目录

一、写在前面

二、关于代码

三、一些词云图

3.1 筛选条件：无

3.2 筛选条件：性别-男

3.3 筛选条件：性别-女

3.4 筛选条件：教程程度-研究生及其以上

3.5 筛选条件：教育程度-本科 

3.6 筛选条件：籍贯-福建

3.7 筛选条件：籍贯-广东

3.8 筛选条件：借款理由-含“苹果”两字

 四、代码

4.1 导入库

4.2 导入数据

4.3 设置停用词

4.4 生成词云图代码

五、写在最后

一、写在前面

关于人人贷的历史博文：人人贷散标爬虫实例_小zhan柯基-CSDN博客_人人贷爬虫、人人贷散标爬虫实例进阶-使用异步io_小zhan柯基-CSDN博客、用python处理28万条人人贷数据，告诉你最详细的借款人结构分布情况_小zhan柯基-CSDN博客

上篇关于人人贷的博文中提到3点，一是可以继续挖掘数据，比如分析各个年龄段的学历分布什么的；二是可以利用人人贷的数据训练信用评价的神经网络模型；三是可以利用借款理由这一列数据生成词云图。

由于最近忙着进行区块链与供应链金融的相关研究，所以这次就先挑软柿子捏吧，生成个词云图还是特别快的。

最后，有需要人人贷贷款数据的私信我！

二、关于代码

生成词语图的方法就不赘述啦哈，网上一搜教程一大堆，例如Python制作炫酷的词云图（包含停用词、词频统计）！！！_gjgfjgy的博客-CSDN博客_停用词分析、绘制词云图
EDG夺冠，用Python分析一波：粉丝都炸锅了_数据分析与统计学之美-CSDN博客
这里提一点关于

pandas一个比较常用的用法：筛选包含某个关键词的行/列！

首先数据如上图，共含有284316条借款理由的数据，如果我要找出借款理由里含有“苹果”两个字的数据应该怎么做呢？

conciseData[conciseData["借款理由"].str.contains("苹果",na=False)]["借款理由"]

从上图可以看出，借款买苹果手机的数据只有646条，占比0.23%，看来买借款买苹果手机的并不多哈哈哈哈。

三、一些词云图

3.1 筛选条件：无

3.2 筛选条件：性别-男

3.3 筛选条件：性别-女

3.4 筛选条件：教程程度-研究生及其以上

3.5 筛选条件：教育程度-本科 

3.6 筛选条件：籍贯-福建

3.7 筛选条件：籍贯-广东

3.8 筛选条件：借款理由-含“苹果”两字

 四、代码

4.1 导入库

import numpy as npimport matplotlib.pyplot as pltimport pandas as pd     import matplotlib.ticker as tickerimport mpl_toolkits.axisartist as AAfrom mpl_toolkits.axisartist.axislines import SubplotZeroimport pylabimport jieba   from wordcloud import WordCloudpylab.mpl.rcParams['font.sans-serif'] = ['SimHei'] #显示中文plt.rcParams['axes.unicode_minus']=False  #用于解决不能显示负号的问题

4.2 导入数据

data = pd.read_csv("all.csv",encoding="gbk",header=None,parse_dates=True)data.columns = ["id","借款时间（月）","剩余还款时间（月）","借款金额","notPayInterest","productRepayType", "贷款类型","利率","性别","籍贯","出生日期","教育程度","工作单位","行业","公司规模","职位","收入", "车贷","汽车数量","婚姻状况","房贷","房子数量","信用等级","none","none","none","借款理由"]conciseData = data[["id","借款时间（月）","剩余还款时间（月）","借款金额","贷款类型","利率","性别","籍贯","出生日期","教育程度","工作单位","行业","公司规模","职位","收入", "车贷","汽车数量","婚姻状况","房贷","房子数量","信用等级","借款理由"]]conciseData = conciseData.set_index("id")conciseData = conciseData.dropna(how="all")

4.3 设置停用词

stopWords = ["人人","真实有效","同时","符合","借款人","提供","上述","考察","实地",    "已经","希望","大家","认证","审核","此次","公司","众信","借款","谢谢","比较","第一次","压力", "贷","的","标准","方友","业","还款","收入","用于","信息","以上","问题","好","一下","通过",     "稳定","全国","企业","位于","该","为","自己","现居","工作","单位","但","高","一些","还清",  "行业","主要","从事","有","无","良好","贷款","累计","自","放心","家里","吱吱","为了","放款",      "多","在","年","所","抵押","无担保","服务","本人","多多","小额贷款","想","与","借","给","建立"   "支持","至今","安信","良好","最","多","探索","大","小","证大速贷","成立","于","信用","成立","每月","流水","一家","因为","我","和","是","做","所以","迅速","以来","需"    "快速","简便","可以","专门","资料","经","了","也","现在","由于", "测试","需要","元","也","还","个","月","人","申请","等",     "能","了","及","没有","现在","就","进行","都","各位","急急",  "每个","准备","有限公司","目前","保证","按时","因","可","持续","一个",      "上","到","万","要","现","来","想","个人","左右","不","年底","能力",]

4.4 生成词云图代码

由于28W条数据过多，此处采用步距为3对数据切片！

txt = ""for each in conciseData[conciseData["性别"]=="男"]["借款理由"][::3]:    if isinstance(each,str):     txt += each + "  "     words = jieba.cut(txt) #分词result = ""for each in words:    if each not in stopWords: result += each + " " wordshow = WordCloud(background_color='black',width=800,height=800,max_words=800,max_font_size=100,font_path="msyh.ttc",    ).generate(result)wordshow.to_file('男.png')

五、写在最后

众生皆苦，不止你一个，放下即是自在。