDfZ, creditCardDfObj["creditCardPayRecordDfZ"]], axis=0,ignore_index=True);

            s = timeit.default_timer() - start
            logger.info(str(s) + " 秒")
            logger.info(queryInfo["queryInfoCardId"] + "信用卡解析完成")

            result += utils.toJson(identityInfoDf) + ","
            result += utils.toJson(mateInfoDf) + ","
            result += utils.toJson(liveInfoDf) + ","
            result += utils.toJson(occupationInfoDf) + ","
            result += "briefInfoDf_loanTradeInfo" + ","  # 占位符
            result += "briefInfoDf_recoveryInfoSum" + ","  # 占位符
            result += "briefInfoDf_overdueInfoSum" + ","
            result += utils.toJson(briefInfoDf_badDebtsInfoSum) + ","
            result += utils.toJson(briefInfoDf_loanTradeCreditInfo) + ","
            result += utils.toJson(publicInfoBriefDf) + ","
            result += utils.toJson(civilJudgmentDf) + "," #yuan
            result += utils.toJson(queryRecordSumDf) + ","

            # 合并被追偿信息DF
            recoveryInfoMergeDf = self.mergeRecoveryInfoDf(reportTime, docXml)
            self.parseRecoveryInfoMergeDf(recoveryInfoMergeDf)

            result = result.replace("briefInfoDf_recoveryInfoSum", utils.toJson(briefInfoDf_recoveryInfoSum))  # 替换汇总中的指标
            result += utils.toJson(creditTradeDetailDf_recoveryInfo) + ","  # 设置占位符，由于存在概要的指标在明细中计算
            # 特殊交易
            self.parseSpecialTrade(loanSpecialTradeMergeDf)
            result += utils.toJson(creditTradeDetailHeader_specialTrade) + ","

            # 信贷交易明细-解析非循环贷账户
            self.parseLoanAccountInfo(loanMergeDf);
            result += utils.toJson(creditTradeDetailDf_loanAccountInfo) + ","
            # 循环额度分账户
            self.parseCycleCreditAccountInfo(loanMergeDf);
            result += utils.toJson(creditTradeDetailDf_cycleCreditAccountInfo) + ","
            # 循环贷
            self.parseCycleLoanAccountInfo(loanMergeDf);
            result += utils.toJson(creditTradeDetailDf_cycleLoanAccountInfo) + ","

            # 解析贷款账户指标
            start = timeit.default_timer();
            self.parseLoanMergeDf(loanMergeDf,reportTime);
            s = timeit.default_timer() - start
            logger.info("解析贷款账户指标 " + str(s) + " 秒")
            # 解析还款记录相关指标
            start = timeit.default_timer();
            self.parseLoanMergeAndPayRecordDf(loanMergeDf, loanPayRecordMergeDf,reportTime);
            s = timeit.default_timer() - start
            logger.info("解析还款记录相关指标 " + str(s) + " 秒")
            result += utils.toJson(loanAccountInfoDf) + ","

            # 解析贷记卡账户指标
            self.parseCreditCardMergeDf(creditCardMergeDf,reportTime);
            self.parseCreditCardMergeAndPayRecordDf(creditCardMergeDf, creditCardPayRecordMergeDf,reportTime)

            # 解析准贷记卡相关指标
            self.parseCreditCardMergeDfZ(creditCardMergeDfZ, creditCardPayRecordMergeDfZ);

            result += utils.toJson(creditCardAccountInfoDf) + ","
            result += utils.toJson(creditCardAccountInfoDfZ) + ","

            # 使用率
            self.parseUseRate()
            result += utils.toJson(useRateDf) + ","
            # 开户数
            self.parseOpenAccount(loanMergeDf, creditCardMergeDf, creditCardMergeDfZ, recoveryInfoMergeDf, loanPayRecordMergeDf,creditCardPayRecordMergeDf, creditCardPayRecordMergeDfZ,reportTime)
            result += utils.toJson(openAccountDf) + ","

            # 24期还款状态
            start = timeit.default_timer();
            self.parsePayRcdStatus(loanMergeDf, creditCardMergeDf, creditCardMergeDfZ, loanPayRecordMergeDf,creditCardPayRecordMergeDf, creditCardPayRecordMergeDfZ,reportTime)
            s = timeit.default_timer() - start
            logger.info("24期还款状态 " + str(s) + " 秒")
            result += utils.toJson(payRcdStatusDf) + ","
            # 由于逾期汇总的指标再还款状态之后需要替换占位 TODO
            result = result.replace("briefInfoDf_overdueInfoSum", utils.toJson(briefInfoDf_overdueInfoSum))
            # 0525 由于在开户数后，统计信贷信息概要的指标，替换占位符
            result = result.replace("briefInfoDf_loanTradeInfo", utils.toJson(briefInfoDf_loanTradeInfo))
            # 公积金
            result += utils.toJson(housingFundRcdDf) + ","
            result += utils.toJson(queryRecordDetailDf) + ","
            #取消汇算帐
            result += utils.toJson(otherDf) + ","
            #三寿新增
            result += utils.toJson(sanshouDf) + ","
            self.calcFirstBusMonth(reportTime)
            self.getLastOverdueMonthJz(loanMergeDf,creditCardMergeDf,loanPayRecordMergeDf,creditCardPayRecordMergeDf,reportTime)
            result += utils.toJson(juziDf)
            result += "}"
        except Exception:
            info = sys.exc_info()
            # logger.error(str(Exception))
            logger.error(queryInfo["queryInfoCardId"]+" error")
            logger.error(info[0])
            logger.error(info[1])
            logger.error(traceback.extract_tb(info[2], 1))
            # logger.error( 'str(Exception):\t', str(Exception))
            # logger.error('str(e):\t\t', str(e))
            # logger.error('repr(e):\t', repr(e))
            # logger.error('e.message:\t', e.message)
            logger.error(traceback.print_exc())
            logger.error(traceback.format_exc())
            mailUtil = MailUtil();
            webhook = 'https://oapi.dingtalk.com/robot/send?access_token=64d8b2c7fed4949e9433b807c7c5559939f1517af8f77c1dacb4de19c6910b56'
            mailUtil.dingtalk("号码:"+queryInfo["queryInfoCardId"]+" 解析错误 "+traceback.format_exc(),webhook)
        return  result,sqlMap;
    # 解析被查询信息指标
    def parseQueryInfo(self,docXml):
        headbo = docXml.documentElement.getElementsByTagName("HEADBO")[0]
        reportTime= self.getNodeData(headbo,"RPTTIME")
        queryInfo = {};
        queryInfoCardId = self.getNodeData(headbo,"CRENUMBER")
        queryInfo["reportTime"] = reportTime.replace("T"," ")
        queryInfo["queryInfoCardId"] = queryInfoCardId;  # 被查询者证件号码
        otherDf.loc[otherIndex, '报告查询日期'] = queryInfo["reportTime"];
        sanshouDf.loc[sanshouIndex,"身份证号前两位"]=queryInfoCardId[0:2]
        # print(queryInfo)
        RPTNO = self.getNodeData(headbo, "RPTNO")
        self.RPTNO = RPTNO
        self.saveNode(headbo)
        return queryInfo;
    #xml节点入库
    def saveNode(self,node):
        try:
            RPTNO = self.RPTNO
            if node.nodeName == "HEADBO":
                sql = "insert into "+node.nodeName+"("
                # 保存根节点
                childNodes = node.childNodes;
                for child in childNodes:
                    sql += child.nodeName+","
                sql=sql[:-1]
                sql += ")"
                sql+="values("
                for child in childNodes:
                    if len(child.childNodes) == 1:
                        sql +="'"+ child.childNodes[0].data + "',"
                    else:
                        sql += "'" + "" + "',"
                if sql.endswith(","):
                    sql = sql[:-1]
                sql += ")"
            else:#保存其它节点
                sql = "insert into " + node.nodeName + "(RPTNO,"
                # 保存根节点
                childNodes = node.childNodes;
                for child in childNodes:
                    if child.nodeName=='RINFOLS':
                        continue
                    sql += child.nodeName + ","
                sql = sql[:-1]
                sql += ")"
                sql += "values("+"'"+RPTNO+"',"
                for child in childNodes:
                    if child.nodeName=='RINFOLS':
                        continue
                    if len(child.childNodes) == 1:
                        sql += "'" + child.childNodes[0].data + "',"
                    else:
                        sql += "'" + "" + "',"
                if sql.endswith(","):
                    sql = sql[:-1]
                sql += ")"
            sqlMap.append(sql)
        except:
            logger.error(traceback.print_exc())
            logger.error(traceback.format_exc())
    def saveNodeWithType(self,node,TYPE):
        try:
            RPTNO = self.RPTNO
            sql = "insert into " + node.nodeName + "(TYPE,RPTNO,"
            # 保存根节点
            childNodes = node.childNodes;
            for child in childNodes:
                if child.nodeName=='RINFOLS':
                    continue
                sql += child.nodeName + ","
            sql = sql[:-1]
            sql += ")"
            sql += "values("+"'"+TYPE+"','"+RPTNO+"',"
            for child in childNodes:
                if child.nodeName=='RINFOLS':
                    continue
                if len(child.childNodes) == 1:
                    sql += "'" + child.childNodes[0].data + "',"
                else:
                    sql += "'" + "" + "',"
            if sql.endswith(","):
                sql = sql[:-1]
            sql += ")"
            sqlMap.append(sql)
        except:
            logger.error(traceback.print_exc())
            logger.error(traceback.format_exc())
    def parseIdentity(self,reportTime,docXml):
        tmpNode =docXml.documentElement.getElementsByTagName("BASEINFO")
        if len(tmpNode)>0:
            baseInfo = docXml.documentElement.getElementsByTagName("BASEINFO")[0]
            print(str(baseInfo))
            self.saveNode(baseInfo)
            tmpNode = docXml.documentElement.getElementsByTagName("MARRIAGEINFOSG")

            identityInfoDf.loc[identityInfoIndex, '性别'] = self.getNodeData(baseInfo,"SEXDESC")
            identityInfoDf.loc[identityInfoIndex, '出生日期'] = self.getNodeData(baseInfo,"BIRTHDAY")
            identityInfoDf.loc[identityInfoIndex, '国籍'] = self.getNodeData(baseInfo,"NATIONDESC")
            identityInfoDf.loc[identityInfoIndex, '户籍地址'] = self.getNodeData(baseInfo,"ADDRESS")
            if len(tmpNode)>0:
                marriageinfosg = docXml.documentElement.getElementsByTagName("MARRIAGEINFOSG")[0]
                self.parseMate(marriageinfosg)
                identityInfoDf.loc[identityInfoIndex, '婚姻状况'] = self.getNodeData(marriageinfosg,"MARSTATUDESC")
            identityInfoDf.loc[identityInfoIndex, '学历'] = self.getNodeData(baseInfo,"DEGREEDESC")
            identityInfoDf.loc[identityInfoIndex, '学位'] = self.getNodeData(baseInfo,"DEGREEDESC")
            identityInfoDf.loc[identityInfoIndex, '通讯地址'] = self.getNodeData(baseInfo,"COMMUADDRESS")
            identityInfoDf.loc[identityInfoIndex, '就业状况'] = self.getNodeData(baseInfo,"WORKSTATUSDESC")
            mobiles = docXml.documentElement.getElementsByTagName("TELEPHONEINFOSGLS")
            if len(mobiles)>0:
                telephoneinfosgls = docXml.documentElement.getElementsByTagName("TELEPHONEINFOSGLS")[0]
                telephoneinfosg = telephoneinfosgls.getElementsByTagName("TELEPHONEINFOSG");
                identityInfoDf.loc[identityInfoIndex, '历史手机号码数'] = len(telephoneinfosg)
                identityInfoDf.loc[identityInfoIndex, '近3个月手机号码数'] = self.getLastMonthMobileCount(telephoneinfosg, 3, reportTime)
                identityInfoDf.loc[identityInfoIndex, '近6个月手机号码数'] = self.getLastMonthMobileCount(telephoneinfosg, 6, reportTime)
                identityInfoDf.loc[identityInfoIndex, '近12个月手机号码数'] = self.getLastMonthMobileCount(telephoneinfosg, 12, reportTime)
                identityInfoDf.loc[identityInfoIndex, '近24个月手机号码数'] = self.getLastMonthMobileCount(telephoneinfosg, 24, reportTime)
            # print(identityInfoDf.to_json(orient="index",force_ascii=False))
    def getLastMonthMobileCount(self,telephoneinfosg, month, reportTime):
        df = pd.DataFrame()
        for i in range(0,len(telephoneinfosg)):
            self.saveNode(telephoneinfosg[i])
            telephone = self.getNodeData(telephoneinfosg[i],"TELEPHONE")
            updatedateinfo= self.getNodeData(telephoneinfosg[i],"UPDATEDATEINFO")
            df.loc[i,0] = telephone
            df.loc[i,1] = updatedateinfo
        # 当前日期
        last1MonthDateStr = reportTime
        # 最近一个月
        lastMonthDate = np.datetime64(last1MonthDateStr, "D") - np.timedelta64(30 * month, 'D')
        lastMonthMobileDf = df[df[1] >= str(lastMonthDate)]
        return lastMonthMobileDf.shape[0];

    #解析配偶信息
    def parseMate(self,marriageinfosg):
        self.saveNode(marriageinfosg)
        mateInfoDf.loc[mateInfoIndex, '姓名'] = self.getNodeData(marriageinfosg,"SPUOSENAME")
        mateInfoDf.loc[mateInfoIndex, '证件号码'] = self.getNodeData(marriageinfosg,"CERTNUM")
        mateInfoDf.loc[mateInfoIndex, '工作单位'] =  self.getNodeData(marriageinfosg,"COMPANY")
        mateInfoDf.loc[mateInfoIndex, '联系电话'] = self.getNodeData(marriageinfosg,"TELNUM")
        # print(mateInfoDf.to_json(orient="index", force_ascii=False))

    # 解析居住信息
    def parseLiveInfo(self,docXml):
        # residenceinfosgls = docXml.documentElement.getElementsByTagName("RESIDENCEINFOSGLS")
        residenceinfosg = docXml.documentElement.getElementsByTagName("RESIDENCEINFOSG");
        df = pd.DataFrame()
        for i in range(0, len(residenceinfosg)):
            self.saveNode(residenceinfosg[i])
            resistatusdesc = self.getNodeData(residenceinfosg[i], "RESISTATUSDESC")
            address = self.getNodeData(residenceinfosg[i], "ADDRESS")
            telnum = self.getNodeData(residenceinfosg[i], "TELNUM")
            updatedateinfo = self.getNodeData(residenceinfosg[i], "UPDATEDATEINFO")
            df.loc[i, 0] = resistatusdesc
            df.loc[i, 1] = address
            df.loc[i, 2] = telnum
            df.loc[i, 3] = updatedateinfo
        if len(residenceinfosg)>0:
            liveInfoDf.loc[liveInfoIndex, '居住地址'] = self.getNodeData(residenceinfosg[0],"ADDRESS")
            liveInfoDf.loc[liveInfoIndex, '住宅电话'] = self.getNodeData(residenceinfosg[0],"TELNUM")
            liveInfoDf.loc[liveInfoIndex, '历史居住地址个数'] = len(residenceinfosg);
            curDate = np.datetime64(time.strftime("%Y-%m-%d"));
            last3year = str(curDate)[0:4]
            last3yearDate = str(int(last3year) - 3) + str(curDate)[4:10]
            lastLiveDf = df[df[3] >= last3yearDate];
            liveInfoDf.loc[liveInfoIndex, '最近3年内居住地址个数'] = lastLiveDf.index.size ;
            houseIndex = df[df[0] == '自置'].index.size > 0
            if (houseIndex):
                houseStr = '是'
            else:
                houseStr = '否'
            liveInfoDf.loc[liveInfoIndex, '当前居住状况-是否具有自有住房'] = houseStr;
            liveInfoDf.loc[liveInfoIndex, '居住状况'] = self.getNodeData(residenceinfosg[0],"RESISTATUSDESC")
            liveInfoDf.loc[liveInfoIndex, '信息更新日期'] = self.getNodeData(residenceinfosg[0],"UPDATEDATEINFO")
            # print(liveInfoDf.to_json(orient="index", force_ascii=False))

    # 解析职业信息
    def parseOccupationInfoDf(self,reportTime,docXml):
        vocationinfosg = docXml.documentElement.getElementsByTagName("VOCATIONINFOSG");
        df = pd.DataFrame()
        if len(vocationinfosg)>0:
            for i in range(0, len(vocationinfosg)):
                # df.loc[i, 0] = self.getNodeData(vocationinfosg[i], "WORDSTATUSDESC")
                self.saveNode(vocationinfosg[i])
                df.loc[i, 0] = self.getNodeData(vocationinfosg[i], "CPNTYPEDESC")
                df.loc[i, 1] = self.getNodeData(vocationinfosg[i], "TRADEDESC")
                df.loc[i, 2] = self.getNodeData(vocationinfosg[i], "VOCATIONDESC")
                df.loc[i, 3] = self.getNodeData(vocationinfosg[i], "VOCATIONTITLEDESC")
                df.loc[i, 4] = self.getNodeData(vocationinfosg[i], "COMPANY")
                df.loc[i, 5] = self.getNodeData(vocationinfosg[i], "CPNADDRESS")
                df.loc[i, 6] = self.getNodeData(vocationinfosg[i], "CPNTELNUM")
                df.loc[i, 7] = self.getNodeData(vocationinfosg[i], "BEGINYEAR")
                df.loc[i, 8] = self.getNodeData(vocationinfosg[i], "INFOUPDATEDATE")
            occupationInfoDf.loc[occupationInfoIndex, '工作单位'] = self.getNodeData(vocationinfosg[0], "COMPANY")
            last3yearDate = utils.getLastMonthDate(reportTime, 12 * 3)
            last3yearOccDf = df[df[8] >= last3yearDate]
            occupationInfoDf.loc[occupationInfoIndex, '最近3年内工作单位数'] = last3yearOccDf.index.size;
            occupationInfoDf.loc[occupationInfoIndex, '单位电话'] = self.getNodeData(vocationinfosg[0], "CPNTELNUM")
            try:
                minDateIndex = np.argmin(df[7]);
                maxDateIndex = np.argmax(df[7]);
                rowYearMin = df.loc[minDateIndex, :].dropna()
                rowYearMax = df.loc[maxDateIndex, :].dropna()
                if rowYearMin[7] != "":
                    occupationInfoDf.loc[occupationInfoIndex, '最早进入本单位年份距报告日期时长'] = int(
                        str(np.datetime64(reportTime, "Y"))) - int(rowYearMin[7])
                if rowYearMax[7] != "":
                    occupationInfoDf.loc[occupationInfoIndex, '最新进入本单位年份距报告日期时长'] = int(
                        str(np.datetime64(reportTime, "Y"))) - int(rowYearMax[7])
            except:
                logger.error("最早进入本单位年份距报告日期时长解析异常")
            occupationInfoDf.loc[occupationInfoIndex, '单位性质'] = self.getNodeData(vocationinfosg[0], "CPNTYPEDESC")
            occupationInfoDf.loc[occupationInfoIndex, '单位地址'] = self.getNodeData(vocationinfosg[0], "CPNADDRESS")
            occupationInfoDf.loc[occupationInfoIndex, '职业'] = self.getNodeData(vocationinfosg[0], "VOCATIONDESC")
            occupationInfoDf.loc[occupationInfoIndex, '行业'] = self.getNodeData(vocationinfosg[0], "TRADEDESC")
            occupationInfoDf.loc[occupationInfoIndex, '职务'] = self.getNodeData(vocationinfosg[0], "TITLEDESC")
            occupationInfoDf.loc[occupationInfoIndex, '职称'] = self.getNodeData(vocationinfosg[0], "VOCATIONTITLEDESC")
            occupationInfoDf.loc[occupationInfoIndex, '进入本单位年份'] =self.getNodeData(vocationinfosg[0], "BEGINYEAR")
            occupationInfoDf.loc[occupationInfoIndex, '信息更新日期'] = self.getNodeData(vocationinfosg[0], "INFOUPDATEDATE")
            occupationInfoDf.loc[occupationInfoIndex, '历史工作单位数'] = len(vocationinfosg)
            # print(occupationInfoDf.to_json(orient="index", force_ascii=False))

    # 信贷交易明细汇总
    def parseLoanTradeInfo(self,docXml):
        credittipsinfo = docXml.documentElement.getElementsByTagName("CREDITTIPSINFO");
        if len(credittipsinfo)>0:
            briefInfoDf_loanTradeInfo.loc[loanTradeInfoIndex, '个人住房贷款账户数'] = utils.toInt(self.getNodeData(credittipsinfo[0],"ACCOUNTNUM"))
            self.saveNode(credittipsinfo[0])
            briefInfoDf_loanTradeInfo.loc[loanTradeInfoIndex, '个人商用房贷款（包括商住两用）账户数'] = utils.toInt(self.getNodeData(credittipsinfo[1],"ACCOUNTNUM"))
            self.saveNode(credittipsinfo[1])
            briefInfoDf_loanTradeInfo.loc[loanTradeInfoIndex, '其他类贷款账户数'] = utils.toInt(self.getNodeData(credittipsinfo[2],"ACCOUNTNUM"))
            self.saveNode(credittipsinfo[2])
            briefInfoDf_loanTradeInfo.loc[loanTradeInfoIndex, '贷记卡账户数'] = utils.toInt(self.getNodeData(credittipsinfo[3],"ACCOUNTNUM"))
            self.saveNode(credittipsinfo[3])
            briefInfoDf_loanTradeInfo.loc[loanTradeInfoIndex, '准贷记卡账户数'] = utils.toInt(self.getNodeData(credittipsinfo[4],"ACCOUNTNUM"))
            self.saveNode(credittipsinfo[4])
            #汇算
            moths = [self.getNodeData(credittipsinfo[0], "FIRSTBUSMONTH"),
                                                          self.getNodeData(credittipsinfo[1], "FIRSTBUSMONTH"),self.getNodeData(credittipsinfo[2], "FIRSTBUSMONTH")]
            moths=utils.get_real_arr(moths)
            if len(moths)>0:
                otherDf.loc[otherIndex, '首笔贷款发放月份'] = min(moths)
            otherDf.loc[otherIndex, '首张贷记卡发卡月份'] = self.getNodeData(credittipsinfo[3], "FIRSTBUSMONTH")
        # print(briefInfoDf_loanTradeInfo.to_json(orient="index", force_ascii=False))

    #贷款首次交易或者第一张贷记卡开卡日期距今月份数
    #juzi
    def calcFirstBusMonth(self,reportTime):
        try:
            firstLoanMonth = str(otherDf.loc[otherIndex, '首笔贷款发放月份'])
            firstCreditMonth = str(otherDf.loc[otherIndex, '首张贷记卡发卡月份'])
            dateStr = ""
            if str(firstLoanMonth) == "nan":
                firstLoanMonth = ""
            if firstCreditMonth == "nan":
                firstCreditMonth = ""
            if firstLoanMonth == "" and firstCreditMonth !="":
                dateStr = firstCreditMonth
            elif firstCreditMonth == "" and firstLoanMonth !="":
                dateStr = firstLoanMonth
            elif  firstCreditMonth == "" and firstLoanMonth =="":
                dateStr = ""
            else:
                dateStr = min(firstCreditMonth,firstLoanMonth)
            if dateStr !="":
                juziDf.loc[juziIndex, '贷款首次交易或者第一张贷记卡开卡日期距今月份数'] = utils.difMonthReportTime(dateStr,reportTime)
        except:
            logger.error(traceback.print_exc())
            logger.error(traceback.format_exc())
            mailUtil = MailUtil();
            webhook = 'https://oapi.dingtalk.com/robot/send?access_token=64d8b2c7fed4949e9433b807c7c5559939f1517af8f77c1dacb4de19c6910b56'
            mailUtil.dingtalk("号码:"  + " 解析错误 " + traceback.format_exc(), webhook)

    # 最近一次逾期距今月份数 juzi
    def getLastOverdueMonthJz(self, loanMergeDf, creditCardMergeDf, loanPayRecordMergeDf, creditCardPayRecordMergeDf,
                            reportTime):
        try:
            result = None;
            if np.sum(loanMergeDf['当前逾期期数']) > 0 or np.sum(creditCardMergeDf['当前逾期期数']) > 0:
                result = 0
            else:
                payRcdDf = loanPayRecordMergeDf.sort_values(by=["账户编号", "还款日期"], ascending=(True, False))
                # payRcdDf = payRcdDf.groupby(['账户编号']).head(60)
                payRcdDf = utils.replacePayRcdStatusOverdue(payRcdDf)  # 替换逾期
                payRcdDf = payRcdDf[payRcdDf['还款状态'] > 0]
                payRcdDf = utils.replacePayRcdStatusOverdue(payRcdDf)  # 替换逾期
                loanOverdueDate = np.max(payRcdDf['还款日期'])
                payRcdDfCredit = creditCardPayRecordMergeDf.sort_values(by=["账户编号", "还款日期"], ascending=(True, False))
                payRcdDfCredit = utils.replacePayRcdStatusOverdue(payRcdDfCredit)  # 替换逾期
                # payRcdDfCredit = payRcdDfCredit.groupby(['账户编号']).head(60)
                payRcdDfCredit = payRcdDfCredit[payRcdDfCredit['还款状态'] > 0]
                if not payRcdDf.empty or not payRcdDfCredit.empty:
                    creditOverdueDate = np.max(payRcdDfCredit['还款日期'])
                    # reportTime = str(np.datetime64(reportTime, "M")) + "-02"
                    reportTime = utils.get_last_month_first_day_v2(reportTime)
                    overdueDate = None
                    if str(loanOverdueDate) != "nan" and str(creditOverdueDate) != "nan":
                        overdueDate = max([loanOverdueDate, creditOverdueDate])
                    elif str(loanOverdueDate) == "nan" and str(creditOverdueDate) != "nan":
                        overdueDate = creditOverdueDate
                    elif str(loanOverdueDate) != "nan" and str(creditOverdueDate) == "nan":
                        overdueDate = loanOverdueDate
                    if overdueDate != None:
                        result = utils.difMonthReportTime(overdueDate, reportTime)
            juziDf.loc[juziIndex, "用户贷记卡或者贷款最近逾期距今月份数"] = result
        except:
            logger.error(traceback.print_exc())
            logger.error(traceback.format_exc())
            mailUtil = MailUtil();
            webhook = 'https://oapi.dingtalk.com/robot/send?access_token=64d8b2c7fed4949e9433b807c7c5559939f1517af8f77c1dacb4de19c6910b56'
            mailUtil.dingtalk("号码:" +  " 解析错误 " + traceback.format_exc(), webhook)
    def encodeJuzi(self,value):
        result = ""
        if value == None:
            result = 'A'
        elif value >=1 and value <3:
            result = 'B'
        elif value >=3 and value <5:
            result = 'C'
        elif value >= 3 and value < 5:
            result = 'C'
        elif value >= 5 and value < 7:
            result = 'D'
        elif value >= 7 and value < 10:
            result = 'E'
        elif value >= 10 and value < 12:
            result = 'F'
        elif value >= 12 and value < 15:
            result = 'G'
        elif value >= 15 and value < 17:
            result = 'H'
        elif value >= 17 and value < 20:
            result = 'I'
        elif value >= 20 and value < 24:
            result = 'J'
        elif value >= 24 and value < 30:
            result = 'K'
        elif value >= 30 and value < 36:
            result = 'L'
        elif value >= 36 and value < 48:
            result = 'M'
        elif value >= 48:
            result = 'N'
        return result
    # 解析被追偿信息汇总
    def parseRecoveryInfoSum(self,docXml):
        recoveredinfosumsmry = docXml.documentElement.getElementsByTagName("RECOVEREDINFOSUMSMRY");
        recorinfosumsg = docXml.documentElement.getElementsByTagName("RECORINFOSUMSG")
        if len(recoveredinfosumsmry)>0:
            if len(recorinfosumsg)>0:
                self.saveNode(recorinfosumsg[0])
                briefInfoDf_recoveryInfoSum.loc[recoveryInfoSumIndex, '资产处置业务账户数'] = utils.toInt(self.getNodeData(recorinfosumsg[0],"ACCOUNTNUM"))
                briefInfoDf_recoveryInfoSum.loc[recoveryInfoSumIndex, '资产处置业务余额'] = utils.replaceAmt(self.getNodeData(recorinfosumsg[0],"BALANCE"))
                briefInfoDf_recoveryInfoSum.loc[recoveryInfoSumIndex, '垫款业务账户数'] = utils.toInt(self.getNodeData(recorinfosumsg[1],"ACCOUNTNUM"))
                briefInfoDf_recoveryInfoSum.loc[recoveryInfoSumIndex, '垫款业务余额'] = utils.replaceAmt(self.getNodeData(recorinfosumsg[1],"BALANCE"))
                self.saveNode(recorinfosumsg[1])
            briefInfoDf_recoveryInfoSum.loc[recoveryInfoSumIndex, '合计总账户数'] = utils.toInt(self.getNodeData(recoveredinfosumsmry[0],"ACCOUNTNUMSUM"))
            briefInfoDf_recoveryInfoSum.loc[recoveryInfoSumIndex, '合计总余额'] = utils.replaceAmt(self.getNodeData(recoveredinfosumsmry[0],"BALANCESUM"))
            self.saveNode(recoveredinfosumsmry[0])
        # print(briefInfoDf_recoveryInfoSum.to_json(orient="index", force_ascii=False))
    # 解析呆账信息汇总
    def parseBadDebtsInfoSumDf(self,docXml):
        baddebtssmry = docXml.documentElement.getElementsByTagName("BADDEBTSSMRY");
        if len(baddebtssmry)>0:
            briefInfoDf_badDebtsInfoSum.loc[badDebtsInfoIndex, '账户数'] = utils.toInt(self.getNodeData(baddebtssmry[0],"ACCOUNTNUM"))
            briefInfoDf_badDebtsInfoSum.loc[badDebtsInfoIndex, '余额'] = utils.replaceAmt(self.getNodeData(baddebtssmry[0],"BALANCE"));
            self.saveNode(baddebtssmry[0])
        # print(briefInfoDf_badDebtsInfoSum.to_json(orient="index", force_ascii=False))
    #透支及逾期信息汇总
    def parseOverdueInfoSum(self,docXml):
        overdraftsumsg = docXml.documentElement.getElementsByTagName("OVERDRAFTSUMSG");
        df = pd.DataFrame()
        if len(overdraftsumsg)>0:
            for i in range(0, len(overdraftsumsg)):
                df.loc[i, 0] = self.getNodeData(overdraftsumsg[i], "ACCTYPEDESC")
                df.loc[i, 1] = self.getNodeData(overdraftsumsg[i], "ACCSUM")
                df.loc[i, 2] = self.getNodeData(overdraftsumsg[i], "MONTHSUM")
                df.loc[i, 3] = self.getNodeData(overdraftsumsg[i], "OVERDRAFTMAX")
                df.loc[i, 4] = self.getNodeData(overdraftsumsg[i], "OVERMONTH")
                self.saveNode(overdraftsumsg[i])
        if len(overdraftsumsg)>0:
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '非循环贷帐户账户数'] = utils.toInt(self.getNodeData(overdraftsumsg[0],"ACCSUM"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '非循环贷帐户月份数'] = utils.toInt(self.getNodeData(overdraftsumsg[0],"MONTHSUM"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '非循环贷帐户单月最高逾期总额'] = utils.replaceAmt(self.getNodeData(overdraftsumsg[0],"OVERDRAFTMAX"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '非循环贷帐户最长逾期月数'] = utils.toInt(self.getNodeData(overdraftsumsg[0],"OVERMONTH"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '循环额度下分账户账户数'] = utils.toInt(self.getNodeData(overdraftsumsg[1],"ACCSUM"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '循环额度下分账户月份数'] = utils.toInt(self.getNodeData(overdraftsumsg[1],"MONTHSUM"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '循环额度下分账户单月最高逾期总额'] = utils.replaceAmt(self.getNodeData(overdraftsumsg[1],"OVERDRAFTMAX"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '循环额度下分账户最长逾期月数'] = utils.toInt(self.getNodeData(overdraftsumsg[1],"OVERMONTH"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '循环贷账户账户数'] = utils.toInt(self.getNodeData(overdraftsumsg[2],"ACCSUM"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '循环贷账户月份数'] = utils.toInt(self.getNodeData(overdraftsumsg[2],"MONTHSUM"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '循环贷账户单月最高逾期总额'] = utils.replaceAmt(self.getNodeData(overdraftsumsg[2],"OVERDRAFTMAX"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '循环贷账户最长逾期月数'] = utils.toInt(self.getNodeData(overdraftsumsg[2],"OVERMONTH"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '贷记卡账户账户数'] = utils.toInt(self.getNodeData(overdraftsumsg[3],"ACCSUM"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '贷记卡账户月份数'] = utils.toInt(self.getNodeData(overdraftsumsg[3],"MONTHSUM"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '贷记卡账户单月逾期总额'] = utils.replaceAmt(self.getNodeData(overdraftsumsg[3],"OVERDRAFTMAX"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '贷记卡账户最长逾期月数'] = utils.toInt(self.getNodeData(overdraftsumsg[3],"OVERMONTH"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '准贷记卡账户账户数'] = utils.toInt(self.getNodeData(overdraftsumsg[4],"ACCSUM"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '准贷记卡账户月份数'] = utils.toInt(self.getNodeData(overdraftsumsg[4],"MONTHSUM"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '准贷记卡账户单月透支总额'] = utils.replaceAmt(self.getNodeData(overdraftsumsg[4],"OVERDRAFTMAX"));
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '准贷记卡账户最长透支月数'] = utils.toInt(self.getNodeData(overdraftsumsg[4],"OVERMONTH"));
            overdueInfoAccountDf = df[df[1] != ''];
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '该用户所有逾期账户最长逾期/透支月数最大值'] = np.max(overdueInfoAccountDf[4].astype('int'))
            briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '该用户所有逾期账户数加总'] = np.sum(overdueInfoAccountDf[1].astype('int'))  # TODO
            # briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '该用户过去5年出现逾期的所有账户数目']=None# TODO
            # print(briefInfoDf_overdueInfoSum.to_json(orient="index", force_ascii=False))


    # 非循环贷账户信息汇总
    def parseLoanAccountInfoSum(self,docXml):
        noncyclicalsmry = docXml.documentElement.getElementsByTagName("NONCYCLICALSMRY");
        if len(noncyclicalsmry)>0:
            # loanAccountInfoSumDf = doFilterCalc(loanAccountInfoSumDf);  # 替换为0
            #TODO 需要检查是否都有值
            # loanAccountInfoSumDf = loanAccountInfoSumDf.reset_index(drop=True)
            # row0 = loanAccountInfoSumDf.loc[0, :]
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '非循环贷账户管理机构数'] = int(self.getNodeData(noncyclicalsmry[0],"MANAGEORGNUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '非循环贷账户账户数'] = int(self.getNodeData(noncyclicalsmry[0],"ACCSUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '非循环贷账户授信总额'] = utils.replaceAmt(self.getNodeData(noncyclicalsmry[0],"CREDITSUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '非循环贷账户余额'] = utils.replaceAmt(self.getNodeData(noncyclicalsmry[0],"BALANCE"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '非循环贷账户6月平均应还款'] = utils.replaceAmt(self.getNodeData(noncyclicalsmry[0],"LATELY6MON"))
            self.saveNode(noncyclicalsmry[0])
        # print(briefInfoDf_loanTradeCreditInfo.to_json(orient="index", force_ascii=False))
    # 循环额度下分账户
    def parseCycleCreditAccountInfoSum(self,docXml):
        cybranchaccsmry = docXml.documentElement.getElementsByTagName("CYBRANCHACCSMRY");
        if len(cybranchaccsmry) > 0:
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环额度下分账户管理机构数'] = int(self.getNodeData(cybranchaccsmry[0],"MANAGEORGNUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环额度下分账户账户数'] = int(self.getNodeData(cybranchaccsmry[0],"ACCSUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环额度下分账户授信总额'] = utils.replaceAmt(self.getNodeData(cybranchaccsmry[0],"CREDITSUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环额度下分账户余额'] = utils.replaceAmt(self.getNodeData(cybranchaccsmry[0],"BALANCE"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环额度下分账户6月平均应还款'] = utils.replaceAmt(self.getNodeData(cybranchaccsmry[0],"LATELY6MON"))
            self.saveNode(cybranchaccsmry[0])
        # print(briefInfoDf_loanTradeCreditInfo.to_json(orient="index", force_ascii=False))

    # 循环贷账户信息
    def parseCyleLoanAccountInfoSum(self,docXml):
        cyclicalloansmry = docXml.documentElement.getElementsByTagName("CYCLICALLOANSMRY");
        if len(cyclicalloansmry) > 0:
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环贷账户管理机构数'] = int(self.getNodeData(cyclicalloansmry[0],"MANAGEORGNUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环贷账户账户数'] = int(self.getNodeData(cyclicalloansmry[0],"ACCSUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环贷账户授信总额'] = utils.replaceAmt(self.getNodeData(cyclicalloansmry[0],"CREDITSUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环贷账户余额'] = utils.replaceAmt(self.getNodeData(cyclicalloansmry[0],"BALANCE"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环贷账户6月平均应还款'] = utils.replaceAmt(self.getNodeData(cyclicalloansmry[0],"LATELY6MON"))
            self.saveNode(cyclicalloansmry[0])

    # 解析贷记卡信息汇总,包含准贷记卡
    def parseCreditCardInfoSum(self,docXml):
        debitcardaccsmry = docXml.documentElement.getElementsByTagName("DEBITCARDACCSMRY");
        if len(debitcardaccsmry) > 0:
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '贷记卡发卡机构数'] = int(self.getNodeData(debitcardaccsmry[0],"CARDISSUERNUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '贷记卡账户数'] =int(self.getNodeData(debitcardaccsmry[0],"ACCSUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '贷记卡授信总额'] = utils.replaceAmt(self.getNodeData(debitcardaccsmry[0],"CREDITSUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '贷记卡单家机构最高授信额'] = utils.replaceAmt(self.getNodeData(debitcardaccsmry[0],"CRAMOUNTMAX"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '贷记卡单家机构最低授信额'] =utils.replaceAmt(self.getNodeData(debitcardaccsmry[0],"CRAMOUNTMIN"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '贷记卡已用额度'] = utils.replaceAmt(self.getNodeData(debitcardaccsmry[0],"ALREADYUSED"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '贷记卡最近6个月平均使用额度'] = utils.replaceAmt(self.getNodeData(debitcardaccsmry[0],"LATELY6MONUSE"))
            self.saveNode(debitcardaccsmry[0])
    # 解析贷记卡信息汇总,包含准贷记卡
    def parseCreditCardInfoSumZ(self,docXml):
        zdebitcardaccsmry = docXml.documentElement.getElementsByTagName("ZDEBITCARDACCSMRY");
        if len(zdebitcardaccsmry) > 0:
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '准贷记卡发卡机构数'] = int(self.getNodeData(zdebitcardaccsmry[0],"CARDISSUERNUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '准贷记卡账户数'] =int(self.getNodeData(zdebitcardaccsmry[0],"ACCSUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '准贷记卡授信总额'] = utils.replaceAmt(self.getNodeData(zdebitcardaccsmry[0],"CREDITSUM"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '准贷记卡单家机构最高授信额'] = utils.replaceAmt(self.getNodeData(zdebitcardaccsmry[0],"CRAMOUNTMAX"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '准贷记卡单家机构最低授信额'] = utils.replaceAmt(self.getNodeData(zdebitcardaccsmry[0],"CRAMOUNTMIN"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '准贷记卡已用额度'] = utils.replaceAmt(self.getNodeData(zdebitcardaccsmry[0],"OVERDRAFT"))
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '准贷记卡最近6个月平均使用额度'] = utils.replaceAmt(self.getNodeData(zdebitcardaccsmry[0],"LATELY6MONDRAFT"))
            self.saveNode(zdebitcardaccsmry[0])

    # 相关还款责任
    def parseRepaymentSum(self,docXml):
        repaymentinfosmry = docXml.documentElement.getElementsByTagName("REPAYMENTINFOSMRY");
        for item in repaymentinfosmry:
            self.saveNode(item)
        if len(repaymentinfosmry)>0:
            perAccountNum = 0;  # 个人账户数
            orgAccountNum = 0;  # 企业账户数
            totalAccountNum = 0;  # 总账户数
            guaranteeAccountNum = 0;  # 相关还款责任总账户数-担保责任
            otherAccountNum = 0;  # 相关还款责任总账户数-其他

            perGuaranteeAmt = 0  # 个人担保金额及其他
            orgGuaranteeAmt = 0  # 企业担保金额及其他
            totalGuaranteeAmt = 0;  # 总担保金额
            guaranteeAmt = 0;  # 相关还款责任总担保金额
            otherPaymentAmt = 0;  # 其他还款责任金额

            perGuaranteeBalance = 0  # 个人担保余额及其他
            orgGuaranteeBalance = 0  # 企业担保余额及其他
            totalGuaranteeBalance = 0;  # 总担保余额
            guaranteeBalance = 0;  # 相关还款责任总担保余额
            otherPaymentBalance = 0;  # 其他还款责任余额

            perGuaranteeNum = self.getNodeData(repaymentinfosmry[0],"ACCSUM")
            perGuaranteeAmtStr = self.getNodeData(repaymentinfosmry[0], "RELPAYRESPSUM")
            perGuaranteeBalanceStr = self.getNodeData(repaymentinfosmry[0], "BALANCE")

            perOtherNum = self.getNodeData(repaymentinfosmry[1], "ACCSUM")
            perOtherAmt = self.getNodeData(repaymentinfosmry[1], "RELPAYRESPSUM")
            perOtherBalance = self.getNodeData(repaymentinfosmry[1], "BALANCE")
            enGuaranteeNum = ""
            enGuaranteeAmt = ""
            enGuaranBalance = ""
            enOtherNum = ""
            enOtherAmt = ""
            enOtherBalance= ""
            if len(repaymentinfosmry)>=3:
                enGuaranteeNum = self.getNodeData(repaymentinfosmry[2], "ACCSUM")
                enGuaranteeAmt = self.getNodeData(repaymentinfosmry[2], "RELPAYRESPSUM")
                enGuaranBalance = self.getNodeData(repaymentinfosmry[2], "BALANCE")
            if len(repaymentinfosmry) >= 4:
                enOtherNum = self.getNodeData(repaymentinfosmry[3], "ACCSUM")
                enOtherAmt = self.getNodeData(repaymentinfosmry[3], "RELPAYRESPSUM")
                enOtherBalance = self.getNodeData(repaymentinfosmry[3], "BALANCE")
            # 计算总账户数
            if perGuaranteeNum != "":
                perAccountNum = perAccountNum + utils.toInt(perGuaranteeNum)
                guaranteeAccountNum = guaranteeAccountNum + utils.toInt(perGuaranteeNum)  # 个人担保责任账户数
            if perOtherNum != "":
                perAccountNum = perAccountNum + utils.toInt(perOtherNum)  # 其他
                otherAccountNum = otherAccountNum + utils.toInt(perOtherNum)  # 其他
            if enGuaranteeNum != None:
                if enGuaranteeNum != "":
                    orgAccountNum = orgAccountNum + utils.toInt(enGuaranteeNum)
                    guaranteeAccountNum = guaranteeAccountNum + utils.toInt(enGuaranteeNum)  # 企业担保责任账户数
                if enOtherNum != "":
                    orgAccountNum = orgAccountNum + utils.toInt(enOtherNum)  # 其他
                    otherAccountNum = otherAccountNum + utils.toInt(enOtherNum)  # 其他
            totalAccountNum = perAccountNum + orgAccountNum
            # 计算担保金额
            if perGuaranteeAmtStr != "":
                perGuaranteeAmt = perGuaranteeAmt + utils.replaceAmt(perGuaranteeAmtStr)  # 担保
                guaranteeAmt = guaranteeAmt + utils.replaceAmt(perGuaranteeAmtStr)  # 担保
            if perOtherAmt != "":
                perGuaranteeAmt = perGuaranteeAmt + utils.replaceAmt(perOtherAmt)  # 其他
                otherPaymentAmt = otherPaymentAmt + utils.replaceAmt(perOtherAmt)  # 其他
            if enGuaranteeAmt != None:
                if enGuaranteeAmt != "":
                    orgGuaranteeAmt = orgGuaranteeAmt + utils.replaceAmt(enGuaranteeAmt)  # 担保
                    guaranteeAmt = guaranteeAmt + utils.replaceAmt(enGuaranteeAmt)  # 担保
                if enOtherAmt != "":
                    orgGuaranteeAmt = orgGuaranteeAmt + utils.replaceAmt(enOtherAmt)  # 其他
                    otherPaymentAmt = otherPaymentAmt + utils.replaceAmt(enOtherAmt)  # 其他
            totalGuaranteeAmt = perGuaranteeAmt + orgGuaranteeAmt
            # 计算余额
            if perGuaranteeBalanceStr != "":
                perGuaranteeBalance = perGuaranteeBalance + utils.replaceAmt(perGuaranteeBalanceStr)
                guaranteeBalance = guaranteeBalance + utils.replaceAmt(perGuaranteeBalanceStr)  # 个人担保余额
            if perOtherBalance != "":
                perGuaranteeBalance = perGuaranteeBalance + utils.replaceAmt(perOtherBalance)  # 其他
                otherPaymentBalance = otherPaymentBalance + utils.replaceAmt(perOtherBalance)  # 其他

            if enGuaranBalance != None:
                if enGuaranBalance != "":
                    orgGuaranteeBalance = orgGuaranteeBalance + utils.replaceAmt(enGuaranBalance)
                    guaranteeBalance = guaranteeBalance + utils.replaceAmt(enGuaranBalance)  # 企业担保余额
                if enOtherBalance != "":
                    orgGuaranteeBalance = orgGuaranteeBalance + utils.replaceAmt(enOtherBalance)
                    otherPaymentBalance = otherPaymentBalance + utils.replaceAmt(enOtherBalance)  # 其他
            totalGuaranteeBalance = perGuaranteeBalance + orgGuaranteeBalance

            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总账户数（担保+其他+个人+企业）'] = totalAccountNum
            briefInfoDf_loanTradeCreditInfo.loc[
                loanTradeCreditInfoIndex, '相关还款责任总担保金额+总还款责任金额（个人+企业）'] = totalGuaranteeAmt
            briefInfoDf_loanTradeCreditInfo.loc[
                loanTradeCreditInfoIndex, '相关还款责任账户总担保余额+总其他余额（个人+企业）'] = totalGuaranteeBalance
            if totalGuaranteeAmt != 0:
                briefInfoDf_loanTradeCreditInfo.loc[
                    loanTradeCreditInfoIndex, '相关还款责任账户总担保余额+总其他余额（个人+企业）/相关还款责任账户总担保金额+总其他金额（个人+企业）'] = \
                    round(totalGuaranteeBalance / totalGuaranteeAmt, 2)
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任担保总账户数-个人'] = perAccountNum
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总担保金额-个人'] = perGuaranteeAmt
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总担保余额-个人'] = perGuaranteeBalance
            if perGuaranteeBalance != 0:
                briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总担保余额-个人/相关还款责任总担保金额-个人'] = round(
                    perGuaranteeBalance / perGuaranteeBalance, 2)
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总账户数-企业'] = orgAccountNum
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总担保金额-企业'] = orgGuaranteeAmt
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总担保余额-企业'] = orgGuaranteeBalance
            if orgGuaranteeAmt != 0:
                briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总担保余额-企业/相关还款责任总担保金额-企业'] = round(
                    orgGuaranteeBalance / orgGuaranteeAmt, 2)
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总账户数-担保责任'] = guaranteeAccountNum
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总担保金额-担保责任'] = guaranteeAmt
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任账户总担保余额-担保责任'] = guaranteeBalance
            if guaranteeAmt != 0:
                briefInfoDf_loanTradeCreditInfo.loc[
                    loanTradeCreditInfoIndex, '相关还款责任总担保余额-担保责任/相关还款责任总担保金额-担保责任'] = round(
                    guaranteeBalance / guaranteeAmt, 2)

            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总账户数-其他'] = otherAccountNum
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总担保金额-其他'] = otherPaymentAmt
            briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任总担保余额-其他'] = otherPaymentBalance
            if otherPaymentAmt != 0:
                briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '相关还款责任账户总担保余额-其他/相关还款责任账户总担保金额-其他'] = round(otherPaymentBalance / otherPaymentAmt, 2)
            # print(briefInfoDf_loanTradeCreditInfo.to_json(orient="index", force_ascii=False))
    #解析公共信息汇总
    def parsePublicInfoBrief(self,docXml):
        comminfosmry = docXml.documentElement.getElementsByTagName("COMMINFOSMRY")
        for item in comminfosmry:
            self.saveNode(item)
        if len(comminfosmry)>0:
            publicInfoBriefDf.loc[publicInfoBriefIndex, '欠税信息-记录数'] = utils.toInt(self.getNodeData(comminfosmry[0],"RECORDNUM"))
            publicInfoBriefDf.loc[publicInfoBriefIndex, '欠税信息-涉及金额'] = utils.replaceAmt(self.getNodeData(comminfosmry[0],"INVOLVEMONEY"))
            publicInfoBriefDf.loc[publicInfoBriefIndex, '民事判决信息-记录数'] = utils.toInt(self.getNodeData(comminfosmry[1],"RECORDNUM"))
            publicInfoBriefDf.loc[publicInfoBriefIndex, '民事判决信息-涉及金额'] = utils.replaceAmt(self.getNodeData(comminfosmry[1],"INVOLVEMONEY"))
            publicInfoBriefDf.loc[publicInfoBriefIndex, '强制执行信息-记录数'] = utils.toInt(self.getNodeData(comminfosmry[2],"RECORDNUM"))
            publicInfoBriefDf.loc[publicInfoBriefIndex, '强制执行信息-涉及金额'] = utils.replaceAmt(self.getNodeData(comminfosmry[2],"INVOLVEMONEY"))
            publicInfoBriefDf.loc[publicInfoBriefIndex, '行政处罚信息-记录数'] = utils.toInt(self.getNodeData(comminfosmry[3],"RECORDNUM"))
            publicInfoBriefDf.loc[publicInfoBriefIndex, '行政处罚信息-涉及金额'] = utils.replaceAmt(self.getNodeData(comminfosmry[3],"INVOLVEMONEY"))



    # 解析查询信息汇总
    def parseQueryRecordSum(self,docXml):
        queryrecordsumsg = docXml.documentElement.getElementsByTagName("QUERYRECORDSUMSG")
        if len(queryrecordsumsg) > 0:
            self.saveNode(queryrecordsumsg[0])
            queryRecordSumDf.loc[queryRecordSumIndex, '近1月内的查询机构数-贷款审批'] = int(self.getNodeData(queryrecordsumsg[0],"LAST1ORGSP"))
            queryRecordSumDf.loc[queryRecordSumIndex, '近1月内的查询机构数-信用卡审批'] = int(self.getNodeData(queryrecordsumsg[0],"LAST1CREDITSP"))
            queryRecordSumDf.loc[queryRecordSumIndex, '近1月内的查询次数-贷款审批'] = int(self.getNodeData(queryrecordsumsg[0],"NUMSP"))
            queryRecordSumDf.loc[queryRecordSumIndex, '近1月内的查询次数-信用卡审批'] = int(self.getNodeData(queryrecordsumsg[0],"NUMCREDITSP"))
            queryRecordSumDf.loc[queryRecordSumIndex, '近1月内的查询次数-本人查询'] = int(self.getNodeData(queryrecordsumsg[0],"LAST1NUMSELF"))
            queryRecordSumDf.loc[queryRecordSumIndex, '近2年内的查询次数-贷后管理'] = int(self.getNodeData(queryrecordsumsg[0],"L2YNUMDH"))
            queryRecordSumDf.loc[queryRecordSumIndex, '近2年内的查询次数-担保资格审查'] = int(self.getNodeData(queryrecordsumsg[0],"L2YNUMDBZG"))
        # print(queryRecordSumDf.to_json(orient="index", force_ascii=False))
    # 解析查询记录明细
    def parseQueryInfoDetail(self,reportTime,docXml):
        queryRecordsg = docXml.documentElement.getElementsByTagName("QUERYRECORDSG");
        if len(queryRecordsg)>0:
            df = pd.DataFrame()
            for i in range(0, len(queryRecordsg)):
                self.saveNode(queryRecordsg[i])
                df.loc[i, 1] = self.getNodeData(queryRecordsg[i], "QUERYDATE")
                df.loc[i, 2] = self.getNodeData(queryRecordsg[i], "QUERYORG")
                queryReson  = self.getNodeData(queryRecordsg[i], "QUERYREASONDESC")
                df.loc[i, 3] = queryReson
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近1月查询次数'] =qip.getLastMonthQueryTimes(df, 1, "",reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近3月查询次数'] =qip.getLastMonthQueryTimes(df, 3, "",reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近6月查询次数'] =qip.getLastMonthQueryTimes(df, 6, "",reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近12月查询次数'] =qip.getLastMonthQueryTimes(df, 12, "",reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近1个月查询机构数'] =qip.getLastMonthQueryOrgTimes(df, 1, "", reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近3个月查询机构数'] =qip.getLastMonthQueryOrgTimes(df, 3, "", reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近6个月查询机构数'] =qip.getLastMonthQueryOrgTimes(df, 6, "", reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近12个月查询机构数'] =qip.getLastMonthQueryOrgTimes(df, 12, "", reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近24个月查询机构数'] =qip.getLastMonthQueryOrgTimes(df, 24, "", reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近3月查询次数贷款审批'] =qip.getLastMonthQueryTimes(df, 3, consts.loanApprove, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近3月查询次数信用卡审批'] =qip.getLastMonthQueryTimes(df, 3, consts.creditCard, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近6月查询次数贷款审批'] =qip.getLastMonthQueryTimes(df, 6, consts.loanApprove, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近6月查询次数信用卡审批'] = qip.getLastMonthQueryTimes(df, 6, consts.creditCard, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近12月查询次数贷款审批'] = qip.getLastMonthQueryTimes(df, 12, consts.loanApprove, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近12月查询次数信用卡审批'] =qip.getLastMonthQueryTimes(df, 12, consts.creditCard, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近3月查询机构数贷款审批'] =qip.getLastMonthQueryOrgTimes(df, 3, consts.loanApprove, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近3月查询机构数信用卡审批'] =qip.getLastMonthQueryOrgTimes(df, 3, consts.creditCard, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近6月查询机构数贷款审批'] =qip.getLastMonthQueryOrgTimes(df, 6, consts.loanApprove, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近6月查询机构数信用卡审批'] = qip.getLastMonthQueryOrgTimes(df, 6, consts.creditCard,reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近12月查询机构数贷款审批'] = qip.getLastMonthQueryOrgTimes(df, 12, consts.loanApprove, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近12月查询机构数信用卡审批'] = qip.getLastMonthQueryOrgTimes(df, 12, consts.creditCard,reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近3个月担保资格审查查询次数'] = qip.getLastMonthQueryTimes(df, 3, consts.insuranceAprove,reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近6个月担保资格审查查询次数'] = qip.getLastMonthQueryTimes(df, 6, consts.insuranceAprove,reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '近12个月担保资格审查查询次数'] = qip.getLastMonthQueryTimes(df, 12, consts.insuranceAprove,reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近6个月贷后管理查询次数'] = qip.getLastMonthQueryTimes(df, 6, consts.loanAfterMgr,reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近12个月贷后管理查询次数'] = qip.getLastMonthQueryTimes(df, 12, consts.loanAfterMgr,reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最后一次查询距离现在的月数贷款审批'] = qip.getLastTimeQueryMonth(df, consts.loanApprove,reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近24个月贷后管理查询次数']  = qip.getLastMonthQueryTimes(df, 24, consts.loanAfterMgr, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近24个月贷款审批审批次数'] = qip.getLastMonthQueryTimes(df, 24, consts.loanApprove, reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近24个月信用卡审批查询次数'] = qip.getLastMonthQueryTimes(df, 24, consts.creditCard,reportTime)
            queryRecordDetailDf.loc[queryRecordDetailIndex, '最近24个月担保资格审查查询次数'] = qip.getLastMonthQueryTimes(df, 24, consts.insuranceAprove,reportTime)
            #三寿 100年所有信用卡审批
            sanshouDf.loc[sanshouIndex, '贷后管理最近3个月内的机构查询次数'] = qip.getLastMonthQueryTimes(df, 3, consts.loanAfterMgr,reportTime)
            sanshouDf.loc[sanshouIndex, '信用卡审批所有机构查询次数'] =qip.getLastMonthQueryTimes(df, 12*100, consts.creditCard, reportTime)
            sanshouDf.loc[sanshouIndex, '所有本人查询次数'] = qip.getSelfLastMonthQueryTimes(df, 12 * 100,reportTime)
            sanshouDf.loc[sanshouIndex, '最近1年内本人查询次数'] = qip.getSelfLastMonthQueryTimes(df, 12, reportTime)
            sanshouDf.loc[sanshouIndex, '最近2年内本人查询次数'] = qip.getSelfLastMonthQueryTimes(df, 24, reportTime)
            # print(queryRecordDetailDf.to_json(orient="index", force_ascii=False))

    def parseHousingFundRcd(self,reportTime,docXml):
        housingfund = docXml.documentElement.getElementsByTagName("HOUSINGFUND");
        if len(housingfund) >0 :
            df = pd.DataFrame()
            for i in range(0, len(housingfund)):
                self.saveNode(housingfund[i])
                df.loc[i, 0] = self.getNodeData(housingfund[i], "ADDRESSDESC")#参缴地
                df.loc[i, 1] = self.getNodeData(housingfund[i], "PAYDATE")#参缴日期
                df.loc[i, 2] = self.getNodeData(housingfund[i], "STATUSDESC")#缴费状态
                df.loc[i, 3] = self.getNodeData(housingfund[i], "BEGINMONTH")#初缴月份
                df.loc[i, 4] = self.getNodeData(housingfund[i], "ENDMONTH")#缴至月份
                df.loc[i, 5] = self.getNodeData(housingfund[i], "COMPROPORTION")#个人存缴比例
                df.loc[i, 6] = self.getNodeData(housingfund[i], "PEPROPORTION")#个人存缴比例
                df.loc[i, 7] = utils.replaceAmt(self.getNodeData(housingfund[i], "MONTHMONEY"))#月缴存额
                df.loc[i, 8] = self.getNodeData(housingfund[i], "COMPANY")#缴费单位
                df.loc[i, 9] = self.getNodeData(housingfund[i], "UPDATEDATE")#信息更新日期
            lastHousingFundRcdDf = df.sort_values(by=9, ascending=(False)).reset_index(drop=True)
            lastHousingFundRcdDf = lastHousingFundRcdDf[0:1]  # 最新
            row1 = lastHousingFundRcdDf.loc[0, :].dropna().reset_index(drop=True)
            housingFundRcdDf.loc[housingFundRcdIndex, '参缴地'] = row1[0]
            housingFundRcdDf.loc[housingFundRcdIndex, '参缴日期'] = row1[1]
            housingFundRcdDf.loc[housingFundRcdIndex, '初缴月份'] = row1[3]  # 初缴日期
            housingFundRcdDf.loc[housingFundRcdIndex, '缴至月份'] = row1[4]
            housingFundRcdDf.loc[housingFundRcdIndex, '缴费状态'] = row1[2]
            housingFundRcdDf.loc[housingFundRcdIndex, '月缴存额'] = row1[7]
            housingFundRcdDf.loc[housingFundRcdIndex, '个人存缴比例'] = row1[6]
            housingFundRcdDf.loc[housingFundRcdIndex, '单位存缴比例'] = row1[5]
            housingFundRcdDf.loc[housingFundRcdIndex, '缴费单位'] = row1[8]  # 扣缴单位
            housingFundRcdDf.loc[housingFundRcdIndex, '信息更新日期'] = row1[9]
            lastDateStr = utils.getLastMonthDate(reportTime, 12)
            avgHousingFundDf = df[df[4] >= lastDateStr]
            housingFundRcdDf.loc[housingFundRcdIndex, '最近1年公积金平均值'] = round(np.mean(avgHousingFundDf[7]), 2)
            lastDateStr = utils.getLastMonthDate(reportTime, 12 * 3)
            avgHousingFundDf = df[df[4] >= lastDateStr]
            housingFundRcdDf.loc[housingFundRcdIndex, '最近3年公积金平均值'] = round(np.mean(avgHousingFundDf[7]), 2)
            # print(housingFundRcdDf.to_json(orient="index", force_ascii=False))

    # 合并被追偿信息
    def mergeRecoveryInfoDf(self, reportTime,docXml):
        tmpNode = docXml.documentElement.getElementsByTagName("FORCEDREPAYINFOBOLS");
        recoveryInfoMergeDf = pd.DataFrame(columns=dfParser.dfHeaderRecoveryInfo)#合并df
        if len(tmpNode)>0:
            forcedrepayinfobols = docXml.documentElement.getElementsByTagName("FORCEDREPAYINFOBOLS")[0]
            creditacctinfobo = forcedrepayinfobols.getElementsByTagName("CREDITACCTINFOBO");
            for i in range(0,len(creditacctinfobo)):
                creditbaseinfobo = creditacctinfobo[i].getElementsByTagName("CREDITBASEINFOBO")[0]
                self.saveNodeWithType(creditbaseinfobo,"FORCEDREPAYINFOBOLS")
                newperformancebo = creditacctinfobo[i].getElementsByTagName("NEWPERFORMANCEBO")[0]
                self.saveNodeWithType(newperformancebo,"FORCEDREPAYINFOBOLS")
                # latemonthlyperformancebo = creditacctinfobo[i].getElementsByTagName("LATEMONTHLYPERFORMANCEBO")
                # latemonthlyperformancebo = latemonthlyperformancebo[0]
                specialtradebaseinfosg = creditacctinfobo[i].getElementsByTagName("SPECIALTRADEBASEINFOSG")
                recoveryInfoDf = pd.DataFrame(columns=dfParser.dfHeaderCreditCard, index=[0])
                recoveryInfoDf.loc[0, '账户编号'] = self.getNodeData(creditbaseinfobo,"ACCTNO")
                recoveryInfoDf.loc[0, '管理机构'] = self.getNodeData(creditbaseinfobo,"BUSIORGCODE")
                recoveryInfoDf.loc[0, '业务种类'] = self.getNodeData(creditbaseinfobo,"LOANTYPEDESC")
                recoveryInfoDf.loc[0, '债权接收日期'] = self.getNodeData(creditbaseinfobo,"OPENDATE")
                recoveryInfoDf.loc[0, '债权金额'] = utils.replaceAmt(self.getNodeData(creditbaseinfobo,"LOANAMT"))
                recoveryInfoDf.loc[0, '债权转移时的还款状态'] = self.getNodeData(creditbaseinfobo,"RPYACCOUNTSTATUSDESC")
                recoveryInfoDf.loc[0, '账户状态'] = self.getNodeData(newperformancebo,"ACCTSTATUSDESC")
                accountStatus = self.getNodeData(newperformancebo,"ACCTSTATUSDESC")# 账户状态
                # if accountStatus == "":
                #     accountStatus = self.getNodeData(latemonthlyperformancebo, "ACCTSTATUDESC")
                if len(specialtradebaseinfosg)>0:
                    self.saveNodeWithType(specialtradebaseinfosg[0],"FORCEDREPAYINFOBOLS")
                    recoveryInfoDf.loc[0, '特殊交易类型'] = self.getNodeData(specialtradebaseinfosg[0],"TRANTYPEDESC")
                    recoveryInfoDf.loc[0, '发生日期'] = self.getNodeData(specialtradebaseinfosg[0],"TRANDATE")
                    recoveryInfoDf.loc[0, '变更月份'] = utils.toInt(self.getNodeData(specialtradebaseinfosg[0],"TRANMONTH"))
                    recoveryInfoDf.loc[0, '发生金额'] = utils.replaceAmt(self.getNodeData(specialtradebaseinfosg[0],"TRANAMT"))
                    recoveryInfoDf.loc[0, '明细记录'] = self.getNodeData(specialtradebaseinfosg[0],"DETRECORD")
                if accountStatus == "催收":
                    recoveryInfoDf.loc[0, '余额'] = utils.replaceAmt(self.getNodeData(newperformancebo,"BALANCE"))
                    recoveryInfoDf.loc[0, '最近一次还款日期'] =self.getNodeData(newperformancebo, "REPAYDATE");
                else:
                    recoveryInfoDf.loc[0, '账户关闭日期'] = self.getNodeData(newperformancebo,"SETTLEDATE")
                # recoveryInfoDf.loc[0, '截至日期'] = self.getNodeData(newperformancebo,"DUEDATE")
                recoveryInfoMergeDf = pd.concat([recoveryInfoMergeDf, recoveryInfoDf], axis=0,ignore_index=True);
        return recoveryInfoMergeDf

    # 合并贷款记录dataframe为宽表
    def mergeLoanDf(self, reportTime,docXml,type):
        tmpNode = docXml.documentElement.getElementsByTagName(type)
        loanAccountDfs = []
        if len(tmpNode)>0:
            noloopcreditaccountbols = docXml.documentElement.getElementsByTagName(type)[0]
            creditacctinfobo = noloopcreditaccountbols.getElementsByTagName("CREDITACCTINFOBO");
            loanDf = pd.DataFrame(columns=dfParser.dfHeaderLoan, index=[0])
            for i in range(0, len(creditacctinfobo)):
                dfObj = {}
                loanDfObj = loanDf.to_json(orient="index",force_ascii=False)
                loanDfObj = json.loads(loanDfObj)['0']
                creditbaseinfobo = creditacctinfobo[i].getElementsByTagName("CREDITBASEINFOBO")[0]
                self.saveNodeWithType(creditbaseinfobo,type)
                newperformancebo = creditacctinfobo[i].getElementsByTagName("NEWPERFORMANCEBO")[0]
                self.saveNodeWithType(newperformancebo,type)
                latemonthlyperformancebo = creditacctinfobo[i].getElementsByTagName("LATEMONTHLYPERFORMANCEBO")
                latemonthlyperformancebo = latemonthlyperformancebo[0]
                self.saveNodeWithType(latemonthlyperformancebo,type)
                specialtradebaseinfosg = creditacctinfobo[i].getElementsByTagName("SPECIALTRADEBASEINFOSG")
                fiveyearhisrpyinfols = creditacctinfobo[i].getElementsByTagName("FIVEYEARHISRPYINFOSG")
                loanDfObj['账户编号'] = self.getNodeData(creditbaseinfobo, "ACCTNO")
                loanDfObj['管理机构'] = self.getNodeData(creditbaseinfobo, "BUSIORGCODE")
                loanDfObj['账户标识'] = self.getNodeData(creditbaseinfobo, "ACCTCODE")
                loanDfObj['开立日期'] = self.getNodeData(creditbaseinfobo, "OPENDATE")
                loanDfObj['到期日期'] = self.getNodeData(creditbaseinfobo, "DUEDATE")
                if type=='LOOPCREDITACCOUNTBOLS':#循环贷款账户
                    loanDfObj['借款金额（本金)'] = utils.replaceAmt(self.getNodeData(creditbaseinfobo, "ACCTCREDLINE"))
                else:
                    loanDfObj['借款金额（本金)'] = utils.replaceAmt(self.getNodeData(creditbaseinfobo, "LOANAMT"))
                loanDfObj['账户币种'] = self.getNodeData(creditbaseinfobo, "MONEYTYPEDESC")

                loanDfObj['业务种类'] = self.getNodeData(creditbaseinfobo, "LOANTYPEDESC")
                loanDfObj['担保方式'] = self.getNodeData(creditbaseinfobo, "GUARMODEDESC")
                loanDfObj['还款期数'] = utils.toInt(self.getNodeData(creditbaseinfobo, "REPAYPRD"))
                loanDfObj['还款频率'] = self.getNodeData(creditbaseinfobo, "REPAYFREQCYDESC")
                loanDfObj['还款方式'] = self.getNodeData(creditbaseinfobo, "REPAYDESC")
                loanDfObj['共同借款标志'] = self.getNodeData(creditbaseinfobo, "COMMBORROWERMAKERDESC")
                # loanDfObj['截至日期'] = formatDateJz(df4[0]);

                accountStatus = self.getNodeData(latemonthlyperformancebo, "ACCTSTATUDESC")
                if accountStatus == "":
                    accountStatus = self.getNodeData(newperformancebo,"ACCTSTATUSDESC")
                loanDfObj['账户状态'] = accountStatus;
                if accountStatus != "结清" and accountStatus != "呆账" and accountStatus != "转出":
                    loanDfObj['五级分类'] = self.getNodeData(latemonthlyperformancebo, "FIVECATEDESC");
                    loanDfObj['余额（本金)'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "BUSIBAL"))
                    loanDfObj['剩余还款期数'] = utils.toInt(self.getNodeData(latemonthlyperformancebo, "REMREPPRD"))
                    loanDfObj['本月应还款'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "CURRPYAMT"))
                    loanDfObj['应还款日'] = self.getNodeData(latemonthlyperformancebo, "SETTDATE");
                    loanDfObj['本月实还款'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "ACTRPYAMT"))
                    loanDfObj['最近一次还款日期'] = self.getNodeData(latemonthlyperformancebo, "LATRPYDATE");
                    loanDfObj['当前逾期期数'] = utils.toInt(self.getNodeData(latemonthlyperformancebo, "OVERDPRD"))
                    loanDfObj['当前逾期总额'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "TOTOVERD"))
                    loanDfObj['逾期31-60天未还本金'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "OVERD3160PRINC"))
                    loanDfObj['逾期61-90天未还本金'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "OVERD6190PRINC"))
                    loanDfObj['逾期91-180天未还本金'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "OVERD91180PRINC"))
                    loanDfObj['逾期180天以上未还本金'] =  utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "OVERDPRINCOVE180"))
                    # loanDfObj['截至日期月份'] = utils.difMonthReportTime(formatDateJz(df4[0]), reportTime);  # 截止日期离报告时间月份
                elif accountStatus == "结清":
                    loanDfObj['账户关闭日期'] = self.getNodeData(newperformancebo,"SETTLEDATE")
                elif accountStatus == "转出":
                    loanDfObj['转出月份'] = self.getNodeData(newperformancebo,"ROLLOUTDATE")
                elif accountStatus == "呆账":
                    loanDfObj['余额（本金)'] = utils.replaceAmt(self.getNodeData(newperformancebo,"BALANCE"))
                    loanDfObj['最近一次还款日期'] = self.getNodeData(newperformancebo, "REPAYDATE")
                loanDfObj['管理机构类型'] = self.getNodeData(creditbaseinfobo, "MANAGEORGTYPEDESC")
                # loanDf = pd.DataFrame(loanDfObj)
                dfObj["loanDf"] = loanDfObj;
                #计算流动负债，汇算才需要计算
                # try:
                #     self.getDebt(loanDf,reportTime)
                # except Exception:
                #     logger.error(traceback.print_exc())
                #     logger.error(traceback.format_exc())
                # 解析还款记录
                loanPayRecord = None
                # 特殊交易
                specialTradeDf = None
                # if(len(fiveyearhisrpyinfols)>0):
                #     fiveyearhisrpyinfols = fiveyearhisrpyinfols[0]
                if accountStatus == "正常":  # 正常
                    recordIndexBegin = 11
                    loanPayRecord = self.mergeLoanPayRecordDf(fiveyearhisrpyinfols, loanPayRecord)
                    specialTradeDf = self.mergeLoanSpecialTrade(specialtradebaseinfosg, specialTradeDf)
                elif accountStatus == "逾期":  # 如果长度小于12后续无需处理
                    # 14 11 有提前最新还款记录的为14
                    recordIndexBegin = 11
                    loanPayRecord = self.mergeLoanPayRecordDf(fiveyearhisrpyinfols, loanPayRecord)
                    specialTradeDf = self.mergeLoanSpecialTrade(specialtradebaseinfosg, specialTradeDf)
                elif accountStatus == "结清":
                    recordIndexBegin = 9
                    loanPayRecord = self.mergeLoanPayRecordDf(fiveyearhisrpyinfols, loanPayRecord)
                    specialTradeDf = self.mergeLoanSpecialTrade(specialtradebaseinfosg, specialTradeDf)
                elif accountStatus == "呆账" or accountStatus == "转出":
                    recordIndexBegin = 9
                    loanPayRecord = self.mergeLoanPayRecordDf(fiveyearhisrpyinfols, loanPayRecord)
                    specialTradeDf = self.mergeLoanSpecialTrade(specialtradebaseinfosg, specialTradeDf)
                else:
                    loanPayRecord = self.mergeLoanPayRecordDf(fiveyearhisrpyinfols, loanPayRecord)
                    specialTradeDf = self.mergeLoanSpecialTrade(specialtradebaseinfosg, specialTradeDf)
                    print("===================还款状态不满足条件===================#"+self.getNodeData(creditbaseinfobo, "ACCTNO")+"#"+accountStatus)
                dfObj["loanPayRecordDf"] = loanPayRecord
                dfObj["specialTradeDf"] = specialTradeDf
                loanAccountDfs.append(dfObj)
        return loanAccountDfs

    #计算流动负债
    def getDebt(self, loanDf, reportTime):
        if loanDf.loc[0, '账户状态'] != '结清' and loanDf.loc[0, '业务种类'] != '个人住房商业贷款' \
                and loanDf.loc[0, '业务种类'] != '个人商用房（含商住两用）贷款' and loanDf.loc[0, '业务种类'] != '个人住房公积金贷款' and loanDf.loc[
            0, '业务种类'] != '个人汽车消费贷款':
            # term1 / term2 / term3
            term1 = None
            term2 = None
            term3 = None
            debt_f12 = 0
            payRate = loanDf.loc[0, '还款频率']
            payDue = loanDf.loc[0, '还款期数']
            payRemainderDue = loanDf.loc[0, '剩余还款期数']
            openDate = loanDf.loc[0, '开立日期']
            expireDate = loanDf.loc[0, '到期日期']
            payoutAmt = loanDf.loc[0, '借款金额（本金)']  # 放款金额
            balance = loanDf.loc[0, '余额（本金)']
            # Step1 - 基础变量计算
            if payRate == '月':
                if str(payDue) != "nan" and payDue > 0:
                    term1 = payDue
                else:
                    term1 = utils.difMonthReportTimeFloor(openDate, expireDate)
                if str(payRemainderDue) != "nan" and payRemainderDue > 0:
                    term2 = payRemainderDue
                else:
                    term2 = utils.difMonthReportTimeFloor(reportTime, expireDate)
                term3 = term1 - term2
            else:
                term1 = utils.difMonthReportTimeFloor(openDate, expireDate)
                term2 = utils.difMonthReportTimeFloor(reportTime, expireDate)
                term3 = term1 - term2
            if term1 <= 0 or term2 <= 0 or term1 < term2 or openDate == None or expireDate == None:
                tag0 = 1
            else:
                tag0 = 0
            payloan = utils.null2Zero(payoutAmt) - utils.null2Zero(balance)
            payloan = max(0, payloan)
            termpct_12 = max(0, 1 - 0.25 * int((term2 - 1) / 3))
            if loanDf.loc[0, '担保方式'] == '抵押':
                tag1 = 1
            else:
                tag1 = 0
            curActualPay = loanDf.loc[0, '本月实还款']
            curShouldPay = loanDf.loc[0, '本月应还款']
            if str(curActualPay) != "nan" and str(curShouldPay) != "nan":
                if max(curShouldPay, curActualPay) * term1 >= payoutAmt * 1.5:
                    tag2 = 1
                elif max(curShouldPay, curActualPay) * term2 >= balance * 1.5:
                    tag2 = 1
                elif term2 > 0 and term3 > 0 and payloan / term3 >= 1.5 * (balance / term2):
                    tag2 = 1
                elif term2 > 0 and term3 > 0 and balance / term2 >= 1.5 * (payloan / term3):
                    tag2 = 1
                elif payoutAmt < balance:
                    tag2 = 1
                else:
                    tag2 = 0
            if tag1 == 1:
                pay_rat = 0.0036
            else:
                pay_rat = 0.0083
            if tag1 == 1:
                mthly_intr = 0.07 / 12
            else:
                mthly_intr = 0.18 / 12
            totpayment1 = payoutAmt * mthly_intr * term1 + payoutAmt
            totpayment2 = balance * mthly_intr * term2 + balance
            # Step2-贷款分流负债统计
            if tag0 == 1:
                debt_f12 = balance * (1 + pay_rat * 12)
            elif tag1 == 1:
                debt_f12 = payoutAmt * pay_rat * 12
            elif payRate != '月' or payRate == None:
                monthy_pay_ref1 = totpayment1 / term1;
                monthy_pay_ref2 = totpayment2 / term2;
                monthy_pay_final = max(monthy_pay_ref1, monthy_pay_ref2);
                debt_f12 = min(monthy_pay_final * 12, totpayment1, totpayment2);
            elif tag2 == 1:
                debt_f12 = min(balance * mthly_intr * min(term2, 12) + termpct_12 * balance, totpayment1, totpayment2);
            else:
                monthy_pay_ref1 = ((pay_rat * term1 + 1) * payoutAmt) / term1
                if term2 == 0:
                    print(loanDf.loc[0, '账户编号'])
                monthy_pay_ref2 = ((pay_rat * term2 + 1) * balance) / term2
                monthy_pay_final = max(curShouldPay, monthy_pay_ref1, monthy_pay_ref2);
                debt_f12 = min(monthy_pay_final * 12, balance * (1 + pay_rat * 12), balance * (1 + mthly_intr * term2));
            loanDf.loc[0, '贷款负债'] = debt_f12
    def mergeCreditCardDf(self,reportTime,docXml):
        creditCardAccountDfs = []
        tmpNode = docXml.documentElement.getElementsByTagName("CREDITCARDACCOUNTBOLS");
        if len(tmpNode)>0:
            noloopcreditaccountbols = docXml.documentElement.getElementsByTagName("CREDITCARDACCOUNTBOLS")[0]
            creditacctinfobo = noloopcreditaccountbols.getElementsByTagName("CREDITACCTINFOBO");
            for i in range(0, len(creditacctinfobo)):
                dfObj = {}
                creditCardDf = pd.DataFrame(columns=dfParser.dfHeaderCreditCard, index=[0])
                creditbaseinfobo = creditacctinfobo[i].getElementsByTagName("CREDITBASEINFOBO")[0]
                self.saveNodeWithType(creditbaseinfobo,"CREDITCARDACCOUNTBOLS")
                newperformancebo = creditacctinfobo[i].getElementsByTagName("NEWPERFORMANCEBO")[0]
                self.saveNodeWithType(newperformancebo,"CREDITCARDACCOUNTBOLS")
                latemonthlyperformancebo = creditacctinfobo[i].getElementsByTagName("LATEMONTHLYPERFORMANCEBO")
                latemonthlyperformancebo = latemonthlyperformancebo[0]
                self.saveNodeWithType(latemonthlyperformancebo,"CREDITCARDACCOUNTBOLS")
                bigquotabaselnfosg  = creditacctinfobo[i].getElementsByTagName("BIGQUOTABASELNFOSG")
                fiveyearhisrpyinfols = creditacctinfobo[i].getElementsByTagName("FIVEYEARHISRPYINFOSG")
                creditCardDf.loc[0, '账户编号'] = self.getNodeData(creditbaseinfobo, "ACCTNO")
                creditCardDf.loc[0, '发卡机构'] = self.getNodeData(creditbaseinfobo, "BUSIORGCODE")
                creditCardDf.loc[0, '账户标识'] = self.getNodeData(creditbaseinfobo, "ACCTCODE")
                creditCardDf.loc[0, '开立日期'] = self.getNodeData(creditbaseinfobo, "OPENDATE")
                creditCardDf.loc[0, '账户授信额度'] = utils.replaceAmt(self.getNodeData(creditbaseinfobo, "ACCTCREDLINE"))
                creditCardDf.loc[0, '共享授信额度'] = utils.replaceAmt(self.getNodeData(creditbaseinfobo, "ORGCREDLINE"))
                # 排除美元027
                MONEYTYPEDESC = self.getNodeData(creditbaseinfobo, "MONEYTYPEDESC")
                if MONEYTYPEDESC.find('人民币')<0:
                    continue
                # if MONEYTYPEDESC.find('美元')>= 0:
                #     continue
                creditCardDf.loc[0, '币种'] = self.getNodeData(creditbaseinfobo, "MONEYTYPEDESC")
                creditCardDf.loc[0, '业务种类'] = self.getNodeData(creditbaseinfobo, "LOANTYPEDESC")
                creditCardDf.loc[0, '担保方式'] = self.getNodeData(creditbaseinfobo, "GUARMODEDESC")
                accountStatus = self.getNodeData(latemonthlyperformancebo, "ACCTSTATUDESC")
                if accountStatus == "":
                    accountStatus = self.getNodeData(newperformancebo,"ACCTSTATUSDESC")
                if accountStatus.find('未激活') < 0:  # 非未激活
                    if accountStatus == "呆账":
                        creditCardDf.loc[0, '账户状态'] = accountStatus
                        creditCardDf.loc[0, '余额'] = utils.replaceAmt(self.getNodeData(newperformancebo, "BALANCE"))
                        creditCardDf.loc[0, '最近一次还款日期'] = self.getNodeData(newperformancebo, "REPAYDATE")
                    elif accountStatus != '销户':
                        try:
                            creditCardDf.loc[0, '账户状态'] =accountStatus
                            creditCardDf.loc[0, '余额'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "BUSIBAL"))
                            creditCardDf.loc[0, '已用额度'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "ACCTBAL"))
                            dividePeriodBalance = self.getNodeData(latemonthlyperformancebo, "INSTBAL")  # 未出单的大额专项分期余额
                            creditCardDf.loc[0, '未出单的大额专项分期余额'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "INSTBAL"))
                            creditCardDf.loc[0, '剩余分期期数'] = utils.toInt(self.getNodeData(latemonthlyperformancebo, "REMREPPRD"))
                            creditCardDf.loc[0, '最近6个月平均使用额度'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "AVGUSEAMOUNT"))
                            creditCardDf.loc[0, '最大使用额'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "MAXUSEAMOUNT"))
                            creditCardDf.loc[0, '账单日'] = self.getNodeData(latemonthlyperformancebo, "SETTDATE")
                            creditCardDf.loc[0, '本月应还款'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "CURRPYAMT"))
                            creditCardDf.loc[0, '本月实还款'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "ACTRPYAMT"))
                            creditCardDf.loc[0, '最近一次还款日期'] = self.getNodeData(latemonthlyperformancebo, "LATRPYDATE");
                            creditCardDf.loc[0, '当前逾期期数'] = utils.toInt(self.getNodeData(latemonthlyperformancebo,"OVERDPRD"))
                            creditCardDf.loc[0, '当前逾期总额'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo,"TOTOVERD"))
                        except Exception:
                            logger.info("解析贷记卡异常-" + i)
                            info = sys.exc_info()
                            logger.error(info[0])
                            logger.error(info[1])
                            logger.error(traceback.extract_tb(info[2]))
                        if len(bigquotabaselnfosg) >0:
                            bigquotabaselnfosg = bigquotabaselnfosg[0]
                            self.saveNodeWithType(bigquotabaselnfosg,"CREDITCARDACCOUNTBOLS")
                            # '大额专项分期额度','分期额度生效日期','分期额度到期日期','已用分期金额']
                            creditCardDf.loc[0, '大额专项分期额度'] =  utils.replaceAmt(self.getNodeData(bigquotabaselnfosg, "SPECLINE"))
                            creditCardDf.loc[0, '分期额度生效日期'] = self.getNodeData(bigquotabaselnfosg, "SPECLINE")
                            creditCardDf.loc[0, '分期额度到期日期'] = self.getNodeData(bigquotabaselnfosg, "SPECENDDATE")
                            creditCardDf.loc[0, '已用分期金额'] =  utils.replaceAmt(self.getNodeData(bigquotabaselnfosg, "INSTAMT"))
                    else:
                        accountStatus = '销户'
                        creditCardDf.loc[0, '账户状态'] = '销户'
                else:  # 未激活
                    creditCardDf.loc[0, '账户状态'] = '未激活'
                    accountStatus = '未激活'
                dfObj["creditCardDf"] = creditCardDf;
                # 解析还款记录
                creditCardPayRecord = pd.DataFrame()
                if accountStatus == "正常" or accountStatus == "冻结" or accountStatus == "止付" or accountStatus == "银行止付":  # 正常
                    recordIndexBegin = 9;
                    creditCardPayRecord = self.mergeCreditCardPayRecordDf(fiveyearhisrpyinfols,creditCardPayRecord)
                elif accountStatus == "未激活":
                    recordIndexBegin = 0;
                elif accountStatus == "销户" or accountStatus == "呆账":
                    recordIndexBegin = 7;
                    creditCardPayRecord = self.mergeCreditCardPayRecordDf(fiveyearhisrpyinfols,creditCardPayRecord)
                else:
                    creditCardPayRecord = self.mergeCreditCardPayRecordDf(fiveyearhisrpyinfols, creditCardPayRecord)
                    print(self.getNodeData(creditbaseinfobo, "ACCTNO")+"===================还款状态不满足条件==================="+"#"+accountStatus)
                dfObj["creditCardPayRecordDf"] = creditCardPayRecord;
                creditCardAccountDfs.append(dfObj)
        return creditCardAccountDfs;

    def mergeCreditCardDfZ(self,reportTime,docXml):
        tmpNode = docXml.documentElement.getElementsByTagName("PASSCREDITCARDACCOUNTBOLS");
        creditCardAccountDfsZ = []
        if len(tmpNode)>0:
            noloopcreditaccountbols = docXml.documentElement.getElementsByTagName("PASSCREDITCARDACCOUNTBOLS")[0]
            creditacctinfobo = noloopcreditaccountbols.getElementsByTagName("CREDITACCTINFOBO");
            for i in range(0, len(creditacctinfobo)):
                dfObj = {}
                creditCardDf = pd.DataFrame(columns=dfParser.dfHeaderCreditCardZ, index=[0])
                creditbaseinfobo = creditacctinfobo[i].getElementsByTagName("CREDITBASEINFOBO")[0]
                self.saveNodeWithType(creditbaseinfobo,"PASSCREDITCARDACCOUNTBOLS")
                newperformancebo = creditacctinfobo[i].getElementsByTagName("NEWPERFORMANCEBO")[0]
                self.saveNodeWithType(newperformancebo,"PASSCREDITCARDACCOUNTBOLS")
                latemonthlyperformancebo = creditacctinfobo[i].getElementsByTagName("LATEMONTHLYPERFORMANCEBO")
                latemonthlyperformancebo = latemonthlyperformancebo[0]
                self.saveNodeWithType(latemonthlyperformancebo,"PASSCREDITCARDACCOUNTBOLS")
                fiveyearhisrpyinfols = creditacctinfobo[i].getElementsByTagName("FIVEYEARHISRPYINFOSG")
                creditCardDf.loc[0, '账户编号'] = self.getNodeData(creditbaseinfobo, "ACCTNO")
                creditCardDf.loc[0, '发卡机构'] = self.getNodeData(creditbaseinfobo, "BUSIORGCODE")
                creditCardDf.loc[0, '账户标识'] = self.getNodeData(creditbaseinfobo, "ACCTCODE")
                creditCardDf.loc[0, '开立日期'] = self.getNodeData(creditbaseinfobo, "OPENDATE")
                creditCardDf.loc[0, '账户授信额度'] = utils.replaceAmt(self.getNodeData(creditbaseinfobo, "ACCTCREDLINE"))
                creditCardDf.loc[0, '共享授信额度'] = utils.replaceAmt(self.getNodeData(creditbaseinfobo, "ORGCREDLINE"))
                creditCardDf.loc[0, '币种'] = self.getNodeData(creditbaseinfobo, "MONEYTYPEDESC")
                creditCardDf.loc[0, '担保方式'] = self.getNodeData(creditbaseinfobo, "GUARMODEDESC")
                MONEYTYPEDESC = self.getNodeData(creditbaseinfobo, "MONEYTYPEDESC")
                if MONEYTYPEDESC.find('人民币') < 0:
                    continue
                # if self.getNodeData(creditbaseinfobo, "MONEYTYPEDESC").find('美元') >= 0:
                #     continue
                accountStatus =  self.getNodeData(latemonthlyperformancebo, "ACCTSTATUDESC")
                if accountStatus == "":
                    accountStatus =self.getNodeData(newperformancebo,"ACCTSTATUSDESC")
                if accountStatus.find('未激活') < 0:  # 非未激活
                    if accountStatus == "呆账":
                        creditCardDf.loc[0, '账户状态'] = accountStatus
                        creditCardDf.loc[0, '余额'] = utils.replaceAmt(self.getNodeData(newperformancebo, "BALANCE"))
                        creditCardDf.loc[0, '透支余额'] = utils.replaceAmt(self.getNodeData(newperformancebo, "BALANCE"))
                        creditCardDf.loc[0, '最近一次还款日期'] = self.getNodeData(newperformancebo, "REPAYDATE")
                    elif accountStatus != '销户':
                        try:
                            creditCardDf.loc[0, '账户状态'] = accountStatus
                            creditCardDf.loc[0, '透支余额'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "BUSIBAL"))
                            creditCardDf.loc[0, '最近6个月平均透支余额'] = utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "LATSIXMNAVGOVDFTBAL"))
                            creditCardDf.loc[0, '最大透支余额'] =  utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "MAXOVERDRAFTBAL"))
                            creditCardDf.loc[0, '账单日'] = self.getNodeData(latemonthlyperformancebo, "SETTDATE")
                            creditCardDf.loc[0, '本月实还款'] =  utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "ACTRPYAMT"))
                            creditCardDf.loc[0, '最近一次还款日期'] = self.getNodeData(latemonthlyperformancebo, "LATRPYDATE");
                            creditCardDf.loc[0, '透支180天以上未付余额'] =  utils.replaceAmt(self.getNodeData(latemonthlyperformancebo, "OVERDRAWBAOVE180"))
                        except:
                            logger.info("解析准贷记卡异常-" + i);
                    else:
                        accountStatus = '销户'
                        creditCardDf.loc[0, '账户状态'] = '销户'
                        creditCardDf.loc[0, '最近一次还款日期'] = ""
                else:  # 未激活
                    creditCardDf.loc[0, '账户状态'] = '未激活'
                    accountStatus = '未激活'
                    creditCardDf.loc[0, '最近一次还款日期'] = ""
                dfObj["creditCardDfZ"] = creditCardDf;

                # 解析还款记录
                creditCardPayRecord = pd.DataFrame()
                if accountStatus == "正常" or accountStatus == "冻结" or accountStatus == "止付" or accountStatus == "银行止付":  # 正常
                    recordIndexBegin = 9;
                    creditCardPayRecord = self.mergeCreditCardPayRecordDf(fiveyearhisrpyinfols,creditCardPayRecord)
                elif accountStatus == "未激活":
                    recordIndexBegin = 0;
                elif accountStatus == "销户" or accountStatus == "呆账":
                    recordIndexBegin = 7;
                    creditCardPayRecord = self.mergeCreditCardPayRecordDf(fiveyearhisrpyinfols,creditCardPayRecord)
                else:
                    creditCardPayRecord = self.mergeCreditCardPayRecordDf(fiveyearhisrpyinfols, creditCardPayRecord)
                    print("账户#" + str(i + 1) + "#" + self.getNodeData(creditbaseinfobo, "ACCTNO")+"===================还款状态不满足条件==================="+"#"+accountStatus)
                dfObj["creditCardPayRecordDfZ"] = creditCardPayRecord;
                creditCardAccountDfsZ.append(dfObj)
        return creditCardAccountDfsZ;
    # 合并贷款还款记录明细
    def mergeLoanPayRecordDf(self,fiveyearhisrpyinfols, loanPayRecord):
        # for i in range(0, len(fiveyearhisrpyinfols)):
        #     loanPayRecordTmp = pd.DataFrame(columns=dfParser.dfHeaderLoanPayRecord, index=[0])
        #     loanPayRecordTmp.loc[0, '账户编号'] = self.getNodeData(fiveyearhisrpyinfols[i], "ACCTNO");
        #     loanPayRecordTmp.loc[0, '还款日期'] = self.getNodeData(fiveyearhisrpyinfols[i], "OVERDMON")+"-01";
        #     loanPayRecordTmp.loc[0, '还款状态值'] = utils.replaceAmt(self.getNodeData(fiveyearhisrpyinfols[i], "TOTACCTBAL"))
        #     loanPayRecordTmp.loc[0, '还款状态'] = self.getNodeData(fiveyearhisrpyinfols[i], "OVERDSTATUSCODE");
        #     loanPayRecord = pd.concat([loanPayRecord, loanPayRecordTmp], axis=0, ignore_index=True);
        # return loanPayRecord;
        ds = []
        # loanPayRecordTmp = pd.DataFrame(columns=dfParser.dfHeaderLoanPayRecord, index=[0])
        for i in range(0, len(fiveyearhisrpyinfols)):
            dsObj = {}
            # loanPayRecordTmp = pd.DataFrame(columns=dfParser.dfHeaderLoanPayRecord, index=[0])
            # loanPayRecordTmp.loc[0, '账户编号'] = self.getNodeData(fiveyearhisrpyinfols[i], "ACCTNO");
            # loanPayRecordTmp.loc[0, '还款日期'] = self.getNodeData(fiveyearhisrpyinfols[i], "OVERDMON") + "-01";
            # loanPayRecordTmp.loc[0, '还款状态值'] = utils.replaceAmt(self.getNodeData(fiveyearhisrpyinfols[i], "TOTACCTBAL"))
            # loanPayRecordTmp.loc[0, '还款状态'] = self.getNodeData(fiveyearhisrpyinfols[i], "OVERDSTATUSCODE");
            # loanPayRecord = pd.concat([loanPayRecord, loanPayRecordTmp], axis=0, ignore_index=True);
            dsObj["账户编号"] = self.getNodeData(fiveyearhisrpyinfols[i], "ACCTNO");
            dsObj["还款日期"] =  self.getNodeData(fiveyearhisrpyinfols[i], "OVERDMON") + "-01";
            dsObj["还款状态值"] = utils.replaceAmt(self.getNodeData(fiveyearhisrpyinfols[i], "TOTACCTBAL"))
            dsObj["还款状态"] = self.getNodeData(fiveyearhisrpyinfols[i], "OVERDSTATUSCODE");
            ds.append(dsObj)
            self.saveNodeWithType(fiveyearhisrpyinfols[i],"LOAN")
        # if len(ds)>0:
        #     loanPayRecordTmp = pd.DataFrame(ds)
        return ds;
        # 合并贷款还款记录明细

    def mergeCreditCardPayRecordDf(self, fiveyearhisrpyinfols, loanPayRecord):
        # for i in range(0, len(fiveyearhisrpyinfols)):
        #     loanPayRecordTmp = pd.DataFrame(columns=dfParser.dfHeaderLoanPayRecord, index=[0])
        #     loanPayRecordTmp.loc[0, '账户编号'] = self.getNodeData(fiveyearhisrpyinfols[i], "ACCTNO");
        #     loanPayRecordTmp.loc[0, '还款日期'] = self.getNodeData(fiveyearhisrpyinfols[i], "OVERDMON") + "-01";
        #     loanPayRecordTmp.loc[0, '还款状态值'] = utils.replaceAmt(self.getNodeData(fiveyearhisrpyinfols[i], "TOTACCTBAL"))
        #     loanPayRecordTmp.loc[0, '还款状态'] = self.getNodeData(fiveyearhisrpyinfols[i], "OVERDSTATUSCODE");
        #     loanPayRecord = pd.concat([loanPayRecord, loanPayRecordTmp], axis=0, ignore_index=True);
        # return loanPayRecord;
        ds = []
        # loanPayRecordTmp = pd.DataFrame(columns=dfParser.dfHeaderLoanPayRecord, index=[0])
        loanPayRecordTmp = None
        for i in range(0, len(fiveyearhisrpyinfols)):
            dsObj = {}
            # loanPayRecordTmp = pd.DataFrame(columns=dfParser.dfHeaderLoanPayRecord, index=[0])
            # loanPayRecordTmp.loc[0, '账户编号'] = self.getNodeData(fiveyearhisrpyinfols[i], "ACCTNO");
            # loanPayRecordTmp.loc[0, '还款日期'] = self.getNodeData(fiveyearhisrpyinfols[i], "OVERDMON") + "-01";
            # loanPayRecordTmp.loc[0, '还款状态值'] = utils.replaceAmt(self.getNodeData(fiveyearhisrpyinfols[i], "TOTACCTBAL"))
            # loanPayRecordTmp.loc[0, '还款状态'] = self.getNodeData(fiveyearhisrpyinfols[i], "OVERDSTATUSCODE");
            # loanPayRecord = pd.concat([loanPayRecord, loanPayRecordTmp], axis=0, ignore_index=True);
            dsObj["账户编号"] = self.getNodeData(fiveyearhisrpyinfols[i], "ACCTNO");
            dsObj["还款日期"] =  self.getNodeData(fiveyearhisrpyinfols[i], "OVERDMON") + "-01";
            dsObj["还款状态值"] = utils.replaceAmt(self.getNodeData(fiveyearhisrpyinfols[i], "TOTACCTBAL"))
            dsObj["还款状态"] = self.getNodeData(fiveyearhisrpyinfols[i], "OVERDSTATUSCODE");
            self.saveNodeWithType(fiveyearhisrpyinfols[i],"CREDITCARD")
            ds.append(dsObj)
        if len(ds)>0:
            loanPayRecordTmp = pd.DataFrame(ds)
        return loanPayRecordTmp;
        # 合并贷款还款记录明细

    #合并特殊交易
    def mergeLoanSpecialTrade(self,specialtradebaseinfosg, specialTradeDf):
        for i in range(0,len(specialtradebaseinfosg)):
            self.saveNode(specialtradebaseinfosg[i])
            specialTradeDfTmp = pd.DataFrame(columns = dfParser.dfHeaderLoanSpecialTrade, index=[0])
            specialTradeDfTmp.loc[0, '账户编号'] = self.getNodeData(specialtradebaseinfosg[i], "ACCTNO");
            specialTradeDfTmp.loc[0, '特殊交易类型'] = self.getNodeData(specialtradebaseinfosg[i], "TRANTYPEDESC");
            specialTradeDfTmp.loc[0, '发生日期'] = self.getNodeData(specialtradebaseinfosg[i], "TRANDATE");
            specialTradeDfTmp.loc[0, '变更月数'] = utils.toInt(self.getNodeData(specialtradebaseinfosg[i], "TRANMONTH"))
            specialTradeDfTmp.loc[0, '发生金额'] = utils.replaceAmt(self.getNodeData(specialtradebaseinfosg[i], "TRANAMT"))
            specialTradeDfTmp.loc[0, '明细记录'] = self.getNodeData(specialtradebaseinfosg[i], "DETRECORD");
            tradeType = self.getNodeData(specialtradebaseinfosg[i], "TRANTYPEDESC");
            content = self.getNodeData(specialtradebaseinfosg[i], "DETRECORD");
            # TODO  加工严重程度 逻辑待确认
            severity = None
            if tradeType == '提前还款' or tradeType == '提前结清':
                severity = 0
            elif tradeType == '其他' and content.find("提前") >= 0:
                severity = 0
            elif tradeType == '担保人（第三方）代偿':
                severity = 1
            # elif tradeType == "其他" and content.find("担保人代还取消")<0 and  content.find("合同展期")<0:
            #     severity = 2
            # 同时出现代还和取消
            elif tradeType == "其他" and (content.find("代还") < 0 or content.find("代偿") < 0) and content.find(
                    "取消") < 0 and content.find("合同展期") < 0:
                severity = 2
            elif (tradeType == "展期" or tradeType.find('延期') >= 0) and content.find("专升本或研究生入学展期") >= 0:
                severity = 2
            # elif tradeType == "其他" and (content.find("担保人代还取消")>=0 or  content.find("合同展期")>=0):
            #     severity = 3
            elif tradeType == "其他" and (
                    ((content.find("代还") < 0 or content.find("代偿") < 0) and content.find("取消") >= 0) or (
                    content.find("合同展期") >= 0)):
                severity = 3
            elif (tradeType == "展期" or tradeType.find('延期') >= 0) and content.find("专升本或研究生入学展期") < 0:
                severity = 3
            elif tradeType == "以资抵债":
                severity = 4
            specialTradeDfTmp.loc[0, '严重程度'] = severity
            specialTradeDf = pd.concat([specialTradeDf, specialTradeDfTmp], axis=0, ignore_index=True);
        return specialTradeDf;

    # 解析被追偿信息汇总
    def parseRecoveryInfoMergeDf(self,df):
        if not df.empty:
            recoveryMaxPayDf = df[df['债权转移时的还款状态'] != '']
            recoveryStatusCs = df[df['账户状态'] == '催收']
            if not recoveryMaxPayDf.empty:
                briefInfoDf_recoveryInfoSum.loc[recoveryInfoSumIndex, '债权转移时的最大还款状态'] = np.max(
                    recoveryMaxPayDf['债权转移时的还款状态']);
            briefInfoDf_recoveryInfoSum.loc[recoveryInfoSumIndex, '债权转移时属于催收状态的账户数'] = recoveryStatusCs.index.size;
            briefInfoDf_recoveryInfoSum.loc[recoveryInfoSumIndex, '债权转移时属于催收状态的账户数/被追偿信息总数'] = round(
                recoveryStatusCs.index.size / df.index.size, 2);
            # creditTradeDetailDf_recoveryInfo
            # 被追偿账户总数
            creditTradeDetailDf_recoveryInfo.loc[recoveryInfoIndex, '被追偿账户总数'] = df.index.size;
            creditTradeDetailDf_recoveryInfo.loc[recoveryInfoIndex, '被追偿业务种类'] = df['业务种类'].unique().size;
            creditTradeDetailDf_recoveryInfo.loc[recoveryInfoIndex, '最新一笔被追偿债券接收时间'] = np.max(df['债权接收日期']);
            creditTradeDetailDf_recoveryInfo.loc[recoveryInfoIndex, '总债权金额'] = np.max(df['债权金额']);
            creditTradeDetailDf_recoveryInfo.loc[recoveryInfoIndex, '债权转移时的最大还款状态'] = np.max(
                recoveryMaxPayDf['债权转移时的还款状态']);

    # 解析信贷交易明细-特殊交易
    def parseSpecialTrade(self,df):
        if not df.empty:
            creditTradeDetailHeader_specialTrade.loc[specialTradeIndex, '当前用户发生特殊交易的严重程度'] = np.max(df['严重程度'])  # 加工的指标
            maxChangeMonthIndex = np.argmax(np.abs(df['变更月数']))
            meanMonthValue = np.mean(np.abs(df['变更月数']))
            row0 = df.loc[maxChangeMonthIndex, :]
            settleDf = df[(df['特殊交易类型'] == '提前结清') | (df['特殊交易类型'] == '提前还款')]
            debtDf = df[(df['特殊交易类型'] == '以资抵债')]
            creditTradeDetailHeader_specialTrade.loc[specialTradeIndex, '用户发生特殊交易变更月数的最大差值'] = row0[3]
            creditTradeDetailHeader_specialTrade.loc[specialTradeIndex, '用户发生特殊交易变更月数的平均差值'] = round(meanMonthValue, 2)
            creditTradeDetailHeader_specialTrade.loc[specialTradeIndex, '用户特殊交易涉及的发生金额的最大值'] = np.max(df['发生金额'])
            creditTradeDetailHeader_specialTrade.loc[specialTradeIndex, '用户特殊交易涉及的发生金额的平均值'] = round(np.mean(df['发生金额']), 2)
            creditTradeDetailHeader_specialTrade.loc[specialTradeIndex, '用户所有帐户发生提前还款交易的次数统计'] = settleDf.index.size
            creditTradeDetailHeader_specialTrade.loc[specialTradeIndex, '用户所有帐户发生不良特殊交易的次数统计'] = debtDf.index.size;

    # 信贷交易明细-非循环贷账户
    def parseLoanAccountInfo(self,df):
        if not df.empty:
            loanAccountNum = int(briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '非循环贷账户账户数'])
            normalDf = df[(df['账户状态'] != '结清') & (df['账户状态'] != '转出') & (df['账户状态'] != '呆账')].reset_index(drop=True)
            normalDf = normalDf[0:loanAccountNum]  # 根据非循环贷账户数进行计算进行截取
            creditTradeDetailDf_loanAccountInfo.loc[loanInfoIndex, '本月应还款（合计）'] = np.sum(normalDf['本月应还款'])
            creditTradeDetailDf_loanAccountInfo.loc[loanInfoIndex, '本月实还款（合计）'] = np.sum(normalDf['本月实还款'])
            creditTradeDetailDf_loanAccountInfo.loc[loanInfoIndex, '最近一次还款日期'] = np.max(normalDf['最近一次还款日期'])
            creditTradeDetailDf_loanAccountInfo.loc[loanInfoIndex, '当前一共逾期期数'] = np.sum(normalDf['当前逾期期数'])
            creditTradeDetailDf_loanAccountInfo.loc[loanInfoIndex, '当前一共逾期总额'] = np.sum(normalDf['当前逾期总额'])
            creditTradeDetailDf_loanAccountInfo.loc[loanInfoIndex, '逾期31-60天未还本金（合计）'] = np.sum(
                normalDf['逾期31-60天未还本金'])
            creditTradeDetailDf_loanAccountInfo.loc[loanInfoIndex, '逾期61-90天未还本金（合计）'] = np.sum(
                normalDf['逾期61-90天未还本金'])
            creditTradeDetailDf_loanAccountInfo.loc[loanInfoIndex, '逾期91-180天未还本金（合计）'] = np.sum(
                normalDf['逾期91-180天未还本金'])
            creditTradeDetailDf_loanAccountInfo.loc[loanInfoIndex, '逾期180天以上未还本金（合计）'] = np.sum(
                normalDf['逾期180天以上未还本金'])

    # 信贷交易明细-循环额度分账户
    def parseCycleCreditAccountInfo(self,df):
        if not df.empty:
            normalDf = df[(df['账户状态'] != '结清') & (df['账户状态'] != '转出') & (df['账户状态'] != '呆账')].reset_index(drop=True)
            loanAccountNum = int(briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '非循环贷账户账户数'])
            cycleCreditAccountNum = int(briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环额度下分账户账户数'])
            normalDf = normalDf[loanAccountNum:(loanAccountNum + cycleCreditAccountNum)]
            if not normalDf.empty:
                creditTradeDetailDf_cycleCreditAccountInfo.loc[cycleCreditAccountInfoIndex, '本月应还款（合计）'] = np.sum(
                    normalDf['本月应还款'])
                creditTradeDetailDf_cycleCreditAccountInfo.loc[cycleCreditAccountInfoIndex, '本月实还款（合计）'] = np.sum(
                    normalDf['本月实还款'])
                creditTradeDetailDf_cycleCreditAccountInfo.loc[cycleCreditAccountInfoIndex, '最近一次还款日期'] = np.max(
                    normalDf['最近一次还款日期'])
                creditTradeDetailDf_cycleCreditAccountInfo.loc[cycleCreditAccountInfoIndex, '当前一共逾期期数'] = np.sum(
                    normalDf['当前逾期期数'])
                creditTradeDetailDf_cycleCreditAccountInfo.loc[cycleCreditAccountInfoIndex, '当前一共逾期总额'] = np.sum(
                    normalDf['当前逾期总额'])
                creditTradeDetailDf_cycleCreditAccountInfo.loc[
                    cycleCreditAccountInfoIndex, '逾期31-60天未还本金（合计）'] = np.sum(normalDf['逾期31-60天未还本金'])
                creditTradeDetailDf_cycleCreditAccountInfo.loc[
                    cycleCreditAccountInfoIndex, '逾期61-90天未还本金（合计）'] = np.sum(normalDf['逾期61-90天未还本金'])
                creditTradeDetailDf_cycleCreditAccountInfo.loc[
                    cycleCreditAccountInfoIndex, '逾期91-180天未还本金（合计）'] = np.sum(normalDf['逾期91-180天未还本金'])
                creditTradeDetailDf_cycleCreditAccountInfo.loc[
                    cycleCreditAccountInfoIndex, '逾期180天以上未还本金（合计）'] = np.sum(normalDf['逾期180天以上未还本金'])

    # 信贷交易明细-循环贷账户
    def parseCycleLoanAccountInfo(self,df):
        if not df.empty:
            normalDf = df[(df['账户状态'] != '结清') & (df['账户状态'] != '转出') & (df['账户状态'] != '呆账')]
            loanAccountNum = int(briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '非循环贷账户账户数'])
            cycleCreditAccountNum = int(briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环额度下分账户账户数'])
            cycleAccountNum = int(briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环贷账户账户数'])
            normalDf = normalDf[(loanAccountNum + cycleCreditAccountNum):normalDf.index.size]
            if not normalDf.empty:
                creditTradeDetailDf_cycleLoanAccountInfo.loc[cycleLoanAccountInfoIndex, '本月应还款（合计）'] = np.sum(
                    normalDf['本月应还款'])
                creditTradeDetailDf_cycleLoanAccountInfo.loc[cycleLoanAccountInfoIndex, '本月实还款（合计）'] = np.sum(
                    normalDf['本月实还款'])
                creditTradeDetailDf_cycleLoanAccountInfo.loc[cycleLoanAccountInfoIndex, '最近一次还款日期'] = np.max(
                    normalDf['最近一次还款日期'])
                creditTradeDetailDf_cycleLoanAccountInfo.loc[cycleLoanAccountInfoIndex, '当前一共逾期期数'] = np.sum(
                    normalDf['当前逾期期数'])
                creditTradeDetailDf_cycleLoanAccountInfo.loc[cycleLoanAccountInfoIndex, '当前一共逾期总额'] = np.sum(
                    normalDf['当前逾期总额'])
                creditTradeDetailDf_cycleLoanAccountInfo.loc[cycleLoanAccountInfoIndex, '逾期31-60天未还本金（合计）'] = np.sum(
                    normalDf['逾期31-60天未还本金'])
                creditTradeDetailDf_cycleLoanAccountInfo.loc[cycleLoanAccountInfoIndex, '逾期61-90天未还本金（合计）'] = np.sum(
                    normalDf['逾期61-90天未还本金'])
                creditTradeDetailDf_cycleLoanAccountInfo.loc[cycleLoanAccountInfoIndex, '逾期91-180天未还本金（合计）'] = np.sum(
                    normalDf['逾期91-180天未还本金'])
                creditTradeDetailDf_cycleLoanAccountInfo.loc[cycleLoanAccountInfoIndex, '逾期180天以上未还本金（合计）'] = np.sum(
                    normalDf['逾期180天以上未还本金'])

    # 解析贷款账户信息指标
    def parseLoanMergeDf(self,df,reportTime):
        if not df.empty:
            sortDf = df.sort_values(by=["账户关闭日期", "借款金额（本金)"], ascending=(False, False))
            sortDf = sortDf[sortDf['账户状态'] == '结清'];
            sortDf = sortDf.reset_index(drop=True)
            if not sortDf.empty:
                row0 = sortDf.loc[0, :]
                loanAccountInfoDf.loc[loanAccountInfoIndex, '最近一笔结清贷款的贷款金额'] = row0['借款金额（本金)']
                openDate = dfParser.formatDate(row0['开立日期'])
                loanAccountInfoDf.loc[loanAccountInfoIndex, '最近一笔结清贷款的发放距今月数'] = utils.difMonthReportTime(openDate,
                                                                                                          reportTime)
                settleDate = dfParser.formatDate(row0['账户关闭日期'])
                loanAccountInfoDf.loc[loanAccountInfoIndex, '最近一笔结清贷款的结清距今月数'] = utils.difMonthReportTime(settleDate,
                                                                                                          reportTime)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '历史贷款总法人机构数'] = df['管理机构'].unique().size
            #1112 汇算
            notSettleDf = df[df['账户状态'] != '结清'];
            otherDf.loc[otherIndex, '未结清贷款机构数'] = notSettleDf['管理机构'].unique().size
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前同时在用的贷款机构数'] = df[df['余额（本金)'] > 0]['管理机构'].unique().size
            statusDf = df[(df['账户状态'] != '结清') & (df['账户状态'] != '转出')]
            bankDf = statusDf[statusDf['管理机构'].str.contains('银行')]
            # 没有记录
            if statusDf.index.size == 0:
                isNotBankCust = -1
            else:
                if bankDf.index.size > 0:  # 有一条以上不为结清，请包含银行
                    isNotBankCust = 1;
                else:
                    isNotBankCust = 0;
            loanAccountInfoDf.loc[loanAccountInfoIndex, '是否有非银行贷款客户'] = isNotBankCust
            # 最严重的五级分类
            # fiveType = ""
            # for fiveTypeTmp in consts.fiveType:
            #     fiveTypeDf = statusDf[statusDf['五级分类']==fiveTypeTmp];
            #     if not fiveTypeDf.empty:
            #         fiveType = fiveTypeTmp;
            #         break;
            # loanAccountInfoDf.loc[loanAccountInfoIndex, '贷款五级分类'] = fiveType
            # 当前贷款LTV
            # 从“贷款信息”中提取，剔除“账户状态”为结清及转出，并剔除“账户状态”为呆账且本金余额 = 0
            # 的记录后，SUM(本金余额) / SUM(贷款本金)
            # 如本金余额为空和贷款本金为0或为空，则当条记录不计算
            loanLtvDf = df[(df['账户状态'] != '结清') & (df['账户状态'] != '转出') & (df['借款金额（本金)'] > 0) & (df['余额（本金)'] != '')]
            badSetDf = loanLtvDf[~((loanLtvDf['账户状态'] == '呆账') & (loanLtvDf['余额（本金)'] == 0))]
            balanceSum = np.sum(badSetDf['余额（本金)'].astype('int'))
            loanAmtSum = np.sum(badSetDf['借款金额（本金)'].astype('int'))
            if (loanAmtSum != 0):
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款LTV'] = round(np.divide(balanceSum, loanAmtSum), 2)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款最高LTV'] = round(
                np.max(np.divide(badSetDf['余额（本金)'].astype('int'), badSetDf['借款金额（本金)'].astype('int'))), 2)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款最低LTV'] = round(
                np.min(np.divide(badSetDf['余额（本金)'].astype('int'), badSetDf['借款金额（本金)'].astype('int'))), 2)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款平均LTV'] = round(np.mean(np.divide(badSetDf['余额（本金)'].astype('int'), badSetDf['借款金额（本金)'].astype('int'))), 2)

            # ['个人住房商业贷款','个人商用房（含商住两用）贷款','个人住房公积金贷款','房'],
            houseLtvList = consts.houseLtvList;# ['个人住房商业贷款','个人商用房（含商住两用）贷款','个人住房公积金贷款','房'],
            # houseLtvDf = badSetDf[badSetDf['业务种类'].isin(houseLtvList)]
            # if not houseLtvDf.empty:
            #     loanAccountInfoDf.loc[loanAccountInfoIndex, '当前房贷LTV'] = round(np.divide(np.sum(houseLtvDf['余额（本金)'].astype('int')),np.sum(houseLtvDf['借款金额（本金)'].astype('int'))), 2)
            # ['个人住房贷款','个人商用房（包括商住两用）贷款']
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前房贷LTV'] = lip.getCurLtv(badSetDf, houseLtvList)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款机构数量'] = loanLtvDf['管理机构'].unique().size
            cardLtvList = ['个人汽车消费贷款']#, '车'
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前车贷LTV'] = lip.getCurLtv(badSetDf, cardLtvList)
            operateLtvList = ['个人经营性贷款']
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前经营贷LTV'] = lip.getCurLtv(badSetDf, operateLtvList)
            consumeLtvList = ['其他个人消费贷款']
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前消费贷LTV'] = lip.getCurLtv(badSetDf, consumeLtvList)

            bankLtvList = ['商业银行', '外资银行', '村镇银行', '住房储蓄银行', '财务公司']
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前银行贷LTV'] = lip.getCurBankLtv(badSetDf, bankLtvList)

            bankLtvList = ['消费金融公司', '汽车金融公司', '信托公司']  # TODO
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前消金贷LTV'] = lip.getCurBankLtv(badSetDf, bankLtvList)
            smallLoanLtvList = ['小额信贷公司']
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前小贷LTV'] = lip.getCurBankLtv(badSetDf, smallLoanLtvList)
            # 当前贷款最大逾期期数
            # 从“贷款信息”中提取，剔除“账户状态”为结清、转出、呆账、呆帐后，MAX(每笔贷款的当前逾期期数)
            loanOverdueLtvDf = df[(df['账户状态'] != '结清') & (df['账户状态'] != '转出') & (df['账户状态'] != '呆账')]
            if not loanOverdueLtvDf.empty:
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款最大逾期期数'] = np.max(loanOverdueLtvDf['当前逾期期数'])
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款最大逾期金额'] = np.max(loanOverdueLtvDf['当前逾期总额'])
                loanOverdueLtvDf = loanOverdueLtvDf.reset_index(drop=True)
                maxOverdueIndex = np.argmax(loanOverdueLtvDf['当前逾期期数'])
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款最大逾期期数对应的最大逾期金额'] = \
                loanOverdueLtvDf.loc[maxOverdueIndex, :]['当前逾期总额']
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近3月开户最高贷款本金'] = lip.getLastLoanAmtMax(df,
                                                                                               reportTime,
                                                                                               3)  # 贷款指标加工单独放到一个文件里
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近3月开户最低贷款本金'] = lip.getLastLoanAmtMin(df,
                                                                                               reportTime,
                                                                                               3)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近3月开户平均贷款本金'] = lip.getLastLoanAmtAvg(df,
                                                                                               reportTime,
                                                                                               3)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近6月开户最高贷款本金'] = lip.getLastLoanAmtMax(df,
                                                                                               reportTime,
                                                                                               6)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近6月开户最低贷款本金'] = lip.getLastLoanAmtMin(df,
                                                                                               reportTime,
                                                                                               6)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近6月开户平均贷款本金'] = lip.getLastLoanAmtAvg(df,
                                                                                               reportTime,
                                                                                               6)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近12月开户最高贷款本金'] = lip.getLastLoanAmtMax(df,
                                                                                                reportTime,
                                                                                                12)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近12月开户最低贷款本金'] = lip.getLastLoanAmtMin(df,
                                                                                                reportTime,
                                                                                                12)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近12月开户平均贷款本金'] = lip.getLastLoanAmtAvg(df,
                                                                                                reportTime,
                                                                                                12)

            lastLoanDf = loanOverdueLtvDf;
            if not lastLoanDf.empty:
                loanAccountInfoDf.loc[loanAccountInfoIndex, '贷款最近一次还款日期距今时长'] = lip.getLastPayDateMinDays(lastLoanDf,reportTime)
            normalDf = df[(df['账户状态'] == '正常') & (df['当前逾期期数'] == 0)]
            # 未结清贷款总账户数：账户状态不等于结清和转出的记录数
            notSettleDf = df[(df['账户状态'] != '结清') & (df['账户状态'] != '转出')]

            #1113 汇算
            cardLoanCountDf = df[df['业务种类'].isin(cardLtvList)]
            otherDf.loc[otherIndex, '车贷发放笔数'] = cardLoanCountDf.index.size
            #count(贷款账户) where 五级分类 in ('关注','次级','可疑','损失')
            fiveDf = df[df['五级分类'].isin(['关注','次级','可疑','损失'])]
            juziDf.loc[juziIndex, "贷款五级分类命中('关注','次级','可疑','损失')的账户数"] = fiveDf.index.size
            loanStatusDf = df[df['账户状态'].isin(['逾期','呆账','转出','担保物不足','强制平仓','司法追偿'])]
            juziDf.loc[juziIndex, "贷款状态为('逾期','呆账','转出','担保物不足','强制平仓','司法追偿')的账户数"] = loanStatusDf.index.size
            #近12个月信贷最大授信额度
            #max(放款金额) where 担保方式 in ("信用/免担保") and 业务种类 not in ("个人住房商业贷款","个人商用房（含商住两用）贷款","个人住房公积金贷款","个人汽车消费贷款") and (报告日期-开立日期)<=360天
            loanCreditAmtDf = df[df['担保方式'].isin(['信用/免担保'])]
            loanCreditAmtDf = loanCreditAmtDf[(~loanCreditAmtDf['业务种类'].isin(consts.notMortgageList))]
            creditDate = utils.getLastMonthDate(reportTime,12)
            loanCreditAmtDf = loanCreditAmtDf[loanCreditAmtDf["开立日期"]>=creditDate]
            if not loanCreditAmtDf.empty:
                otherDf.loc[otherIndex, "近12个月信贷最大授信额度"] = np.max(loanCreditAmtDf['借款金额（本金)'])

            if not notSettleDf.empty:
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前正常贷款账户数'] = normalDf.index.size
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前正常贷款账户数占比'] = round(
                    normalDf.index.size / notSettleDf.index.size, 2)
                # 当前未结清贷款余额总和
                # ltvDf = tmpDf[tmpDf['业务种类'].isin(bizTypeList)]
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前未结清贷款余额总和'] = np.sum(notSettleDf['余额（本金)'])
                # loanAccountInfoDf.loc[loanAccountInfoIndex, '当前未结清贷款余额总和'] = np.sum(notSettleDf['余额（本金)'])
                # 当前未结清住房贷款余额总和
                houseDf = notSettleDf[notSettleDf['业务种类'].isin(houseLtvList)]
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前未结清住房贷款余额总和'] = np.sum(houseDf['余额（本金)'])
                # 当前未结清汽车贷款余额总和
                cardDf = notSettleDf[notSettleDf['业务种类'].isin(cardLtvList)]
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前未结清汽车贷款余额总和'] = np.sum(cardDf['余额（本金)'])
                # 当前未结清个人经营性贷款余额总和
                operateLtvDf = notSettleDf[notSettleDf['业务种类'].isin(operateLtvList)]
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前未结清个人经营性贷款余额总和'] = np.sum(operateLtvDf['余额（本金)'])
                # 当前平均每月贷款余额总和
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前平均每月贷款余额总和'] = round(np.sum(notSettleDf['余额（本金)']) / 12,
                                                                                    2)
                # 当前正常贷款账户余额
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前正常贷款账户余额'] = np.sum(normalDf['余额（本金)'])
                # "从“贷款信息”中提取，剔除结清、转出，当前正常贷款账户余额/未结清贷款总余额（本金余额加总）
                if np.sum(notSettleDf['余额（本金)']) > 0:
                    loanAccountInfoDf.loc[loanAccountInfoIndex, '当前正常贷款账户余额占总余额比'] = round(
                        np.sum(normalDf['余额（本金)']) / np.sum(notSettleDf['余额（本金)']), 2)
                #1113 汇算
                #sum(本月应还款) where 业务种类 in  ("个人住房商业贷款","个人商用房（含商住两用）贷款","个人住房公积金贷款")  and 账户状态 not in("结清","转出","呆账")
                housePayDf = notSettleDf[notSettleDf['业务种类'].isin(consts.houseLtvList)]
                otherDf.loc[otherIndex, '房贷月还总额']=np.sum(housePayDf['本月应还款'])

                cardPayMonthDf = notSettleDf[notSettleDf['业务种类'].isin(cardLtvList)]
                otherDf.loc[otherIndex, '车贷月还总额'] = np.sum(cardPayMonthDf['本月应还款'])
                #小额贷款笔数
                smallLoanDf =  notSettleDf[notSettleDf['借款金额（本金)']<=20000]
                otherDf.loc[otherIndex, '小额贷款笔数'] = smallLoanDf.index.size
            settleDf = df[(df['账户状态'] == '结清')]
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前正常结清贷款账户数'] = settleDf.index.size
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前正常结清贷款账户数占比'] = round(settleDf.index.size / df.index.size, 2)
            # 贷款24期还款记录次数 TODO
            # 最近3个月个人消费贷款发放额度
            loanAccountInfoDf.loc[loanAccountInfoIndex, '贷款本月实还款金额'] = np.sum(loanOverdueLtvDf['本月应还款'])
            loanAccountInfoDf.loc[loanAccountInfoIndex, '最近3个月个人消费贷款发放额度'] = lip.getLastPerConsumeAmt(df, 3, reportTime)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '最近6个月个人消费贷款发放额度'] = lip.getLastPerConsumeAmt(df, 6, reportTime)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '最近12个月个人消费贷款发放额度'] = lip.getLastPerConsumeAmt(df, 12, reportTime)
            # 未结清贷款平均剩余还款期数
            payPieDf = notSettleDf[notSettleDf['还款期数'] != '']
            if payPieDf.index.size != 0:
                loanAccountInfoDf.loc[loanAccountInfoIndex, '未结清贷款平均剩余还款期数'] = round(
                    np.sum(payPieDf['剩余还款期数']) / payPieDf.index.size, 2)
            # 当前贷款本月应还金额总和
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款本月应还金额总和'] = np.sum(notSettleDf['本月应还款'])
            # 当前贷款本月实还金额总额
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款本月实还金额总额'] = np.sum(notSettleDf['本月实还款'])
            #1112汇算 非抵押类贷款授信总额
            #sum(放款金额) where 业务种类 not in ("个人住房商业贷款","个人商用房（含商住两用）贷款","个人住房公积金贷款","个人汽车消费贷款")
            # and 担保方式 not in ("抵押") and 账户状态不等于"结清" TODO
            notMortgageList = consts.notMortgageList
            notMortgageSettleDf = df[(df['担保方式'] != '抵押') & (df['账户状态'] != '结清')]
            notMortgageSettleDf = notMortgageSettleDf[(~notMortgageSettleDf['业务种类'].isin(notMortgageList))]
            otherDf.loc[otherIndex,"非抵押类贷款授信总额"]=np.sum(notMortgageSettleDf['借款金额（本金)'])
            otherDf.loc[otherIndex, "未结清非抵押贷款笔数"] = notMortgageSettleDf.index.size
            otherDf.loc[otherIndex, "未结清非抵押类贷款本金余额"] = np.sum(notMortgageSettleDf['余额（本金)'])

            notMortgageDf = df[(df['担保方式'] != '抵押') & (df['账户状态'] != '结清') & (df['账户状态'] != '呆账') & (df['账户状态'] != '转出')]
            notMortgageDf = notMortgageDf[(~notMortgageDf['业务种类'].isin(notMortgageList))]
            otherDf.loc[otherIndex, "未结清非抵押类贷款月还总额"] = np.sum(notMortgageDf['本月应还款'])
            sanshouDf.loc[sanshouIndex,"最大贷款余额"]= np.max(notSettleDf["余额（本金)"])
            sanshouDf.loc[sanshouIndex, "未结清所有贷款合同总额"] = np.sum(notSettleDf["借款金额（本金)"])

    # 解析贷记卡账户信息指标
    def parseCreditCardMergeDf(self,df,reportTime):
        if not df.empty:
            # 历史信用卡总法人机构数
            # creditCardAccountInfoDf.loc[creditCardAccountInfoIndex,'历史信用卡总法人机构数'] = df['发卡机构'].unique().size
            # creditCardUseDf = df[df['已用额度']>0];
            # creditCardAccountInfoDf.loc[creditCardAccountInfoIndex,'当前同时在用的信用卡机构数'] = creditCardUseDf['发卡机构'].unique().size
            fstDate = np.min(df['开立日期']);
            sanshouDf.loc[sanshouIndex, '最早信用卡发卡天数'] = utils.difDateReportTime(reportTime,fstDate)
            # 统一排除
            creditDf = df[(df['币种'] == '人民币元') & (df['账户状态'] != '未激活') & (df['账户状态'] != '销户') & (df['账户状态'] != '呆账')]
            totalAmtDf = df[(df['币种'] == '人民币元') & (df['账户状态'] != '未激活') & (df['账户状态'] != '销户') & (df['账户状态'] != '呆账')]
            # 大额专项分期额度（合计）
            # 已用分期金额(合计）
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '大额专项分期额度（合计）'] = np.sum(creditDf['大额专项分期额度'])
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '已用分期金额(合计）'] = np.sum(creditDf['已用分期金额'])
            # creditCardAccountInfoDf.loc[creditCardAccountInfoIndex,'贷记卡账户当前总额度'] = cip.getMaxCreditAmt(creditDf)
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '最近新发放的3张贷记卡平均额度'] = cip.getAvgCreditAmt(creditDf)
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '贷记卡额度使用率超过90%的机构数占比'] = cip.getUseRate(creditDf,
                                                                                                            df, 0.9)
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '贷记卡额度使用率超过100%的机构数占比'] = cip.getUseRate(creditDf,
                                                                                                             totalAmtDf,
                                                                                                             1)
            # 从“贷记卡信息”中提取，计算授信额度时剔除销户，计算已用额度时剔除呆账、呆帐、销户后，SUM(各账户已用额度) / SUM(各账户授信额度)
            useCreditDf = df[(df['币种'] == '人民币元') & (df['账户状态'] != '销户') & (df['账户状态'] != '呆账')]
            totalCreditDf = df[(df['币种'] == '人民币元') & (df['账户状态'] != '销户')]
            totalCreditAmt = np.sum(totalCreditDf['账户授信额度'])
            if totalCreditAmt != 0:  # 授信额度不能为0
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '贷记卡账户当前总额度使用率'] = round(
                    np.sum(useCreditDf['已用额度']) / np.sum(totalCreditDf['账户授信额度']), 2)
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '贷记卡账户最高使用额度总的使用率'] = round(
                    np.sum(useCreditDf['最大使用额']) / np.sum(totalCreditDf['账户授信额度']), 2)
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '贷记卡账户近6月平均额度总的使用率'] = round(
                    np.sum(useCreditDf['最近6个月平均使用额度']) / np.sum(totalCreditDf['账户授信额度']), 2)
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前贷记卡最大逾期期数'] = np.max(creditDf['当前逾期期数'])  # 用于计算
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前贷记卡最大逾期金额'] = np.max(creditDf['当前逾期总额'])
            #1112 修改
            otherDf.loc[otherIndex, '贷记卡当前逾期金额'] = np.sum(creditDf['当前逾期总额'])


            if not creditDf.empty:
                creditDf = creditDf.reset_index(drop=True)
                maxOverdueIndex = np.argmax(creditDf['当前逾期期数'])
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前贷记卡最大逾期期数对应的最大逾期金额'] = \
                creditDf.loc[maxOverdueIndex, :]['当前逾期总额']
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近3月开卡最高额度'] = cip.getLastMonthMaxCreditAmt(df,
                                                                                                                reportTime,
                                                                                                                3)
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近3月开卡最低额度'] = cip.getLastMonthMinCreditAmt(df,
                                                                                                                reportTime,
                                                                                                                3)
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近3月开卡平均额度'] = cip.getLastMonthAvgCreditAmt(df,
                                                                                                                reportTime,
                                                                                                                3)
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近6月开卡最高额度'] = cip.getLastMonthMaxCreditAmt(df,
                                                                                                                reportTime,
                                                                                                                6)
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近6月开卡最低额度'] = cip.getLastMonthMinCreditAmt(df,
                                                                                                                reportTime,
                                                                                                                6)
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近6月开卡平均额度'] = cip.getLastMonthAvgCreditAmt(df,
                                                                                                                reportTime,
                                                                                                                6)

            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近12月开卡最高额度'] = cip.getLastMonthMaxCreditAmt(df,
                                                                                                                 reportTime,
                                                                                                                 12)
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近12月开卡最低额度'] = cip.getLastMonthMinCreditAmt(df,
                                                                                                                 reportTime,
                                                                                                                 12)
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近12月开卡平均额度'] = cip.getLastMonthAvgCreditAmt(df,
                                                                                                                 reportTime,
                                                                                                                 12)

            if not creditDf.empty:
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '贷记卡最近一次还款日期距今时长'] = cip.getLastPayDateMinDays(
                    creditDf, reportTime)
                paySo = np.sum(creditDf['本月应还款'])
                if (paySo) != 0:
                    creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '贷记卡还款比例'] = round(
                        np.sum(creditDf['本月实还款']) / np.sum(creditDf['本月应还款']), 2)
                    creditDfTmp = creditDf[creditDf['本月应还款'] > 0]
                    creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '贷记卡最高还款比例'] = round(
                        np.max(np.divide(creditDfTmp['本月实还款'], creditDfTmp['本月应还款'])), 2)
                    creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '贷记卡最低还款比例'] = round(
                        np.min(np.divide(creditDfTmp['本月实还款'], creditDfTmp['本月应还款'])), 2)
                #贷记卡疑似分期笔数 汇算
                installmentDf = creditDf[(creditDf['本月实还款']>0) & (creditDf['本月应还款']>200) &(creditDf['本月实还款']< creditDf['本月应还款']/9)]
                otherDf.loc[otherIndex, '贷记卡疑似分期笔数'] = installmentDf.index.size
            normalDf = df[(df['币种'] == '人民币元') & (df['账户状态'] == '正常') & (df['当前逾期期数'] == 0)];
            activeDf = df[(df['币种'] == '人民币元') & (df['账户状态'] == '正常')];
            notCloseDf = df[(df['账户状态'] != '销户')]

            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前正常贷记卡账户数'] = normalDf.index.size
            if not notCloseDf.empty and not normalDf.empty:
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前正常贷记卡账户数占比'] = round(
                    normalDf.index.size / notCloseDf.index.size, 2)
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前正常贷记卡已用额度'] = np.sum(normalDf['已用额度'])
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前正常且有余额的贷记卡账户数'] = normalDf[
                    normalDf['已用额度'] > 0].index.size
            if not creditDf.empty:
                creditUseAmt = np.sum(creditDf['已用额度'])
                if creditUseAmt != 0:
                    creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前正常贷记卡账户余额占总余额比'] = round(
                        np.sum(normalDf['已用额度']) / np.sum(creditDf['已用额度']), 2)

            if notCloseDf.empty:
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前正常且有余额的贷记卡账户数占比'] = -99
            else:
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前正常且有余额的贷记卡账户数占比'] = \
                    round(creditCardAccountInfoDf.loc[
                              creditCardAccountInfoIndex, '当前正常且有余额的贷记卡账户数'] / notCloseDf.index.size, 3)
            # 当前正常贷记卡账户余额占总余额比

            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前贷记卡本月实还金额总和'] = np.sum(creditDf['本月实还款'])
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前贷记卡本月应还金额总和'] = np.sum(creditDf['本月应还款'])
            maxAmtDf = df[(df['币种'] == '人民币元')]
            if not maxAmtDf.empty:
                maxAmtDf = maxAmtDf.reset_index(drop=True)
                maxAmtIndex = np.argmax(maxAmtDf['账户授信额度'])
                maxOpenDate = maxAmtDf.loc[maxAmtIndex, :]['开立日期'];
                creditCardAccountInfoDf.loc[
                    creditCardAccountInfoIndex, '额度最高的人民币贷记卡开卡距今月份数'] = utils.difMonthReportTime(maxOpenDate,reportTime);
            # 名下贷记卡数量-状态正常
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '名下贷记卡数量-状态正常'] = df[
                (df['账户状态'] != '销户')].index.size
            # 名下贷记卡数量-状态未激活
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '名下贷记卡数量-状态未激活'] = df[
                (df['账户状态'] == '未激活')].index.size
            # 名下贷记卡数量-状态异常异常包含（2－冻结,3－止付,5－呆帐，10-其他）
            abnormalList = ['冻结', '止付', '呆账', '司法追偿','银行止付']#原来为其他
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '名下贷记卡数量-状态异常'] = df[(df['账户状态'].isin(abnormalList))].index.size
            # 名下贷记卡比例-状态正常
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '名下贷记卡比例-状态正常'] = round(
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '名下贷记卡数量-状态正常'] / df.index.size, 2)
            # 名下贷记卡比例-状态未激活
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '名下贷记卡比例-状态未激活'] = round(
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '名下贷记卡数量-状态未激活'] / df.index.size, 2)
            # 名下贷记卡比例-状态异常
            creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '名下贷记卡比例-状态异常'] = round(
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '名下贷记卡数量-状态异常'] / df.index.size, 2)
            #1113汇算
            abnormalList = ['冻结', '止付', '呆账', '司法追偿','银行止付']
            otherDf.loc[otherIndex, "贷记卡状态为('呆账','冻结','止付','司法追偿')的账户数"] = df[(df['账户状态'].isin(abnormalList))].index.size
            availableCreditDf = activeDf[activeDf['最近6个月平均使用额度'] > 500] #正常的信用卡
            otherDf.loc[otherIndex, '有效信用卡张数'] = availableCreditDf.index.size
            #使用中的所有贷记卡中发卡时间距征信查询时间的最大天数 0111三寿
            if not activeDf.empty:
                fstDate = np.min(activeDf['开立日期']);
                sanshouDf.loc[sanshouIndex,"使用中贷记卡最早发卡天数"]=utils.difDateReportTime(reportTime, fstDate)

    # 解析准贷记卡账户信息指标
    def parseCreditCardMergeDfZ(self,df, payRcd):
        if not df.empty:
            overdueCreditCardRcdDf = payRcd[payRcd['账户编号'].isin(df['账户编号'].values)];
            overdueCreditCardRcdDf = utils.replacePayRcdStatusOverdue(overdueCreditCardRcdDf)
            creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '本月应还款（合计）'] = np.nansum(df['透支余额'])
            creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '本月实还款（合计）'] = np.nansum(df['本月实还款'])
            creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '最近一次还款日期'] = np.nanmax(df['最近一次还款日期'])
            creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '当前一共透支期数'] = cip.getCurOverdueNum(
                overdueCreditCardRcdDf);
            creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '当前一共透支总额'] = np.nansum(df['透支余额'])
            creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '透支180天以上未支付余额（合计）'] = np.nansum(
                df['透支180天以上未付余额'])

            # 名下贷记卡数量-状态异常异常包含（2－冻结,3－止付,5－呆帐，10-其他）
            abnormalList = ['冻结', '止付', '呆账', '其他','银行止付']
            creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '名下准贷记卡数量-状态异常'] = df[(df['账户状态'].isin(abnormalList))].index.size

            creditDf = df[(df['账户状态'] != '未激活') & (df['账户状态'] != '销户')]
            if not creditDf.empty:
                totalAmt = np.nansum(creditDf['账户授信额度'])
                creditAmt = np.nansum(creditDf['透支余额'])
                if totalAmt != 0:
                    # 从“贷记卡信息”中提取，剔除未激活、销户后，所有账户透支金额/所有账户账户授信额度。
                    creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '全部准贷记卡账户当前总额度使用率'] = round(
                        creditAmt / totalAmt, 2)
                # 从“贷记卡信息”中提取，剔除未激活、销户后，MAX（单账户最高透支金额/单账户授信额度）
                creditMaxDf = creditDf[creditDf['账户授信额度'] > 0]
                if not creditMaxDf.empty:
                    creditMaxDf = creditMaxDf.fillna(0.0)
                    creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '准贷记卡账户最高使用额度总的使用率'] = round(
                        np.max(np.divide(creditMaxDf['最大透支余额'], creditMaxDf['账户授信额度'])), 2)
                creditMaxDf = creditDf[creditDf['最大透支余额'] > 0]
                if not creditMaxDf.empty:
                    creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '当前准贷记卡最大透支金额'] = np.max(
                        creditMaxDf['最大透支余额'])
                # 从“贷记卡信息”中提取，剔除未激活、销户后，当前透支准贷记卡账户数/总准贷记卡账户数，透支账户判断：透支余额不为0的账户
                creditDfTmp = creditDf[creditDf['透支余额'] > 0]
                creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '当前准贷记卡透支账户数占比'] = round(
                    creditDfTmp.index.size / creditDf.index.size, 2)
            creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '当前准贷记卡本月应还金额总和'] = np.nansum(df['透支余额'])
            creditCardAccountInfoDfZ.loc[creditCardAccountInfoIndexZ, '当前准贷记卡本月实还金额总和'] = np.nansum(df['本月实还款'])

    # 解析使用率 TODO 使用汇总计算还是使用明细计算
    def parseUseRate(self):
        # useRateDf.loc[useRateIndex, '贷记卡账户使用率（已用额度/授信总额）']
        # 从“信贷交易授信及负债信息概要”中“非循环贷账户信息汇总”、“循环额度下分账户信息汇总”、“循环贷账户信息汇总”、“贷记卡账户信息汇总”和“准贷记卡账户信息汇总”里提取，SUM(
        #     所有“余额”、“已用额度”和“透支余额”） / SUM(所有“授信总额”和“授信额度”)

        loanUseAmt = briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '非循环贷账户余额']
        cycleCreditUseAmt = briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环额度下分账户余额']
        cycleUseAmt = briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环贷账户余额']
        creditUseAmt = briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '贷记卡已用额度']
        creditAmtUseZ = briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '准贷记卡已用额度']

        loanTotalAmt = briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '非循环贷账户授信总额']
        cycleCreditTotalAmt = briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环额度下分账户授信总额']
        cycleTotalAmt = briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '循环贷账户授信总额']
        creditTotalAmt = briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '贷记卡授信总额']
        creditAmtTotalZ = briefInfoDf_loanTradeCreditInfo.loc[loanTradeCreditInfoIndex, '准贷记卡授信总额']
        # if str(loanUseAmt)=="nan":
        #     loanUseAmt = 0;
        # if str(cycleCreditUseAmt) == "nan":
        #     loanUseAmt = 0;
        # if str(cycleCreditUseAmt) == "nan":
        #     loanUseAmt = 0;

        useAmt = loanUseAmt + cycleCreditUseAmt + cycleUseAmt + creditUseAmt + creditAmtUseZ
        totalAmt = loanTotalAmt + cycleCreditTotalAmt + cycleTotalAmt + creditTotalAmt + creditAmtTotalZ
        if totalAmt != 0:
            useRateDf.loc[useRateIndex, '全账户使用率（已用额度/授信总额）'] = round(useAmt / totalAmt, 2)
        if loanTotalAmt != 0:
            useRateDf.loc[useRateIndex, '非循环贷账户使用率（已用额度/授信总额）'] = round(loanUseAmt / loanTotalAmt, 2)
        if cycleCreditTotalAmt != 0:
            useRateDf.loc[useRateIndex, '循环额度下分账户使用率（已用额度/授信总额）'] = round(cycleCreditTotalAmt / cycleCreditTotalAmt, 2)
        if cycleTotalAmt != 0:
            useRateDf.loc[useRateIndex, '循环贷账户使用率（已用额度/授信总额）'] = round(cycleUseAmt / cycleTotalAmt, 2)
        if creditTotalAmt != 0:
            useRateDf.loc[useRateIndex, '贷记卡账户使用率（已用额度/授信总额）'] = round(creditUseAmt / creditTotalAmt, 2)
        if creditAmtTotalZ != 0:
            useRateDf.loc[useRateIndex, '准贷记卡账户使用率（已用额度/授信总额）'] = round(creditAmtUseZ / creditAmtTotalZ, 2)

    # 解析开户数
    def parseOpenAccount(self,loanDf, creditCardDf, creditCardDfZ, recoveryInfoMergeDf, loanPayRecordMergeDf,
                         creditCardPayRecordMergeDf, creditCardPayRecordMergeDfZ,reportTime):

        openAccountDf.loc[openAccountIndex, '近3个月全账户开户数'] = cip.getOpenAccount(loanDf, reportTime,
                                                                               3) + cip.getOpenAccount(creditCardDf,
                                                                                                       reportTime,
                                                                                                       3) + cip.getOpenAccount(
            creditCardDfZ, reportTime, 3)
        openAccountDf.loc[openAccountIndex, '近6个月全账户开户数'] = cip.getOpenAccount(loanDf, reportTime,
                                                                               6) + cip.getOpenAccount(creditCardDf,
                                                                                                       reportTime,
                                                                                                       6) + cip.getOpenAccount(
            creditCardDfZ, reportTime, 6)
        openAccountDf.loc[openAccountIndex, '近9个月全账户开户数'] = cip.getOpenAccount(loanDf, reportTime,
                                                                               9) + cip.getOpenAccount(creditCardDf,
                                                                                                       reportTime,
                                                                                                       9) + cip.getOpenAccount(
            creditCardDfZ, reportTime, 9)
        openAccountDf.loc[openAccountIndex, '近12个月全账户开户数'] = cip.getOpenAccount(loanDf, reportTime,
                                                                                12) + cip.getOpenAccount(creditCardDf,
                                                                                                         reportTime,
                                                                                                         12) + cip.getOpenAccount(
            creditCardDfZ, reportTime, 12)
        openAccountDf.loc[openAccountIndex, '近24个月全账户开户数'] = cip.getOpenAccount(loanDf, reportTime,
                                                                                24) + cip.getOpenAccount(creditCardDf,
                                                                                                         reportTime,
                                                                                                         24) + cip.getOpenAccount(
            creditCardDfZ, reportTime, 24)

        openAccountDf.loc[openAccountIndex, '近3个月消费金融类账户开户数'] = lip.getOpenAccount(loanDf, reportTime, 3,
                                                                                   consts.bankList)
        openAccountDf.loc[openAccountIndex, '近6个月消费金融类账户开户数'] = lip.getOpenAccount(loanDf, reportTime, 6,
                                                                                   consts.bankList)
        openAccountDf.loc[openAccountIndex, '近9个月消费金融类账户开户数'] = lip.getOpenAccount(loanDf, reportTime, 9,
                                                                                   consts.bankList)
        openAccountDf.loc[openAccountIndex, '近12个月消费金融类账户开户数'] = lip.getOpenAccount(loanDf, reportTime, 12,
                                                                                    consts.bankList)
        openAccountDf.loc[openAccountIndex, '近24个月消费金融类账户开户数'] = lip.getOpenAccount(loanDf, reportTime, 24,
                                                                                    consts.bankList)

        openAccountDf.loc[openAccountIndex, '近3个月贷款账户开户数'] = lip.getOpenAccount(loanDf, reportTime, 3, "")
        openAccountDf.loc[openAccountIndex, '近6个月贷款账户开户数'] = lip.getOpenAccount(loanDf, reportTime, 6, "")
        openAccountDf.loc[openAccountIndex, '近9个月贷款账户开户数'] = lip.getOpenAccount(loanDf, reportTime, 9, "")
        openAccountDf.loc[openAccountIndex, '近12个月贷款账户开户数'] = lip.getOpenAccount(loanDf, reportTime, 12, "")
        openAccountDf.loc[openAccountIndex, '近24个月贷款账户开户数'] = lip.getOpenAccount(loanDf, reportTime, 24, "")

        openAccountDf.loc[openAccountIndex, '近3个月贷记卡账户开户数'] = cip.getOpenAccount(creditCardDf, reportTime, 3)
        openAccountDf.loc[openAccountIndex, '近6个月贷记卡账户开户数'] = cip.getOpenAccount(creditCardDf, reportTime, 6)
        openAccountDf.loc[openAccountIndex, '近9个月贷记卡账户开户数'] = cip.getOpenAccount(creditCardDf, reportTime, 9)
        openAccountDf.loc[openAccountIndex, '近12个月贷记卡账户开户数'] = cip.getOpenAccount(creditCardDf, reportTime, 12)
        openAccountDf.loc[openAccountIndex, '近24个月贷记卡账户开户数'] = cip.getOpenAccount(creditCardDf, reportTime, 24)

        openAccountDf.loc[openAccountIndex, '近3个月准贷记卡账户开户数'] = cip.getOpenAccount(creditCardDfZ, reportTime, 3)
        openAccountDf.loc[openAccountIndex, '近6个月准贷记卡账户开户数'] = cip.getOpenAccount(creditCardDfZ, reportTime, 6)
        openAccountDf.loc[openAccountIndex, '近9个月准贷记卡账户开户数'] = cip.getOpenAccount(creditCardDfZ, reportTime, 9)
        openAccountDf.loc[openAccountIndex, '近12个月准贷记卡账户开户数'] = cip.getOpenAccount(creditCardDfZ, reportTime, 12)
        openAccountDf.loc[openAccountIndex, '近24个月准贷记卡账户开户数'] = cip.getOpenAccount(creditCardDfZ, reportTime, 24)

        #三寿
        sanshouDf.loc[sanshouIndex, '最近24M发卡数量'] = cip.getOpenAccount(creditCardDf, reportTime, 24)+cip.getOpenAccount(creditCardDfZ, reportTime, 24)
        # 从“信贷交易信息明细”中“非循环贷账户”、“循环额度下分账户”、“循环贷账户”、“贷记卡账户”和“准贷记卡账户”里提取，5年里账户还款状态出现“1、2、3、4、5、6、7、D、Z、G、B”的账户数/所有账户数
        overdueLoanPayRcdDf = loanPayRecordMergeDf[loanPayRecordMergeDf['账户编号'].isin(loanDf['账户编号'].values)]
        overdueLoanPayRcdDf = utils.replacePayRcdStatusOverdue(overdueLoanPayRcdDf)
        overdueLoanPayRcdDf = overdueLoanPayRcdDf[overdueLoanPayRcdDf['还款状态'] > 0]
        overdueCreditPayRcdDf = creditCardPayRecordMergeDf[
            creditCardPayRecordMergeDf['账户编号'].isin(creditCardDf['账户编号'].values)]
        overdueCreditPayRcdDf = utils.replacePayRcdStatusOverdue(overdueCreditPayRcdDf)
        overdueCreditPayRcdDf = overdueCreditPayRcdDf[overdueCreditPayRcdDf['还款状态'] > 0]
        overdueCreditPayRcdDfZ = creditCardPayRecordMergeDfZ[
            creditCardPayRecordMergeDfZ['账户编号'].isin(creditCardDfZ['账户编号'].values)]
        overdueCreditPayRcdDfZ = utils.replacePayRcdStatusOverdue(overdueCreditPayRcdDfZ)
        overdueCreditPayRcdDfZ = overdueCreditPayRcdDfZ[overdueCreditPayRcdDfZ['还款状态'] > 0]

        loanAccountNum = loanPayRecordMergeDf['账户编号'].unique().size
        creditAccountNum = creditCardPayRecordMergeDf['账户编号'].unique().size
        creditAccountNumZ = creditCardPayRecordMergeDfZ['账户编号'].unique().size
        overdueLoanNum = overdueLoanPayRcdDf['账户编号'].unique().size
        overdueCreditNum = overdueCreditPayRcdDf['账户编号'].unique().size
        overdueCreditNumZ = overdueCreditPayRcdDfZ['账户编号'].unique().size
        if (loanAccountNum + creditAccountNum + creditAccountNumZ) > 0:
            openAccountDf.loc[openAccountIndex, '有过逾期记录的账户/全账户数'] = round(
                (overdueLoanNum + overdueCreditNum + overdueCreditNumZ) / (
                            loanAccountNum + creditAccountNum + creditAccountNumZ), 2)

        otherPerLoanDf = loanDf[loanDf['业务种类'].isin(consts.bankList)]
        otherPerLoanNum = otherPerLoanDf.index.size;
        overdueOtherPerLoanNum = otherPerLoanDf[
            otherPerLoanDf['账户编号'].isin(overdueLoanPayRcdDf['账户编号'].values)].index.size;
        if otherPerLoanNum != 0:
            openAccountDf.loc[openAccountIndex, '有过逾期记录的消费金融类账户/全消费金融类账户数'] = round(
                overdueOtherPerLoanNum / otherPerLoanNum, 2)
        if loanAccountNum != 0:
            openAccountDf.loc[openAccountIndex, '有过逾期记录的贷款账户/全贷款账户数'] = round(overdueLoanNum / loanAccountNum, 2)
        if creditAccountNum != 0:
            openAccountDf.loc[openAccountIndex, '有过逾期记录的贷记卡账户/全贷记卡账户数'] = round(overdueCreditNum / creditAccountNum, 2)
        if creditAccountNumZ != 0:
            openAccountDf.loc[openAccountIndex, '有过透支记录的准贷记卡账户/全准贷记卡账户数'] = round(overdueCreditNumZ / creditAccountNumZ,
                                                                                  2)

        # 0525新增
        pledgeLoanDf = loanDf[loanDf['担保方式'] == '抵押']
        pledgeCreditCardDf = creditCardDf[creditCardDf['担保方式'] == '抵押']
        pledgeCreditCardDfZ = creditCardDfZ[creditCardDfZ['担保方式'] == '抵押']
        isPledge = "否"
        if pledgeLoanDf.index.size + pledgeCreditCardDf.index.size + pledgeCreditCardDfZ.index.size > 0:
            isPledge = "是"
        creditLoanDf = loanDf[loanDf['担保方式'] == '信用/免担保']
        creditCreditCardDf = creditCardDf[creditCardDf['担保方式'] == '信用/免担保']
        creditCreditCardDfZ = creditCardDfZ[creditCardDfZ['担保方式'] == '信用/免担保']
        isCredit = 0
        if creditLoanDf.index.size + creditCreditCardDf.index.size + creditCreditCardDfZ.index.size > 0:
            isCredit = creditLoanDf.index.size + creditCreditCardDf.index.size + creditCreditCardDfZ.index.size
        briefInfoDf_loanTradeInfo.loc[loanTradeInfoIndex, '是否存在担保方式为抵押的贷款'] = isPledge
        briefInfoDf_loanTradeInfo.loc[loanTradeInfoIndex, '担保方式为信用的贷款数量'] = isCredit

        #使用中贷记卡平均发卡天数
        try:
            sanshouDf.loc[sanshouIndex, '使用中贷记卡平均发卡天数'] = cip.getAvgUseCreditCardDay(creditCardDf,creditCardDfZ,reportTime)
            sanshouDf.loc[sanshouIndex, '最大信用账户天数（贷款&贷记卡人民账户）'] = cip.getMaxAmtAccountDay(loanDf,creditCardDf,creditCardDfZ ,reportTime)
            sanshouDf.loc[sanshouIndex, '额度使用>=50%的贷记卡数'] = cip.getUseRateCount(creditCardDf,creditCardDfZ,0.5)
            sanshouDf.loc[sanshouIndex, '额度使用>=75%的贷记卡数'] = cip.getUseRateCount(creditCardDf, creditCardDfZ, 0.75)
            sanshouDf.loc[sanshouIndex, '额度使用>=100%的贷记卡数'] = cip.getUseRateCount(creditCardDf, creditCardDfZ, 1)
            minMonthStr = ""
            fstLoanMon = str(otherDf.loc[otherIndex, '首笔贷款发放月份'])
            fstCreditCardMonth = str(otherDf.loc[otherIndex, '首张贷记卡发卡月份'])
            if fstLoanMon=="nan":
                fstLoanMon = ""
            if fstCreditCardMonth=="nan":
                fstCreditCardMonth = ""
            if fstLoanMon=='' and fstCreditCardMonth!='':
                minMonthStr = otherDf.loc[otherIndex, '首张贷记卡发卡月份']
            elif fstLoanMon!='' and fstCreditCardMonth!='':
                minMonth = [otherDf.loc[otherIndex, '首笔贷款发放月份'], otherDf.loc[otherIndex, '首张贷记卡发卡月份']]
                minMonthStr = min(minMonth)
            elif fstLoanMon!='' and fstCreditCardMonth=='':
                minMonthStr = otherDf.loc[otherIndex, '首笔贷款发放月份']
            if minMonthStr != '':
                sanshouDf.loc[sanshouIndex, '贷款、贷记卡最大天数'] = utils.difDateReportTime(reportTime, minMonthStr)
            if not loanDf.empty:
                openAccount3MDf = loanDf[loanDf['开立日期'] < utils.getLastMonthDate(reportTime, 6)]
                sanshouDf.loc[sanshouIndex, '开户180天以上的贷款笔数'] = openAccount3MDf.index.size
            if not creditCardDf.empty:
                sanshouDf.loc[sanshouIndex, '开户1年以内历史逾期90+贷记卡和准贷记卡数'] =cip.getOverDueCount(creditCardDf,creditCardPayRecordMergeDf,reportTime,3,12)\
                    +cip.getOverDueCount(creditCardDfZ,creditCardPayRecordMergeDfZ,reportTime,3,12)
                sanshouDf.loc[sanshouIndex, '开户1年以内历史逾期贷记卡和准贷记卡数'] = cip.getOverDueCount(creditCardDf,creditCardPayRecordMergeDf,reportTime, 1, 12)\
                    +cip.getOverDueCount(creditCardDfZ, creditCardPayRecordMergeDfZ, reportTime, 1, 12)
                sanshouDf.loc[sanshouIndex, '开户2年以内历史逾期贷记卡数'] = cip.getOverDueCount(creditCardDf,creditCardPayRecordMergeDf,reportTime, 1, 24)
        except:
            logger.error(traceback.print_exc())
            logger.error(traceback.format_exc())
    # 解析24期还款状态指标
    def parsePayRcdStatus(self,loanMergeDf, creditCardMergeDf, creditCardMergeDfZ, loanPayRecordMergeDf,
                          creditCardPayRecordMergeDf, creditCardPayRecordMergeDfZ,reportTime):
        # creditCardPayRecordMergeDf
        # 去掉外币
        creditCardMergeDf = creditCardMergeDf[creditCardMergeDf['币种'] == '人民币元']
        creditCardPayRecordMergeDf = creditCardPayRecordMergeDf[
            creditCardPayRecordMergeDf['账户编号'].isin(creditCardMergeDf['账户编号'].values)]
        reportTime = str(np.datetime64(reportTime, "M")) + "-02"  # 06-02，统计24期还款状态报告期，按每月的2号，避免chu'xian
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近3月逾期期数大于或等于“1”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                  reportTime, 1, 3)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近6月逾期期数大于或等于“1”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                  reportTime, 1, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近12月逾期期数大于或等于“1”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                   reportTime, 1, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近24月逾期期数大于或等于“1”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                   reportTime, 1, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近6月逾期期数大于或等于“2”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                  reportTime, 2, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近12月逾期期数大于或等于“2”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                   reportTime, 2, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近24月逾期期数大于或等于“2”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                   reportTime, 2, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近6月逾期期数大于或等于“3”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                  reportTime, 3, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近12月逾期期数大于或等于“3”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                   reportTime, 3, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近24月逾期期数大于或等于“3”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                   reportTime, 3, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近12月逾期期数大于或大等于“4”的次数'] = prp.getLoanOverdueTimes(
            loanPayRecordMergeDf, reportTime, 4, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近24月逾期期数大于或等于“4”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                   reportTime, 4, 24)

        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近3月逾期期数大于或等于“1”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 1, 3)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近6月逾期期数大于或等于“1”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 1, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近12月逾期期数大于或等于“1”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 1, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近24月逾期期数大于或等于“1”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 1, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近6月逾期期数大于或等于“2”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 2, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近12月逾期期数大于或等于“2”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 2, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近24月逾期期数大于或等于“2”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 2, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近6月逾期期数大于或等于“3”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 3, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近12月逾期期数大于或等于“3”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 3, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近24月逾期期数大于或等于“3”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 3, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近12月逾期期数大于或等于“4”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 4, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近24月逾期期数大于或等于“4”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 4, 24)

        payRcdStatusDf.loc[payRcdStatusIndex, '准贷记卡账户近6月逾期期数大于或等于“3”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDfZ, reportTime, 3, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '准贷记卡账户近12月逾期期数大于或等于“3”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDfZ, reportTime, 3, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '准贷记卡账户近24月逾期期数大于或等于“3”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDfZ, reportTime, 3, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '准贷记卡账户近6月逾期期数大于或等于“4”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDfZ, reportTime, 4, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '准贷记卡账户近12月逾期期数大于或等于“4”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDfZ, reportTime, 4, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '准贷记卡账户近24月逾期期数大于或等于“4”的次数'] = cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDfZ, reportTime, 4, 24)

        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近3月逾期期数大于或等于“1”的次数'] = prp.getLoanOverdueTimes(loanPayRecordMergeDf,
                                                                                                 reportTime, 1, 3) \
                                                                         + cip.getLoanOverdueTimes(
            creditCardPayRecordMergeDf, reportTime, 1, 3) + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ,
                                                                                    reportTime, 1, 3)
        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近6月逾期期数大于或等于“1”的次数'] = \
            prp.getLoanOverdueTimes(loanPayRecordMergeDf, reportTime, 1, 6) + cip.getLoanOverdueTimes(
                creditCardPayRecordMergeDf, reportTime, 1, 6) \
            + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ, reportTime, 1, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近12月逾期期数大于或等于“1”的次数'] = \
            prp.getLoanOverdueTimes(loanPayRecordMergeDf, reportTime, 1, 12) + cip.getLoanOverdueTimes(
                creditCardPayRecordMergeDf, reportTime, 1, 12) \
            + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ, reportTime, 1, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近24月逾期期数大于或等于“1”的次数'] = \
            prp.getLoanOverdueTimes(loanPayRecordMergeDf, reportTime, 1, 24) + cip.getLoanOverdueTimes(
                creditCardPayRecordMergeDf, reportTime, 1, 24) \
            + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ, reportTime, 1, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近6月逾期期数大于或等于“2”的次数'] = \
            prp.getLoanOverdueTimes(loanPayRecordMergeDf, reportTime, 2, 6) + cip.getLoanOverdueTimes(
                creditCardPayRecordMergeDf, reportTime, 2, 6) \
            + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ, reportTime, 2, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近12月逾期期数大于或等于“2”的次数'] = \
            prp.getLoanOverdueTimes(loanPayRecordMergeDf, reportTime, 2, 12) + cip.getLoanOverdueTimes(
                creditCardPayRecordMergeDf, reportTime, 2, 12) \
            + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ, reportTime, 2, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近24月逾期期数大于或等于“2”的次数'] = \
            prp.getLoanOverdueTimes(loanPayRecordMergeDf, reportTime, 2, 24) + cip.getLoanOverdueTimes(
                creditCardPayRecordMergeDf, reportTime, 2, 24) \
            + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ, reportTime, 2, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近6月逾期期数大于或等于“3”的次数'] = \
            prp.getLoanOverdueTimes(loanPayRecordMergeDf, reportTime, 3, 6) + cip.getLoanOverdueTimes(
                creditCardPayRecordMergeDf, reportTime, 3, 6) \
            + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ, reportTime, 3, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近12月逾期期数大于或等于“3”的次数'] = \
            prp.getLoanOverdueTimes(loanPayRecordMergeDf, reportTime, 3, 12) + cip.getLoanOverdueTimes(
                creditCardPayRecordMergeDf, reportTime, 3, 12) \
            + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ, reportTime, 3, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近24月逾期期数大于或等于“3”的次数'] = \
            prp.getLoanOverdueTimes(loanPayRecordMergeDf, reportTime, 3, 24) + cip.getLoanOverdueTimes(
                creditCardPayRecordMergeDf, reportTime, 3, 24) \
            + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ, reportTime, 3, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近12月逾期期数大于或等于“4”的次数'] = \
            prp.getLoanOverdueTimes(loanPayRecordMergeDf, reportTime, 4, 12) + cip.getLoanOverdueTimes(
                creditCardPayRecordMergeDf, reportTime, 4, 12) \
            + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ, reportTime, 4, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '全账户近24月逾期期数大于或等于“4”的次数'] = \
            prp.getLoanOverdueTimes(loanPayRecordMergeDf, reportTime, 4, 24) + cip.getLoanOverdueTimes(
                creditCardPayRecordMergeDf, reportTime, 4, 24) \
            + cip.getLoanOverdueTimes(creditCardPayRecordMergeDfZ, reportTime, 4, 24)

        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近24个月是否出现"G"'] = prp.isExistsInd(loanPayRecordMergeDf, reportTime,
                                                                                    "G", 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷记卡账户近24个月是否出现"G"'] = prp.isExistsInd(creditCardPayRecordMergeDf,
                                                                                     reportTime, "G", 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '准贷记卡账户近24个月是否出现"G"'] = prp.isExistsInd(creditCardPayRecordMergeDfZ,
                                                                                      reportTime, "G", 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '贷款账户近24个月是否出现"Z"'] = prp.isExistsInd(loanPayRecordMergeDf, reportTime,
                                                                                    "Z", 24)

        payRcdStatusDf.loc[payRcdStatusIndex, '用户所有贷款账户过去24个月存在逾期的账户数目'] = prp.getLoanOverdueCount(loanPayRecordMergeDf,
                                                                                                   reportTime, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '用户所有贷款账户过去24个月状态正常账户数目'] = prp.getLoanNormalCount(loanPayRecordMergeDf,
                                                                                                 reportTime, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '用户所有贷记卡账户过去24个月存在逾期的账户数目'] = prp.getLoanOverdueCount(
            creditCardPayRecordMergeDf, reportTime, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '用户所有贷记卡账户过去24个月状态正常的账户数目'] = prp.getLoanNormalCount(
            creditCardPayRecordMergeDf, reportTime, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '用户所有准贷记卡账户过去24个月存在逾期的账户数目'] = prp.getLoanOverdueCount(
            creditCardPayRecordMergeDfZ, reportTime, 24)
        payRcdStatusDf.loc[payRcdStatusIndex, '用户所有准贷记卡账户过去24个月状态正常的账户数目'] = prp.getLoanNormalCount(
            creditCardPayRecordMergeDfZ, reportTime, 24)

        payRcdStatusDf.loc[payRcdStatusIndex, '用户过去3个月最大逾期期数'] = prp.getPayRcdMaxOverdueNumAllAccout(
            loanPayRecordMergeDf, creditCardPayRecordMergeDf, creditCardPayRecordMergeDfZ, reportTime, 3)
        payRcdStatusDf.loc[payRcdStatusIndex, '用户过去6个月最大逾期期数'] = prp.getPayRcdMaxOverdueNumAllAccout(
            loanPayRecordMergeDf, creditCardPayRecordMergeDf, creditCardPayRecordMergeDfZ, reportTime, 6)
        payRcdStatusDf.loc[payRcdStatusIndex, '用户过去12个月最大逾期期数'] = prp.getPayRcdMaxOverdueNumAllAccout(
            loanPayRecordMergeDf, creditCardPayRecordMergeDf, creditCardPayRecordMergeDfZ, reportTime, 12)
        payRcdStatusDf.loc[payRcdStatusIndex, '用户过去24个月最大逾期期数'] = prp.getPayRcdMaxOverdueNumAllAccout(
            loanPayRecordMergeDf, creditCardPayRecordMergeDf, creditCardPayRecordMergeDfZ, reportTime, 24)

        # 概要信息里的字段，从还款状态计算
        briefInfoDf_overdueInfoSum.loc[overdueInfoSumIndex, '该用户过去5年出现逾期的所有账户数目'] = \
            prp.getLoanOverdueCount(loanPayRecordMergeDf, reportTime, 24 * 5) + prp.getLoanOverdueCount(
                creditCardPayRecordMergeDf, reportTime, 24 * 5) \
            + prp.getLoanOverdueCount(creditCardPayRecordMergeDfZ, reportTime, 24 * 5)
        #1112
        otherDf.loc[otherIndex, '近12个月最大逾期期数'] = prp.getPayRcdMaxOverdueNumAccout(
            loanPayRecordMergeDf, creditCardPayRecordMergeDf, reportTime, 12)

    # 解析贷记卡还款记录指标
    def parseCreditCardMergeAndPayRecordDf(self,df, payRcdDf,reportTime):
        if not df.empty and not payRcdDf.empty:
            # 正常
            normalDf = df[(df['账户状态'] != '未激活') & (df['账户状态'] != '销户') & (df['账户状态'] != '呆账')]
            if not normalDf.empty:
                overduePayRcdDf = payRcdDf[payRcdDf['账户编号'].isin(normalDf['账户编号'].values)]
                overduePayRcdDf = utils.replacePayRcdStatus(overduePayRcdDf)
                # 计算当前贷款，为还款记录的最后一期 0529
                curOverduePayRcdDf = overduePayRcdDf.sort_values(by=["账户编号", "还款日期"], ascending=(True, False))
                curOverduePayRcdDf = curOverduePayRcdDf.groupby(['账户编号']).head(1)
                curOverduePayRcdDf = curOverduePayRcdDf[curOverduePayRcdDf['还款状态'] > 0]
                # 临时保存,不用过滤还款状态为0的
                payRcdMaxOverdueDf = overduePayRcdDf;
                # overduePayRcdDf = overduePayRcdDf[overduePayRcdDf['还款状态'] > 0]
                # creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前贷记卡逾期账户数'] = curOverduePayRcdDf['账户编号'].unique().size
                # 从“贷记卡信息”中提取，剔除“账户状态”为未激活、销户、呆账、呆帐后，“当前信用卡逾期账户数”/未销户贷记卡账户数（剔除“账户状态”为未激活、销户、呆账、呆帐后记录条数）
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前贷记卡逾期账户数占比'] = round(
                    curOverduePayRcdDf['账户编号'].unique().size / normalDf.index.size, 2)
                # 从“贷记卡信息”中提取，剔除“账户状态”为未激活、销户、呆账、呆帐后，对（当前信用卡逾期账户数）按“开户机构代码”去重统计账户状态为逾期，按按“开户机构代码”去重后的记录条数
                overdueCreditCardDf = normalDf[normalDf['账户编号'].isin(curOverduePayRcdDf['账户编号'].values)]
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前贷记卡逾期机构数'] = overdueCreditCardDf[
                    '发卡机构'].unique().size
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前贷记卡逾期机构数占比'] = round(
                    creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '当前贷记卡逾期机构数'] / normalDf[
                        '发卡机构'].unique().size, 2)
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近3月贷记卡最大逾期期数'] = cip.getPayRcdMaxOverdueNum(
                    payRcdMaxOverdueDf, 3);
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近6月贷记卡最大逾期期数'] = cip.getPayRcdMaxOverdueNum(
                    payRcdMaxOverdueDf, 6);
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近9月贷记卡最大逾期期数'] = cip.getPayRcdMaxOverdueNum(
                    payRcdMaxOverdueDf, 9);
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近12月贷记卡最大逾期期数'] = cip.getPayRcdMaxOverdueNum(
                    payRcdMaxOverdueDf, 12);
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '近24月贷记卡最大逾期期数'] = cip.getPayRcdMaxOverdueNum(
                    payRcdMaxOverdueDf, 24);
                creditCardAccountInfoDf.loc[
                    creditCardAccountInfoIndex, '近24月贷记卡最大逾期距离现在的月数'] = cip.getPayRcdMaxOverdueNumMonth(
                    payRcdMaxOverdueDf, normalDf, reportTime, 24);
                creditCardAccountInfoDf.loc[
                    creditCardAccountInfoIndex, '最近3个月贷记卡最大连续逾期月份数'] = cip.getContinuousOverdueMonth(payRcdMaxOverdueDf,
                                                                                                     normalDf, 3);
                creditCardAccountInfoDf.loc[
                    creditCardAccountInfoIndex, '最近6个月贷记卡最大连续逾期月份数'] = cip.getContinuousOverdueMonth(payRcdMaxOverdueDf,
                                                                                                     normalDf, 6);
                creditCardAccountInfoDf.loc[
                    creditCardAccountInfoIndex, '最近9个月贷记卡最大连续逾期月份数'] = cip.getContinuousOverdueMonth(payRcdMaxOverdueDf,
                                                                                                     normalDf, 9);
                creditCardAccountInfoDf.loc[
                    creditCardAccountInfoIndex, '最近12个月贷记卡最大连续逾期月份数'] = cip.getContinuousOverdueMonth(
                    payRcdMaxOverdueDf, normalDf, 12);
                creditCardAccountInfoDf.loc[
                    creditCardAccountInfoIndex, '最近24个月贷记卡最大连续逾期月份数'] = cip.getContinuousOverdueMonth(
                    payRcdMaxOverdueDf, normalDf, 24);
                # payRcdTimesDf = payRcdDf[payRcdDf['账户编号'].isin(normalDf['账户编号'].values)]
                # payRcdTimesDf = payRcdTimesDf.sort_values(by=["账户编号", "还款日期"], ascending=(True, False))
                # payRcdTimesDf = payRcdTimesDf.groupby(['账户编号']).head(24)
                # payStatus = ["G", "D", "C", "N", "M", "1", "2", "3", "4", "5", "6", "7"]
                # payRcdTimesDf = payRcdTimesDf[payRcdTimesDf['还款状态'].isin(payStatus)]
                # payRcdTimes = payRcdTimesDf.groupby(['账户编号'])['还款状态'].count()
                # # 从“贷记卡信息”中提取，剔除未激活、销户、呆账、呆帐后，各账户的还款次数统计“24个月（账户）还款状态”包含"G","D","C","N","M"及数字的个数
                # creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '贷记卡24期还款记录次数'] = np.max(payRcdTimes)
                creditCardAccountInfoDf.loc[creditCardAccountInfoIndex, '贷记卡24期还款记录次数'] = cip.getPayRcdCount(payRcdDf,normalDf,24)
            otherDf.loc[otherIndex, '近6个月有贷记卡还款记录的月份数'] = cip.getPayRcdCountNew(payRcdDf,reportTime,6)
            otherDf.loc[otherIndex, '近12个月有贷记卡还款记录的月份数'] = cip.getPayRcdCountNew(payRcdDf, reportTime, 12)

    # 解析贷款还款记录指标
    def parseLoanMergeAndPayRecordDf(self,df, payRcdDf,reportTime):
        if not df.empty and not payRcdDf.empty:
            # 正常
            normalDf = df[(df['账户状态'] != '结清') & (df['账户状态'] != '转出') & (df['账户状态'] != '呆账')]
            overduePayRcdDf = payRcdDf[payRcdDf['账户编号'].isin(normalDf['账户编号'].values)]
            overduePayRcdDf = utils.replacePayRcdStatus(overduePayRcdDf)
            # 计算当前贷款，为还款记录的最后一期 0529
            curOverduePayRcdDf = overduePayRcdDf.sort_values(by=["账户编号", "还款日期"], ascending=(True, False))
            curOverduePayRcdDf = curOverduePayRcdDf.groupby(['账户编号']).head(1)
            curOverduePayRcdDf = curOverduePayRcdDf[curOverduePayRcdDf['还款状态'] > 0]
            # 临时保存,不用过滤还款状态为0的
            payRcdMaxOverdueDf = overduePayRcdDf;
            # 所有逾期的记录
            # overduePayRcdDf = overduePayRcdDf[overduePayRcdDf['还款状态']>0]
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款逾期账户数'] = curOverduePayRcdDf['账户编号'].unique().size
            if normalDf.index.size > 0:
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款逾期账户数占比'] = round(
                    loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款逾期账户数'] / normalDf.index.size, 3)
            # 存在逾期的贷款账户 非结清的过滤出逾期的账户号
            overdueLoanDf = normalDf[normalDf['账户编号'].isin(curOverduePayRcdDf['账户编号'].values)]
            loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款逾期机构数'] = overdueLoanDf['管理机构'].unique().size
            if normalDf['管理机构'].unique().size > 0:
                loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款逾期机构数占比'] = round(
                    loanAccountInfoDf.loc[loanAccountInfoIndex, '当前贷款逾期机构数'] / normalDf['管理机构'].unique().size, 2)

            # 还款记录按日期排序最近3笔的最大逾期期数
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近1月贷款的最大逾期期数'] = prp.getPayRcdMaxOverdueNum(payRcdMaxOverdueDf,
                                                                                                     1);
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近3月贷款的最大逾期期数'] = prp.getPayRcdMaxOverdueNum(payRcdMaxOverdueDf,
                                                                                                     3);
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近6月贷款的最大逾期期数'] = prp.getPayRcdMaxOverdueNum(payRcdMaxOverdueDf,
                                                                                                     6);
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近9月贷款的最大逾期期数'] = prp.getPayRcdMaxOverdueNum(payRcdMaxOverdueDf,
                                                                                                     9);
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近12月贷款的最大逾期期数'] = prp.getPayRcdMaxOverdueNum(
                payRcdMaxOverdueDf, 12);
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近24月贷款的最大逾期期数'] = prp.getPayRcdMaxOverdueNum(
                payRcdMaxOverdueDf, 24);
            loanAccountInfoDf.loc[loanAccountInfoIndex, '近24月贷款最大逾期距离现在的月数'] = prp.getPayRcdMaxOverdueNumMonth(
                payRcdMaxOverdueDf, normalDf, reportTime, 24);
            payStatus = ["G", "D", "C", "N", "M", "1", "2", "3", "4", "5", "6", "7"]
            # 贷款24期还款记录次数 剔除结清 转出 呆账
            # payRcdTimesDf = payRcdDf[payRcdDf['账户编号'].isin(normalDf['账户编号'].values)]
            # payRcdTimesDf = payRcdTimesDf.sort_values(by=["账户编号", "还款日期"], ascending=(True, False))
            # payRcdTimesDf = payRcdTimesDf.groupby(['账户编号']).head(24)
            # # 从“贷款信息”中提取，剔除“账户状态”为结清、转出、呆账、呆帐后，各账户的还款次数统计“24个月（账户）还款状态”包含"G","D","C","N","M"及数字的个数，MAX（各账户的还款次数）
            # payRcdTimesDf = payRcdTimesDf[payRcdTimesDf['还款状态'].isin(payStatus)]
            # payRcdTimes = payRcdTimesDf.groupby(['账户编号'])['还款状态'].count()
            #loanAccountInfoDf.loc[loanAccountInfoIndex, '贷款24期还款记录次数'] = np.max(payRcdTimes)
            loanAccountInfoDf.loc[loanAccountInfoIndex, '贷款24期还款记录次数'] = prp.getPayRcdCount(payRcdDf,normalDf,24);
            otherDf.loc[otherIndex, '最近6个月有贷款还款记录的月份数'] = prp.getPayRcdCountNew(payRcdDf, reportTime, 6);
            otherDf.loc[otherIndex, '最近12个月有贷款还款记录的月份数'] = prp.getPayRcdCountNew(payRcdDf, reportTime, 12);

    #最近一次逾期距今月份数
    def getLastOverdueMonth(self, loanMergeDf, creditCardMergeDf, loanPayRecordMergeDf, creditCardPayRecordMergeDf,
                            reportTime):
        result = 120;
        if np.sum(loanMergeDf['当前逾期期数']) > 0 or np.sum(creditCardMergeDf['当前逾期期数']) > 0:
            result = 0
        else:
            payRcdDf = loanPayRecordMergeDf.sort_values(by=["账户编号", "还款日期"], ascending=(True, False))
            payRcdDf = payRcdDf.groupby(['账户编号']).head(60)
            payRcdDf = utils.replacePayRcdStatusOverdue(payRcdDf)  # 替换逾期
            payRcdDf = payRcdDf[payRcdDf['还款状态'] > 0]
            payRcdDf = utils.replacePayRcdStatusOverdue(payRcdDf)  # 替换逾期
            loanOverdueDate = np.max(payRcdDf['还款日期'])
            payRcdDfCredit = creditCardPayRecordMergeDf.sort_values(by=["账户编号", "还款日期"], ascending=(True, False))
            payRcdDfCredit = utils.replacePayRcdStatusOverdue(payRcdDfCredit)  # 替换逾期
            payRcdDfCredit = payRcdDfCredit.groupby(['账户编号']).head(60)
            payRcdDfCredit = payRcdDfCredit[payRcdDfCredit['还款状态'] > 0]
            if not payRcdDf.empty or not payRcdDfCredit.empty:
                creditOverdueDate = np.max(payRcdDfCredit['还款日期'])
                # reportTime = str(np.datetime64(reportTime, "M")) + "-02"
                reportTime = utils.get_last_month_first_day_v2(reportTime)
                overdueDate = None
                if str(loanOverdueDate) != "nan" and str(creditOverdueDate) != "nan":
                    overdueDate = max([loanOverdueDate, creditOverdueDate])
                elif str(loanOverdueDate) == "nan" and str(creditOverdueDate) != "nan":
                    overdueDate = creditOverdueDate
                elif str(loanOverdueDate) != "nan" and str(creditOverdueDate) == "nan":
                    overdueDate = loanOverdueDate
                if overdueDate != None:
                    result = utils.difMonthReportTime(overdueDate, reportTime)
        otherDf.loc[otherIndex, "最近一次逾期距今月份数"] = result
    #近12个月贷款和贷记卡还款状态异常次数 慧算1112
    #count(三类贷款账户m1-m12中D/Z/B/G的个数)+count(贷记卡m1-m12中D/Z/B/G的个数)
    def calcPayRcdStatus(self,loanPayRecordMergeDf,creditCardPayRecordMergeDf,reportTime):
        reportTime = str(np.datetime64(reportTime, "M")) + "-02"
        statusList =['G','B','Z','D']
        payDate = utils.getLastMonthDate(reportTime, 12)
        loanPayRcdTmpDf = loanPayRecordMergeDf[loanPayRecordMergeDf['还款日期']>=payDate]
        loanTimes = loanPayRcdTmpDf[(loanPayRcdTmpDf['还款状态'].isin(statusList))].index.size
        creditPayRcdTmpDf = creditCardPayRecordMergeDf[creditCardPayRecordMergeDf['还款日期']>=payDate]
        creditTimes = creditPayRcdTmpDf[(creditPayRcdTmpDf['还款状态'].isin(statusList))].index.size
        result = loanTimes+creditTimes
        otherDf.loc[otherIndex, "近12个月贷款和贷记卡还款状态异常次数"] = result

    #yuan新开发部分,暂时不存入数据库

    #基础个人信息1
    def parseBaseIdentity(self,reportTime,docXml):
        tmpNode =docXml.documentElement.getElementsByTagName("BASEINFO")
        if len(tmpNode)>0:
            baseInfo = docXml.documentElement.getElementsByTagName("BASEINFO")[0]
            # print(str(baseInfo))
            # self.saveNode(baseInfo)
            tmpNode = docXml.documentElement.getElementsByTagName("MARRIAGEINFOSG")

            baseIdentityInfoDf.loc[baseIdentityInfo, '性别'] = self.getNodeData(baseInfo,"SEXDESC")
            baseIdentityInfoDf.loc[baseIdentityInfo, '出生日期'] = self.getNodeData(baseInfo,"BIRTHDAY")
            baseIdentityInfoDf.loc[baseIdentityInfo, '国籍'] = self.getNodeData(baseInfo,"NATIONDESC")
            baseIdentityInfoDf.loc[baseIdentityInfo, '户籍地址'] = self.getNodeData(baseInfo,"ADDRESS")
            if len(tmpNode)>0:
                marriageinfosg = docXml.documentElement.getElementsByTagName("MARRIAGEINFOSG")[0]
                self.parseMate(marriageinfosg)
                baseIdentityInfoDf.loc[baseIdentityInfo, '婚姻状况'] = self.getNodeData(marriageinfosg,"MARSTATUDESC")
            baseIdentityInfoDf.loc[baseIdentityInfo, '学历'] = self.getNodeData(baseInfo,"DEGREEDESC")
            baseIdentityInfoDf.loc[baseIdentityInfo, '学位'] = self.getNodeData(baseInfo,"DEGREEDESC")
            baseIdentityInfoDf.loc[baseIdentityInfo, '通讯地址'] = self.getNodeData(baseInfo,"COMMUADDRESS")
            baseIdentityInfoDf.loc[baseIdentityInfo, '就业状况'] = self.getNodeData(baseInfo,"WORKSTATUSDESC")
            # mobiles = docXml.documentElement.getElementsByTagName("TELEPHONEINFOSGLS")
            # if len(mobiles)>0:
            #     telephoneinfosgls = docXml.documentElement.getElementsByTagName("TELEPHONEINFOSGLS")[0]
            #     telephoneinfosg = telephoneinfosgls.getElementsByTagName("TELEPHONEINFOSG");
            #     identityInfoDf.loc[identityInfoIndex, '历史手机号码数'] = len(telephoneinfosg)
            #     identityInfoDf.loc[identityInfoIndex, '近3个月手机号码数'] = self.getLastMonthMobileCount(telephoneinfosg, 3, reportTime)
            #     identityInfoDf.loc[identityInfoIndex, '近6个月手机号码数'] = self.getLastMonthMobileCount(telephoneinfosg, 6, reportTime)
            #     identityInfoDf.loc[identityInfoIndex, '近12个月手机号码数'] = self.getLastMonthMobileCount(telephoneinfosg, 12, reportTime)
            #     identityInfoDf.loc[identityInfoIndex, '近24个月手机号码数'] = self.getLastMonthMobileCount(telephoneinfosg, 24, reportTime)
            # print(identityInfoDf.to_json(orient="index",force_ascii=False))

    #基础配偶信息2
    def parseMate(self,marriageinfosg):
        self.saveNode(marriageinfosg)
        baseMateInfoDf.loc[baseMateInfo, '姓名'] = self.getNodeData(marriageinfosg,"SPUOSENAME")
        baseMateInfoDf.loc[baseMateInfo, '证件号码'] = self.getNodeData(marriageinfosg,"CERTNUM")
        baseMateInfoDf.loc[baseMateInfo, '工作单位'] =  self.getNodeData(marriageinfosg,"COMPANY")
        baseMateInfoDf.loc[baseMateInfo, '联系电话'] = self.getNodeData(marriageinfosg,"TELNUM")
        # print(mateInfoDf.to_json(orient="index", force_ascii=False))

    # 基础居住信息3
    def parseLiveInfo(self,docXml):
        # residenceinfosgls = docXml.documentElement.getElementsByTagName("RESIDENCEINFOSGLS")
        residenceinfosg = docXml.documentElement.getElementsByTagName("RESIDENCEINFOSG");
        df = pd.DataFrame()
        for i in range(0, len(residenceinfosg)):
            # self.saveNode(residenceinfosg[i])
            resistatusdesc = self.getNodeData(residenceinfosg[i], "RESISTATUSDESC")
            address = self.getNodeData(residenceinfosg[i], "ADDRESS")
            telnum = self.getNodeData(residenceinfosg[i], "TELNUM")
            updatedateinfo = self.getNodeData(residenceinfosg[i], "UPDATEDATEINFO")
            df.loc[i, 0] = resistatusdesc
            df.loc[i, 1] = address
            df.loc[i, 2] = telnum
            df.loc[i, 3] = updatedateinfo
        if len(residenceinfosg)>0:
            baseLiveInfoDf.loc[baseLiveInfo, '居住地址'] = self.getNodeData(residenceinfosg[0],"ADDRESS")
            baseLiveInfoDf.loc[baseLiveInfo, '住宅电话'] = self.getNodeData(residenceinfosg[0],"TELNUM")
            baseLiveInfoDf.loc[baseLiveInfo, '历史居住地址个数'] = len(residenceinfosg);
            # curDate = np.datetime64(time.strftime("%Y-%m-%d"));
            # last3year = str(curDate)[0:4]
            # last3yearDate = str(int(last3year) - 3) + str(curDate)[4:10]
            # lastLiveDf = df[df[3] >= last3yearDate];
            # liveInfoDf.loc[liveInfoIndex, '最近3年内居住地址个数'] = lastLiveDf.index.size ;
            # houseIndex = df[df[0] == '自置'].index.size > 0
            # if (houseIndex):
            #     houseStr = '是'
            # else:
            #     houseStr = '否'
            # liveInfoDf.loc[liveInfoIndex, '当前居住状况-是否具有自有住房'] = houseStr;
            baseLiveInfoDf.loc[baseLiveInfo, '居住状况'] = self.getNodeData(residenceinfosg[0],"RESISTATUSDESC")
            baseLiveInfoDf.loc[baseLiveInfo, '信息更新日期'] = self.getNodeData(residenceinfosg[0],"UPDATEDATEINFO")
            # print(liveInfoDf.to_json(orient="index", force_ascii=False))


    # 基础职业信息4
    def parseOccupationInfoDf(self,docXml):
        vocationinfosg = docXml.documentElement.getElementsByTagName("VOCATIONINFOSG");
        df = pd.DataFrame()
        if len(vocationinfosg)>0:
            for i in range(0, len(vocationinfosg)):
                # df.loc[i, 0] = self.getNodeData(vocationinfosg[i], "WORDSTATUSDESC")
                # self.saveNode(vocationinfosg[i])
                df.loc[i, 0] = self.getNodeData(vocationinfosg[i], "CPNTYPEDESC")
                df.loc[i, 1] = self.getNodeData(vocationinfosg[i], "TRADEDESC")
                df.loc[i, 2] = self.getNodeData(vocationinfosg[i], "VOCATIONDESC")
                df.loc[i, 3] = self.getNodeData(vocationinfosg[i], "VOCATIONTITLEDESC")
                df.loc[i, 4] = self.getNodeData(vocationinfosg[i], "COMPANY")
                df.loc[i, 5] = self.getNodeData(vocationinfosg[i], "CPNADDRESS")
                df.loc[i, 6] = self.getNodeData(vocationinfosg[i], "CPNTELNUM")
                df.loc[i, 7] = self.getNodeData(vocationinfosg[i], "BEGINYEAR")
                df.loc[i, 8] = self.getNodeData(vocationinfosg[i], "INFOUPDATEDATE")
            baseOccupationInfoDf.loc[baseOccupationInfo, '工作单位'] = self.getNodeData(vocationinfosg[0], "COMPANY")
            # last3yearDate = utils.getLastMonthDate(reportTime, 12 * 3)
            # last3yearOccDf = df[df[8] >= last3yearDate]
            # baseOccupationInfoDf.loc[baseOccupationInfo, '最近3年内工作单位数'] = last3yearOccDf.index.size;
            baseOccupationInfoDf.loc[baseOccupationInfo, '单位电话'] = self.getNodeData(vocationinfosg[0], "CPNTELNUM")
            # try:
            #     minDateIndex = np.argmin(df[7]);
            #     maxDateIndex = np.argmax(df[7]);
            #     rowYearMin = df.loc[minDateIndex, :].dropna()
            #     rowYearMax = df.loc[maxDateIndex, :].dropna()
            #     if rowYearMin[7] != "":
            #         occupationInfoDf.loc[occupationInfoIndex, '最早进入本单位年份距报告日期时长'] = int(
            #             str(np.datetime64(reportTime, "Y"))) - int(rowYearMin[7])
            #     if rowYearMax[7] != "":
            #         occupationInfoDf.loc[occupationInfoIndex, '最新进入本单位年份距报告日期时长'] = int(
            #             str(np.datetime64(reportTime, "Y"))) - int(rowYearMax[7])
            # except:
            #     logger.error("最早进入本单位年份距报告日期时长解析异常")
            baseOccupationInfoDf.loc[baseOccupationInfo, '单位性质'] = self.getNodeData(vocationinfosg[0], "CPNTYPEDESC")
            baseOccupationInfoDf.loc[baseOccupationInfo, '单位地址'] = self.getNodeData(vocationinfosg[0], "CPNADDRESS")
            baseOccupationInfoDf.loc[baseOccupationInfo, '职业'] = self.getNodeData(vocationinfosg[0], "VOCATIONDESC")
            baseOccupationInfoDf.loc[baseOccupationInfo, '行业'] = self.getNodeData(vocationinfosg[0], "TRADEDESC")
            baseOccupationInfoDf.loc[baseOccupationInfo, '职务'] = self.getNodeData(vocationinfosg[0], "TITLEDESC")
            baseOccupationInfoDf.loc[baseOccupationInfo, '职称'] = self.getNodeData(vocationinfosg[0], "VOCATIONTITLEDESC")
            baseOccupationInfoDf.loc[baseOccupationInfo, '进入本单位年份'] =self.getNodeData(vocationinfosg[0], "BEGINYEAR")
            baseOccupationInfoDf.loc[baseOccupationInfo, '信息更新日期'] = self.getNodeData(vocationinfosg[0], "INFOUPDATEDATE")
            # occupationInfoDf.loc[occupationInfoIndex, '历史工作单位数'] = len(vocationinfosg)
            # print(occupationInfoDf.to_json(orient="index", force_ascii=False))

    # 个人信用报告"数字解读"5
    def personalCreditInfo(self,docXml):
        tmpNode =docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")
        if len(tmpNode)>0:
            personalCreditInfo =  docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")[0]
            personalCreditInfoDf.loc[baseMateInfo, '数字解读'] = self.getNodeData(personalCreditInfo,"NUMBERREAD")
            personalCreditInfoDf.loc[baseMateInfo, '相对位置'] = self.getNodeData(personalCreditInfo,"RELPOSITION")
            personalCreditInfoDf.loc[baseMateInfo, '说明'] =  self.getNodeData(personalCreditInfo,"SCOREEXPLAINNUM")

    # 信贷交易信息提示6
    def parseBriefInfoHeaderLoanTradeInfo(self,docXml):
        credittipsinfo = docXml.documentElement.getElementsByTagName("CREDITTIPSINFO");
        if len(credittipsinfo)>0:
            briefInfoHeaderLoanTradeInfoDf.loc[briefInfoHeaderLoanTradeInfo, '个人住房贷款账户数'] = utils.toInt(self.getNodeData(credittipsinfo[0],"ACCOUNTNUM"))
            briefInfoHeaderLoanTradeInfoDf.loc[briefInfoHeaderLoanTradeInfo, '个人住房贷款首笔业务发放月份'] = self.getNodeData(credittipsinfo[0],"FIRSTBUSMONTH")

            # self.saveNode(credittipsinfo[0])
            briefInfoHeaderLoanTradeInfoDf.loc[briefInfoHeaderLoanTradeInfo, '个人商用房贷款（包括商住两用）账户数'] = utils.toInt(self.getNodeData(credittipsinfo[1],"ACCOUNTNUM"))
            briefInfoHeaderLoanTradeInfoDf.loc[briefInfoHeaderLoanTradeInfo, '个人商用房贷款（包括商住两用）首笔业务发放月份'] = self.getNodeData(credittipsinfo[1],"FIRSTBUSMONTH")

            # self.saveNode(credittipsinfo[1])
            briefInfoHeaderLoanTradeInfoDf.loc[briefInfoHeaderLoanTradeInfo, '其他类贷款账户数'] = utils.toInt(self.getNodeData(credittipsinfo[2],"ACCOUNTNUM"))
            briefInfoHeaderLoanTradeInfoDf.loc[briefInfoHeaderLoanTradeInfo, '其他类贷款首笔业务发放月份'] = self.getNodeData(credittipsinfo[2],"FIRSTBUSMONTH")

            # self.saveNode(credittipsinfo[2])
            briefInfoHeaderLoanTradeInfoDf.loc[briefInfoHeaderLoanTradeInfo, '贷记卡账户数'] = utils.toInt(self.getNodeData(credittipsinfo[3],"ACCOUNTNUM"))
            briefInfoHeaderLoanTradeInfoDf.loc[briefInfoHeaderLoanTradeInfo, '贷记卡首笔业务发放月份'] = self.getNodeData(credittipsinfo[3],"FIRSTBUSMONTH")

            # self.saveNode(credittipsinfo[3])
            briefInfoHeaderLoanTradeInfoDf.loc[briefInfoHeaderLoanTradeInfo, '准贷记卡账户数'] = utils.toInt(self.getNodeData(credittipsinfo[4],"ACCOUNTNUM"))
            briefInfoHeaderLoanTradeInfoDf.loc[briefInfoHeaderLoanTradeInfo, '准贷记卡首笔业务发放月份'] = self.getNodeData(credittipsinfo[4],"FIRSTBUSMONTH")

            # self.saveNode(credittipsinfo[4])
            #汇算
            # moths = [self.getNodeData(credittipsinfo[0], "FIRSTBUSMONTH"),
            #                                               self.getNodeData(credittipsinfo[1], "FIRSTBUSMONTH"),self.getNodeData(credittipsinfo[2], "FIRSTBUSMONTH")]
            # moths=utils.get_real_arr(moths)
            # if len(moths)>0:
            #     otherDf.loc[otherIndex, '首笔贷款发放月份'] = min(moths)
            # otherDf.loc[otherIndex, '首张贷记卡发卡月份'] = self.getNodeData(credittipsinfo[3], "FIRSTBUSMONTH")

    #信贷交易违约信息概要7
    def parseCreditTransactionDefault(self,docXml):
        recoveredinfosumsmry = docXml.documentElement.getElementsByTagName("RECOVEREDINFOSUMSMRY");
        recorinfosumsg = docXml.documentElement.getElementsByTagName("RECORINFOSUMSG")
        if len(recoveredinfosumsmry)>0:
            if len(recorinfosumsg)>0:
                self.saveNode(recorinfosumsg[0])
                briefInfoDf_recoveryInfoSumY.loc[recoveryInfoSumIndexY, '资产处置业务账户数'] = utils.toInt(self.getNodeData(recorinfosumsg[0],"ACCOUNTNUM"))
                briefInfoDf_recoveryInfoSumY.loc[recoveryInfoSumIndexY, '资产处置业务余额'] = utils.replaceAmt(self.getNodeData(recorinfosumsg[0],"BALANCE"))
                briefInfoDf_recoveryInfoSumY.loc[recoveryInfoSumIndexY, '垫款业务账户数'] = utils.toInt(self.getNodeData(recorinfosumsg[1],"ACCOUNTNUM"))
                briefInfoDf_recoveryInfoSumY.loc[recoveryInfoSumIndexY, '垫款业务余额'] = utils.replaceAmt(self.getNodeData(recorinfosumsg[1],"BALANCE"))
            briefInfoDf_recoveryInfoSumY.loc[recoveryInfoSumIndexY, '合计总账户数'] = utils.toInt(self.getNodeData(recoveredinfosumsmry[0],"ACCOUNTNUMSUM"))
            briefInfoDf_recoveryInfoSumY.loc[recoveryInfoSumIndexY, '合计总余额'] = utils.replaceAmt(self.getNodeData(recoveredinfosumsmry[0],"BALANCESUM"))


    # 信贷交易授信及负债信息概要8
    def parseCreditTransactionCredit(self,docXml):
        tmpNode =docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")
        if len(tmpNode)>0:
            personalCreditInfo =  docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")[0]
            personalCreditInfoDf.loc[baseMateInfo, '数字解读'] = self.getNodeData(personalCreditInfo,"NUMBERREAD")
            personalCreditInfoDf.loc[baseMateInfo, '相对位置'] = self.getNodeData(personalCreditInfo,"RELPOSITION")
            personalCreditInfoDf.loc[baseMateInfo, '说明'] =  self.getNodeData(personalCreditInfo,"SCOREEXPLAINNUM")

    # 非信贷交易信息概要9
    def personalCreditInfo(self,docXml):
        tmpNode =docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")
        if len(tmpNode)>0:
            personalCreditInfo =  docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")[0]
            personalCreditInfoDf.loc[baseMateInfo, '数字解读'] = self.getNodeData(personalCreditInfo,"NUMBERREAD")
            personalCreditInfoDf.loc[baseMateInfo, '相对位置'] = self.getNodeData(personalCreditInfo,"RELPOSITION")
            personalCreditInfoDf.loc[baseMateInfo, '说明'] =  self.getNodeData(personalCreditInfo,"SCOREEXPLAINNUM")

    # 公共信息概要10
    def personalCreditInfo(self,docXml):
        tmpNode =docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")
        if len(tmpNode)>0:
            personalCreditInfo =  docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")[0]
            personalCreditInfoDf.loc[baseMateInfo, '数字解读'] = self.getNodeData(personalCreditInfo,"NUMBERREAD")
            personalCreditInfoDf.loc[baseMateInfo, '相对位置'] = self.getNodeData(personalCreditInfo,"RELPOSITION")
            personalCreditInfoDf.loc[baseMateInfo, '说明'] =  self.getNodeData(personalCreditInfo,"SCOREEXPLAINNUM")


    # 查询记录概要11
    def personalCreditInfo(self,docXml):
        tmpNode =docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")
        if len(tmpNode)>0:
            personalCreditInfo =  docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")[0]
            personalCreditInfoDf.loc[baseMateInfo, '数字解读'] = self.getNodeData(personalCreditInfo,"NUMBERREAD")
            personalCreditInfoDf.loc[baseMateInfo, '相对位置'] = self.getNodeData(personalCreditInfo,"RELPOSITION")
            personalCreditInfoDf.loc[baseMateInfo, '说明'] =  self.getNodeData(personalCreditInfo,"SCOREEXPLAINNUM")


    # 被追偿信息12
    def personalCreditInfo(self,docXml):
        tmpNode =docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")
        if len(tmpNode)>0:
            personalCreditInfo =  docXml.documentElement.getElementsByTagName("GRADEINFOSMRY")[0]
            personalCreditInfoDf.loc[baseMateInfo, '数字解读'] = self.getNodeData(personalCreditInfo,"NUMBERREAD")
            personalCreditInfoDf.loc[baseMateInfo, '相对位置'] = self.getNodeData(personalCreditInfo,"RELPOSITION")
            personalCreditInfoDf.loc[baseMateInfo, '说明'] =  self.getNodeData(personalCreditInfo,"SCOREEXPLAINNUM")






















































    #欠税记录
    def parseTaxArrears(self,docXml,reportTime):
        tax = docXml.documentElement.getElementsByTagName("OWINGTAXES")
        # for item in civil:
        #     self.saveNode(item)
        if len(tax)>0:
            taxArrearsDf.loc[taxArrearsIndex, '立案法院'] = self.getNodeData(tax[0],"COURT")



    # 民事判决记录
    def parseCivilJudgment(self,docXml):
        civil = docXml.documentElement.getElementsByTagName("CIVIL")
        # for item in civil:
        #     self.saveNode(item)
        if len(civil)>0:
            civilJudgmentDf.loc[civilJudgmentIndex, '立案法院'] = self.getNodeData(civil[0],"COURT")
            civilJudgmentDf.loc[civilJudgmentIndex, '案由'] = self.getNodeData(civil[0],"CAUSEOFACTION")
            civilJudgmentDf.loc[civilJudgmentIndex, '立案日期'] = self.getNodeData(civil[0],"FILINGDATE")
            civilJudgmentDf.loc[civilJudgmentIndex, '结案方式代码'] = self.getNodeData(civil[0],"CLOSEMCODE")
            civilJudgmentDf.loc[civilJudgmentIndex, '结案方式描述'] = self.getNodeData(civil[0],"CLOSEMDESC")
            civilJudgmentDf.loc[civilJudgmentIndex, '判决/调解结果'] = self.getNodeData(civil[0],"JUDRESULT")
            civilJudgmentDf.loc[civilJudgmentIndex, '判决/调解生效日期'] = self.getNodeData(civil[0],"JUDDATE")
            civilJudgmentDf.loc[civilJudgmentIndex, '诉讼标的'] = self.getNodeData(civil[0],"OBJECTOFACTION")
            civilJudgmentDf.loc[civilJudgmentIndex, '诉讼标的金额'] = self.getNodeData(civil[0],"OBJOFACTMONEY")
        #还有近24个月的属性值