【大数据基础】基于信用卡逾期数据的Spark数据处理与分析

https://dblab.xmu.edu.cn/blog/2707/

实验过程

数据预处理

本次实验数据集来自和鲸社区的信用卡评分模型构建数据，以数据集cs-training.csv为分析主体，其中共有15万条记录，11列属性。
每个数据包含以下字段：
字段名称字段含义例子
（1）SeriousDlqin2yrs 是否逾期 0,1
（2）RevolvingUtilizationOfUnsecuredLines 信用卡和个人信贷额度的总余额 0.766126609
（3）Age 年龄 45,20,30
（4）NumberOfTime30-59DaysPastDueNotWorse 借款人逾期30-59天的次数 0,2,3
（5）DebtRatio 负债比率 0.802982129
（6）MonthlyIncome 月收入 9120,3000
（7）NumberOfOpenCreditLinesAndLoans 未偿还贷款数量 ,0,4,13
（8）NumberOfTimes90DaysLate 借款人逾期90天以上的次数 0,1,3
（9）NumberRealEstateLoansOrLines 房地产贷款的数量 3,6
（10）NumberOfTime60-89DaysPastDueNotWorse 借款人逾期60-89天的次数 0,3
（11）NumberOfDependents 家庭中的家属人数 0,1,3

本次实验采用pandas库对数据进行预处理。在实验中，不对信用卡和个人信贷额度的总余额、负债比率、未偿还贷款数量、逾期90天以上的次数这4个属性进行处理分析。
具体处理步骤如下：
（1）读取数据
（2）查看数据是否具有重复值，去除重复值
（3）查看各字段缺失率，缺失值以均值填充
（4）选取要研究的属性，删除不研究的属性
（5）保存文件到本地
使用代码文件data_preprocessing.py对数据预处理，运行data_preprocessing.py文件的步骤如下：

import pandas as pd# 读取数据
df = pd.read_csv("~/Desktop/cs-training.csv")# 去除重复值
df.duplicated()
df.drop_duplicates()# 查看各字段缺失率
df.info()
# 缺失值按均值填充
for col in list(df.columns[df.isnull().sum() > 0]):mean_val = df[col].mean()df[col].fillna(mean_val, inplace=True)# 删除不分析的列
columns = ["RevolvingUtilizationOfUnsecuredLines","DebtRatio","NumberOfOpenCreditLinesAndLoans","NumberOfTimes90DaysLate"]
df.drop(columns,axis=1,inplace=True)# 保存到本地
df.to_csv("~/OverDue/data.csv")

将文件上传至HDFS文件系统

# 启动Hadoop
cd /usr/local/hadoop
./sbin/start-dfs.sh
# 在HDFS文件系统中创建/OverDue目录
./bin/hdfs dfs -mkdir /data
# 上传文件到HDFS文件系统中
./bin/hdfs dfs -put ~/OverDue/data.csv /OverDue/data.csv

使用Spark对数据处理分析

我们将采用Python编程语言和Spark大数据框架对数据集“data.csv”进行处理分析，具体步骤如下：
（1）读取HDFS文件系统中的数据文件，生成DataFrame
（2）修改列名
（3）本次信用卡逾期的总体统计
（4）年龄与本次信用卡逾期的结合统计
（5）两次逾期记录与本次信用卡逾期的结合统计
（6）房产抵押数量与本次信用卡逾期的结合统计
（7）家属人数与本次信用卡逾期的结合统计
（8）月收入与本次信用卡逾期的结合统计
（9）将统计数据返回给数据可视化文件data_web.py

./bin/hdfs dfs -put ~/OverDue/data1.csv /user/hadoop

代码文件data_analysis.py的内容如下：

from pyspark.sql import SparkSession
from pyspark import SparkContext,SparkConf
from pyspark.sql import Row
from pyspark.sql.types import *
from pyspark.sql import functionsdef analyse(filename):# 读取数据spark = SparkSession.builder.config(conf = SparkConf()).getOrCreate()df = spark.read.format("csv").option("header","true").load(filename)# 修改列名df = df.withColumnRenamed('SeriousDlqin2yrs','y')df = df.withColumnRenamed('NumberOfTime30-59DaysPastDueNotWorse','30-59days')df = df.withColumnRenamed('NumberOfTime60-89DaysPastDueNotWorse','60-89days')df = df.withColumnRenamed('NumberRealEstateLoansOrLines','RealEstateLoans')df = df.withColumnRenamed('NumberOfDependents','families')# 返回data_web.py的数据列表all_list = []# 本次信用卡逾期分析# 共有逾期10026人，139974没有逾期，总人数150000total_y = []for i in range(2):total_y.append(df.filter(df['y'] == i).count())all_list.append(total_y)# 年龄分析df_age = df.select(df['age'],df['y'])agenum = []bin = [0,30,45,60,75,100]# 统计各个年龄段的人口for i in range(5):agenum.append(df_age.filter(df['age'].between(bin[i],bin[i+1])).count())all_list.append(agenum)# 统计各个年龄段逾期与不逾期的数量age_y = []for i in range(5):y0 = df_age.filter(df['age'].between(bin[i],bin[i+1])).\filter(df['y']=='0').count()y1 = df_age.filter(df['age'].between(bin[i],bin[i+1])).\filter(df['y']=='1').count()age_y.append([y0,y1])all_list.append(age_y)# 有逾期记录的人的本次信用卡逾期数量df_pastDue = df.select(df['30-59days'],df['60-89days'],df['y'])# 30-59有23982人，4985逾期，18997不逾期numofpastdue = []numofpastdue.append(df_pastDue.filter(df_pastDue['30-59days'] > 0).count())y_numofpast1 = []for i in range(2):x = df_pastDue.filter(df_pastDue['30-59days'] > 0).\filter(df_pastDue['y'] == i).count()y_numofpast1.append(x)# 60-89有7604人，2770逾期，4834不逾期numofpastdue.append(df_pastDue.filter(df_pastDue['60-89days'] > 0).count())y_numofpast2 = []for i in range(2):x = df_pastDue.filter(df_pastDue['60-89days'] > 0).\filter(df_pastDue['y'] == i).count()y_numofpast2.append(x)# 两个记录都有的人有4393人，逾期1907，不逾期2486numofpastdue.append(df_pastDue.filter(df_pastDue['30-59days'] > 0).filter(df_pastDue['60-89days'] > 0).count())y_numofpast3 = []for i in range(2):x = df_pastDue.filter(df_pastDue['30-59days'] > 0).\filter(df_pastDue['60-89days'] > 0).filter(df_pastDue['y'] == i).count()y_numofpast3.append(x)all_list.append(numofpastdue)all_list.append(y_numofpast1)all_list.append(y_numofpast2)all_list.append(y_numofpast3)# 房产抵押数量分析df_Loans = df.select(df['RealEstateLoans'],df['y'])# 有无抵押房产人数情况numofrealandnoreal = []numofrealandnoreal.append(df_Loans.filter(df_Loans['RealEstateLoans']==0).count())numofrealandnoreal.append(df_Loans.filter(df_Loans['RealEstateLoans']>0).count())all_list.append(numofrealandnoreal)## 房产无抵押共有56188人，逾期4672人，没逾期51516人norealnum = []for i in range(2):x = df_Loans.filter(df_Loans['RealEstateLoans']==0).\filter(df_Loans['y'] == i).count()norealnum.append(x)all_list.append(norealnum)# 房产抵押共有93812人，逾期5354人，不逾期88458人realnum = []for i in range(2):x = df_Loans.filter(df_Loans['RealEstateLoans']>0).\filter(df_Loans['y'] == i).count()realnum.append(x)all_list.append(realnum)# 家属人数分析df_families = df.select(df['families'],df['y'])# 有无家属人数统计nofamiliesAndfamilies = []nofamiliesAndfamilies.append(df_families.filter(df_families['families']>0).count())nofamiliesAndfamilies.append(df_families.filter(df_families['families']==0).count())all_list.append(nofamiliesAndfamilies)# 有家属59174人，逾期4752人，没逾期54422人y_families = []y_families.append(df_families.filter(df_families['families']>0).filter(df_families['y']==0).count())y_families.append(df_families.filter(df_families['families']>0).filter(df_families['y']==1).count())all_list.append(y_families)# 没家属90826人，逾期5274人，没逾期85552人y_nofamilies = []y_nofamilies.append(df_families.filter(df_families['families']==0).filter(df_families['y']==0).count())y_nofamilies.append(df_families.filter(df_families['families']==0).filter(df_families['y']==1).count())all_list.append(y_nofamilies)# 月收入分析df_income = df.select(df['MonthlyIncome'],df['y'])# 获取平均值，其中先返回Row对象，再获取其中均值mean_income = df_income.agg(functions.avg(df_income['MonthlyIncome'])).head()[0]# 收入分布，105854人没超过均值6670，44146人超过均值6670numofMeanincome = []numofMeanincome.append(df_income.filter(df['MonthlyIncome'] < mean_income).count())numofMeanincome.append(df_income.filter(df['MonthlyIncome'] > mean_income).count())all_list.append(numofMeanincome)# 未超过均值的逾期情况分析,97977人没逾期，7877人逾期y_NoMeanIncome = []y_NoMeanIncome.append(df_income.filter(df['MonthlyIncome'] < mean_income).filter(df['y']==0).count())y_NoMeanIncome.append(df_income.filter(df['MonthlyIncome'] < mean_income).filter(df['y']==1).count())all_list.append(y_NoMeanIncome)# 超过均值的逾期情况分析，41997人没逾期，2149人逾期y_MeanIncome = []y_MeanIncome.append(df_income.filter(df['MonthlyIncome'] > mean_income).filter(df['y']==0).count())y_MeanIncome.append(df_income.filter(df['MonthlyIncome'] > mean_income).filter(df['y']==1).count())all_list.append(y_MeanIncome)# 数据可视化data_web.pyreturn all_list

数据可视化

选择使用python第三方库pyecharts作为可视化工具，其中pyecharts版本为1.7.0。采用其中的柱状图和饼图来详细展现分析结果。
代码文件data_web.py的内容如下：

from pyecharts.charts import Bar
from pyecharts.charts import Pie
from pyecharts.charts import Page
from pyecharts import options as opts
import data_analysis
# --------总体逾期人数情况--------------
def draw_total(total_list):attr = ["未逾期人数", "逾期人数"]pie = (Pie().add("总体逾期人数", [list(z) for z in zip(attr,total_list)]).set_global_opts(title_opts=opts.TitleOpts(title="总体逾期人数分布")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))return pie
# --------年龄与逾期人数情况--------------
def draw_age(age_list,y_ageList):total_pie = draw_total(all_list[0])attr = ["0-30", "30-45", "45-60", "60-75", "75-100"]y0_agenum = []y1_agenum = []for i in range(5):y0_agenum.append(y_ageList[i][0])y1_agenum.append(y_ageList[i][1])bar = (Bar().add_xaxis(attr).add_yaxis("人数分布", age_list).add_yaxis("未逾期人数分布", y0_agenum).add_yaxis("逾期人数分布", y1_agenum).set_global_opts(title_opts=opts.TitleOpts(title="各年龄段逾期情况")))attr = ["未逾期","逾期"]pie1 = (Pie().add("0-30年龄段", [list(z) for z in zip(attr,y_ageList[0])]).set_global_opts(title_opts=opts.TitleOpts(title="0-30年龄段逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie2 = (Pie().add("30-45年龄段", [list(z) for z in zip(attr,y_ageList[1])]).set_global_opts(title_opts=opts.TitleOpts(title="30-45年龄段逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie3 = (Pie().add("45-60年龄段", [list(z) for z in zip(attr,y_ageList[2])]).set_global_opts(title_opts=opts.TitleOpts(title="45-60年龄段逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie4 = (Pie().add("60-75年龄段", [list(z) for z in zip(attr,y_ageList[3])]).set_global_opts(title_opts=opts.TitleOpts(title="60-75年龄段逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie5 = (Pie().add("75-100年龄段", [list(z) for z in zip(attr,y_ageList[4])]).set_global_opts(title_opts=opts.TitleOpts(title="75-100年龄段逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))page = Page()page.add(bar)page.add(total_pie)page.add(pie1)page.add(pie2)page.add(pie3)page.add(pie4)page.add(pie5)page.render('age_OverDue.html')# --------逾期记录与逾期人数情况--------------
def draw_pastdue(numofpastdue,pastdue1num,pastdue2num,pastdue12num):total_pie = draw_total(all_list[0])attr = ["有30-59days逾期记录的人数", "有60-89days逾期记录的人数", "有长短期逾期记录的人数"]bar = (Bar().add_xaxis(attr).add_yaxis("人数", numofpastdue).set_global_opts(title_opts=opts.TitleOpts(title="有逾期记录的人数")))attr = ["未逾期","逾期"]pie1 = (Pie().add("有短期逾期记录的人的逾期情况", [list(z) for z in zip(attr,pastdue1num)]).set_global_opts(title_opts=opts.TitleOpts(title="有短期逾期记录的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie2 = (Pie().add("有长期逾期记录的人的逾期情况", [list(z) for z in zip(attr,pastdue2num)]).set_global_opts(title_opts=opts.TitleOpts(title="有长期逾期记录的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie3 = (Pie().add("长短期逾期记录都有的人的逾期情况", [list(z) for z in zip(attr,pastdue12num)]).set_global_opts(title_opts=opts.TitleOpts(title="长短期逾期记录都有的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))page = Page()page.add(bar)page.add(total_pie)page.add(pie1)page.add(pie2)page.add(pie3)page.render('pastDue_OverDue.html')
# --------房产抵押与逾期人数情况--------------
def draw_realestateLoans(numofrealornoreal,y_norealnum,y_realnum):total_pie = draw_total(all_list[0])attr = ["无房产抵押人数", "有房产抵押人数"]bar = (Bar().add_xaxis(attr).add_yaxis("人数", numofrealornoreal).set_global_opts(title_opts=opts.TitleOpts(title="房产抵押人数分布")))attr = ["未逾期","逾期"]pie1 = (Pie().add("无房产抵押的人的逾期情况", [list(z) for z in zip(attr,y_norealnum)]).set_global_opts(title_opts=opts.TitleOpts(title="无房产抵押的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie2 = (Pie().add("有房产抵押的人的逾期情况", [list(z) for z in zip(attr,y_realnum)]).set_global_opts(title_opts=opts.TitleOpts(title="有房产抵押的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))page = Page()page.add(bar)page.add(total_pie)page.add(pie1)page.add(pie2)page.render('realestateLoans_OverDue.html')# --------家属人数与逾期人数情况--------------
def draw_families(nofamiliesAndfamilies,y_families,y_nofamilies):total_pie = draw_total(all_list[0])attr = ["有家属人数", "无家属人数"]bar = (Bar().add_xaxis(attr).add_yaxis("人数", nofamiliesAndfamilies).set_global_opts(title_opts=opts.TitleOpts(title="有无家属人数分布")))attr = ["未逾期","逾期"]pie1 = (Pie().add("无家属的人的逾期情况", [list(z) for z in zip(attr,y_nofamilies)]).set_global_opts(title_opts=opts.TitleOpts(title="无家属的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie2 = (Pie().add("有家属的人的逾期情况", [list(z) for z in zip(attr,y_families)]).set_global_opts(title_opts=opts.TitleOpts(title="有家属的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))page = Page()page.add(bar)page.add(total_pie)page.add(pie1)page.add(pie2)page.render('families_OverDue.html')
# --------月收入与逾期人数情况--------------
def draw_income(numofMeanincome,y_NoMeanIncome,y_MeanIncome):total_pie = draw_total(all_list[0])attr = ["未超过均值收入人数", "超过均值收入人数"]bar = (Bar().add_xaxis(attr).add_yaxis("人数", numofMeanincome).set_global_opts(title_opts=opts.TitleOpts(title="有无超过均值收入人数分布")))attr = ["未逾期","逾期"]pie1 = (Pie().add("未超过均值收入的人的逾期情况", [list(z) for z in zip(attr,y_NoMeanIncome)]).set_global_opts(title_opts=opts.TitleOpts(title="未超过均值收入的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie2 = (Pie().add("超过均值收入的人的逾期情况", [list(z) for z in zip(attr,y_MeanIncome)]).set_global_opts(title_opts=opts.TitleOpts(title="超过均值收入的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))page = Page()page.add(bar)page.add(total_pie)page.add(pie1)page.add(pie2)page.render('meanIncome_OverDue.html')if __name__ == '__main__':print("开始总程序")Filename = "/OverDue/data.csv"all_list = data_analysis.analyse(Filename) # 年龄与是否逾期情况draw_age(all_list[1],all_list[2])# 有无逾期记录与是否逾期情况draw_pastdue(all_list[3],all_list[4],all_list[5],all_list[6])# 房产抵押数量与是否逾期情况draw_realestateLoans(all_list[7],all_list[8],all_list[9])# 家属人数与是否逾期情况draw_families(all_list[10],all_list[11],all_list[12])# 月收入与是否逾期情况draw_income(all_list[13],all_list[14],all_list[15])print("结束总程序")

完整代码

from pyspark.sql import SparkSession
from pyspark import SparkContext,SparkConf
from pyspark.sql import Row
from pyspark.sql.types import *
from pyspark.sql import functionsdef analyse(filename):# 读取数据spark = SparkSession.builder.config(conf = SparkConf()).getOrCreate()df = spark.read.format("csv").option("header","true").load(filename)# 修改列名df = df.withColumnRenamed('SeriousDlqin2yrs','y')df = df.withColumnRenamed('NumberOfTime30-59DaysPastDueNotWorse','30-59days')df = df.withColumnRenamed('NumberOfTime60-89DaysPastDueNotWorse','60-89days')df = df.withColumnRenamed('NumberRealEstateLoansOrLines','RealEstateLoans')df = df.withColumnRenamed('NumberOfDependents','families')# 返回data_web.py的数据列表all_list = []# 本次信用卡逾期分析# 共有逾期10026人，139974没有逾期，总人数150000total_y = []for i in range(2):total_y.append(df.filter(df['y'] == i).count())all_list.append(total_y)# 年龄分析df_age = df.select(df['age'],df['y'])agenum = []bin = [0,30,45,60,75,100]# 统计各个年龄段的人口for i in range(5):agenum.append(df_age.filter(df['age'].between(bin[i],bin[i+1])).count())all_list.append(agenum)# 统计各个年龄段逾期与不逾期的数量age_y = []for i in range(5):y0 = df_age.filter(df['age'].between(bin[i],bin[i+1])).\filter(df['y']=='0').count()y1 = df_age.filter(df['age'].between(bin[i],bin[i+1])).\filter(df['y']=='1').count()age_y.append([y0,y1])all_list.append(age_y)# 有逾期记录的人的本次信用卡逾期数量df_pastDue = df.select(df['30-59days'],df['60-89days'],df['y'])# 30-59有23982人，4985逾期，18997不逾期numofpastdue = []numofpastdue.append(df_pastDue.filter(df_pastDue['30-59days'] > 0).count())y_numofpast1 = []for i in range(2):x = df_pastDue.filter(df_pastDue['30-59days'] > 0).\filter(df_pastDue['y'] == i).count()y_numofpast1.append(x)# 60-89有7604人，2770逾期，4834不逾期numofpastdue.append(df_pastDue.filter(df_pastDue['60-89days'] > 0).count())y_numofpast2 = []for i in range(2):x = df_pastDue.filter(df_pastDue['60-89days'] > 0).\filter(df_pastDue['y'] == i).count()y_numofpast2.append(x)# 两个记录都有的人有4393人，逾期1907，不逾期2486numofpastdue.append(df_pastDue.filter(df_pastDue['30-59days'] > 0).filter(df_pastDue['60-89days'] > 0).count())y_numofpast3 = []for i in range(2):x = df_pastDue.filter(df_pastDue['30-59days'] > 0).\filter(df_pastDue['60-89days'] > 0).filter(df_pastDue['y'] == i).count()y_numofpast3.append(x)all_list.append(numofpastdue)all_list.append(y_numofpast1)all_list.append(y_numofpast2)all_list.append(y_numofpast3)# 房产抵押数量分析df_Loans = df.select(df['RealEstateLoans'],df['y'])# 有无抵押房产人数情况numofrealandnoreal = []numofrealandnoreal.append(df_Loans.filter(df_Loans['RealEstateLoans']==0).count())numofrealandnoreal.append(df_Loans.filter(df_Loans['RealEstateLoans']>0).count())all_list.append(numofrealandnoreal)## 房产无抵押共有56188人，逾期4672人，没逾期51516人norealnum = []for i in range(2):x = df_Loans.filter(df_Loans['RealEstateLoans']==0).\filter(df_Loans['y'] == i).count()norealnum.append(x)all_list.append(norealnum)# 房产抵押共有93812人，逾期5354人，不逾期88458人realnum = []for i in range(2):x = df_Loans.filter(df_Loans['RealEstateLoans']>0).\filter(df_Loans['y'] == i).count()realnum.append(x)all_list.append(realnum)# 家属人数分析df_families = df.select(df['families'],df['y'])# 有无家属人数统计nofamiliesAndfamilies = []nofamiliesAndfamilies.append(df_families.filter(df_families['families']>0).count())nofamiliesAndfamilies.append(df_families.filter(df_families['families']==0).count())all_list.append(nofamiliesAndfamilies)# 有家属59174人，逾期4752人，没逾期54422人y_families = []y_families.append(df_families.filter(df_families['families']>0).filter(df_families['y']==0).count())y_families.append(df_families.filter(df_families['families']>0).filter(df_families['y']==1).count())all_list.append(y_families)# 没家属90826人，逾期5274人，没逾期85552人y_nofamilies = []y_nofamilies.append(df_families.filter(df_families['families']==0).filter(df_families['y']==0).count())y_nofamilies.append(df_families.filter(df_families['families']==0).filter(df_families['y']==1).count())all_list.append(y_nofamilies)# 月收入分析df_income = df.select(df['MonthlyIncome'],df['y'])# 获取平均值，其中先返回Row对象，再获取其中均值mean_income = df_income.agg(functions.avg(df_income['MonthlyIncome'])).head()[0]# 收入分布，105854人没超过均值6670，44146人超过均值6670numofMeanincome = []numofMeanincome.append(df_income.filter(df['MonthlyIncome'] < mean_income).count())numofMeanincome.append(df_income.filter(df['MonthlyIncome'] > mean_income).count())all_list.append(numofMeanincome)# 未超过均值的逾期情况分析,97977人没逾期，7877人逾期y_NoMeanIncome = []y_NoMeanIncome.append(df_income.filter(df['MonthlyIncome'] < mean_income).filter(df['y']==0).count())y_NoMeanIncome.append(df_income.filter(df['MonthlyIncome'] < mean_income).filter(df['y']==1).count())all_list.append(y_NoMeanIncome)# 超过均值的逾期情况分析，41997人没逾期，2149人逾期y_MeanIncome = []y_MeanIncome.append(df_income.filter(df['MonthlyIncome'] > mean_income).filter(df['y']==0).count())y_MeanIncome.append(df_income.filter(df['MonthlyIncome'] > mean_income).filter(df['y']==1).count())all_list.append(y_MeanIncome)# 数据可视化data_web.pyreturn all_listfrom pyecharts.charts import Bar
from pyecharts.charts import Pie
from pyecharts.charts import Page
from pyecharts import options as opts# --------总体逾期人数情况--------------
def draw_total(total_list):attr = ["未逾期人数", "逾期人数"]pie = (Pie().add("总体逾期人数", [list(z) for z in zip(attr,total_list)]).set_global_opts(title_opts=opts.TitleOpts(title="总体逾期人数分布")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))return pie
# --------年龄与逾期人数情况--------------
def draw_age(age_list,y_ageList):total_pie = draw_total(all_list[0])attr = ["0-30", "30-45", "45-60", "60-75", "75-100"]y0_agenum = []y1_agenum = []for i in range(5):y0_agenum.append(y_ageList[i][0])y1_agenum.append(y_ageList[i][1])bar = (Bar().add_xaxis(attr).add_yaxis("人数分布", age_list).add_yaxis("未逾期人数分布", y0_agenum).add_yaxis("逾期人数分布", y1_agenum).set_global_opts(title_opts=opts.TitleOpts(title="各年龄段逾期情况")))attr = ["未逾期","逾期"]pie1 = (Pie().add("0-30年龄段", [list(z) for z in zip(attr,y_ageList[0])]).set_global_opts(title_opts=opts.TitleOpts(title="0-30年龄段逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie2 = (Pie().add("30-45年龄段", [list(z) for z in zip(attr,y_ageList[1])]).set_global_opts(title_opts=opts.TitleOpts(title="30-45年龄段逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie3 = (Pie().add("45-60年龄段", [list(z) for z in zip(attr,y_ageList[2])]).set_global_opts(title_opts=opts.TitleOpts(title="45-60年龄段逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie4 = (Pie().add("60-75年龄段", [list(z) for z in zip(attr,y_ageList[3])]).set_global_opts(title_opts=opts.TitleOpts(title="60-75年龄段逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie5 = (Pie().add("75-100年龄段", [list(z) for z in zip(attr,y_ageList[4])]).set_global_opts(title_opts=opts.TitleOpts(title="75-100年龄段逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))page = Page()page.add(bar)page.add(total_pie)page.add(pie1)page.add(pie2)page.add(pie3)page.add(pie4)page.add(pie5)page.render('age_OverDue.html')# --------逾期记录与逾期人数情况--------------
def draw_pastdue(numofpastdue,pastdue1num,pastdue2num,pastdue12num):total_pie = draw_total(all_list[0])attr = ["有30-59days逾期记录的人数", "有60-89days逾期记录的人数", "有长短期逾期记录的人数"]bar = (Bar().add_xaxis(attr).add_yaxis("人数", numofpastdue).set_global_opts(title_opts=opts.TitleOpts(title="有逾期记录的人数")))attr = ["未逾期","逾期"]pie1 = (Pie().add("有短期逾期记录的人的逾期情况", [list(z) for z in zip(attr,pastdue1num)]).set_global_opts(title_opts=opts.TitleOpts(title="有短期逾期记录的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie2 = (Pie().add("有长期逾期记录的人的逾期情况", [list(z) for z in zip(attr,pastdue2num)]).set_global_opts(title_opts=opts.TitleOpts(title="有长期逾期记录的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie3 = (Pie().add("长短期逾期记录都有的人的逾期情况", [list(z) for z in zip(attr,pastdue12num)]).set_global_opts(title_opts=opts.TitleOpts(title="长短期逾期记录都有的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))page = Page()page.add(bar)page.add(total_pie)page.add(pie1)page.add(pie2)page.add(pie3)page.render('pastDue_OverDue.html')
# --------房产抵押与逾期人数情况--------------
def draw_realestateLoans(numofrealornoreal,y_norealnum,y_realnum):total_pie = draw_total(all_list[0])attr = ["无房产抵押人数", "有房产抵押人数"]bar = (Bar().add_xaxis(attr).add_yaxis("人数", numofrealornoreal).set_global_opts(title_opts=opts.TitleOpts(title="房产抵押人数分布")))attr = ["未逾期","逾期"]pie1 = (Pie().add("无房产抵押的人的逾期情况", [list(z) for z in zip(attr,y_norealnum)]).set_global_opts(title_opts=opts.TitleOpts(title="无房产抵押的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie2 = (Pie().add("有房产抵押的人的逾期情况", [list(z) for z in zip(attr,y_realnum)]).set_global_opts(title_opts=opts.TitleOpts(title="有房产抵押的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))page = Page()page.add(bar)page.add(total_pie)page.add(pie1)page.add(pie2)page.render('realestateLoans_OverDue.html')# --------家属人数与逾期人数情况--------------
def draw_families(nofamiliesAndfamilies,y_families,y_nofamilies):total_pie = draw_total(all_list[0])attr = ["有家属人数", "无家属人数"]bar = (Bar().add_xaxis(attr).add_yaxis("人数", nofamiliesAndfamilies).set_global_opts(title_opts=opts.TitleOpts(title="有无家属人数分布")))attr = ["未逾期","逾期"]pie1 = (Pie().add("无家属的人的逾期情况", [list(z) for z in zip(attr,y_nofamilies)]).set_global_opts(title_opts=opts.TitleOpts(title="无家属的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie2 = (Pie().add("有家属的人的逾期情况", [list(z) for z in zip(attr,y_families)]).set_global_opts(title_opts=opts.TitleOpts(title="有家属的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))page = Page()page.add(bar)page.add(total_pie)page.add(pie1)page.add(pie2)page.render('families_OverDue.html')
# --------月收入与逾期人数情况--------------
def draw_income(numofMeanincome,y_NoMeanIncome,y_MeanIncome):total_pie = draw_total(all_list[0])attr = ["未超过均值收入人数", "超过均值收入人数"]bar = (Bar().add_xaxis(attr).add_yaxis("人数", numofMeanincome).set_global_opts(title_opts=opts.TitleOpts(title="有无超过均值收入人数分布")))attr = ["未逾期","逾期"]pie1 = (Pie().add("未超过均值收入的人的逾期情况", [list(z) for z in zip(attr,y_NoMeanIncome)]).set_global_opts(title_opts=opts.TitleOpts(title="未超过均值收入的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))pie2 = (Pie().add("超过均值收入的人的逾期情况", [list(z) for z in zip(attr,y_MeanIncome)]).set_global_opts(title_opts=opts.TitleOpts(title="超过均值收入的人的逾期情况")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)")))page = Page()page.add(bar)page.add(total_pie)page.add(pie1)page.add(pie2)page.render('meanIncome_OverDue.html')if __name__ == '__main__':print("开始总程序")Filename = "hdfs://localhost:8020/user/hadoop/data1.csv"all_list = analyse(Filename) # 年龄与是否逾期情况draw_age(all_list[1],all_list[2])# 有无逾期记录与是否逾期情况draw_pastdue(all_list[3],all_list[4],all_list[5],all_list[6])# 房产抵押数量与是否逾期情况draw_realestateLoans(all_list[7],all_list[8],all_list[9])# 家属人数与是否逾期情况draw_families(all_list[10],all_list[11],all_list[12])# 月收入与是否逾期情况draw_income(all_list[13],all_list[14],all_list[15])print("结束总程序")

运行结果如下：（带overdue.html的文件）

数据可视化结果

# 进入OverDue目录
cd ~/OverDue
# 提交data_web.py文件到spark-submit
/usr/local/spark/bin/spark-submit --master local ~/OverDue/data_web.py

家属人数

逾期记录

房产抵押数量

月收入

### 总体

年龄