目錄

一、寫在前面

二、關于代碼

三、一些詞云圖

3.1 篩選條件：無

3.2 篩選條件：性別-男

3.3 篩選條件：性別-女

3.4 篩選條件：教程程度-研究生及其以上

3.5 篩選條件：教育程度-本科?

3.6 篩選條件：籍貫-福建

3.7 篩選條件：籍貫-廣東

3.8 篩選條件：借款理由-含“蘋果”兩字

?四、代碼

4.1 導入庫

4.2 導入數據

4.3 設置停用詞

4.4 生成詞云圖代碼

五、寫在最后

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一、寫在前面

關于人人貸的歷史博文：人人貸散標爬蟲實例_小zhan柯基-CSDN博客_人人貸爬蟲、人人貸散標爬蟲實例進階-使用異步io_小zhan柯基-CSDN博客、用python處理28萬條人人貸數據，告訴你最詳細的借款人結構分布情況_小zhan柯基-CSDN博客

上篇關于人人貸的博文中提到3點，一是可以繼續挖掘數據，比如分析各個年齡段的學歷分布什么的；二是可以利用人人貸的數據訓練信用評價的神經網絡模型；三是可以利用借款理由這一列數據生成詞云圖。

由于最近忙著進行區塊鏈與供應鏈金融的相關研究，所以這次就先挑軟柿子捏吧，生成個詞云圖還是特別快的。

最后，有需要人人貸貸款數據的私信我！

二、關于代碼

生成詞語圖的方法就不贅述啦哈，網上一搜教程一大堆，例如Python制作炫酷的詞云圖（包含停用詞、詞頻統計）！！！_gjgfjgy的博客-CSDN博客_停用詞分析、繪制詞云圖
EDG奪冠，用Python分析一波：粉絲都炸鍋了_數據分析與統計學之美-CSDN博客
這里提一點關于

pandas一個比較常用的用法：篩選包含某個關鍵詞的行/列！

首先數據如上圖，共含有284316條借款理由的數據，如果我要找出借款理由里含有“蘋果”兩個字的數據應該怎么做呢？

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

從上圖可以看出，借款買蘋果手機的數據只有646條，占比0.23%，看來買借款買蘋果手機的并不多哈哈哈哈。

三、一些詞云圖

3.1 篩選條件：無

3.2 篩選條件：性別-男

3.3 篩選條件：性別-女

3.4 篩選條件：教程程度-研究生及其以上

3.5 篩選條件：教育程度-本科?

3.6 篩選條件：籍貫-福建

3.7 篩選條件：籍貫-廣東

3.8 篩選條件：借款理由-含“蘋果”兩字

?四、代碼

4.1 導入庫

import numpy as np import matplotlib.pyplot as plt import pandas as pdimport matplotlib.ticker as ticker import mpl_toolkits.axisartist as AA from mpl_toolkits.axisartist.axislines import SubplotZero import pylabimport jieba from wordcloud import WordCloudpylab.mpl.rcParams['font.sans-serif'] = ['SimHei'] #顯示中文 plt.rcParams['axes.unicode_minus']=False #用于解決不能顯示負號的問題

4.2 導入數據

data = pd.read_csv("all.csv",encoding="gbk",header=None,parse_dates=True) data.columns = ["id","借款時間（月）","剩余還款時間（月）","借款金額","notPayInterest","productRepayType","貸款類型","利率","性別","籍貫","出生日期","教育程度","工作單位","行業","公司規模","職位","收入","車貸","汽車數量","婚姻狀況","房貸","房子數量","信用等級","none","none","none","借款理由"]conciseData = data[["id","借款時間（月）","剩余還款時間（月）","借款金額","貸款類型","利率","性別","籍貫","出生日期","教育程度","工作單位","行業","公司規模","職位","收入","車貸","汽車數量","婚姻狀況","房貸","房子數量","信用等級","借款理由"]] conciseData = conciseData.set_index("id") conciseData = conciseData.dropna(how="all")

4.3 設置停用詞

stopWords = ["人人","真實有效","同時","符合","借款人","提供","上述","考察","實地","已經","希望","大家","認證","審核","此次","公司","眾信","借款","謝謝","比較","第一次","壓力","貸","的","標準","方友","業","還款","收入","用于","信息","以上","問題","好","一下","通過","穩定","全國","企業","位于","該","為","自己","現居","工作","單位","但","高","一些","還清","行業","主要","從事","有","無","良好","貸款","累計","自","放心","家里","吱吱","為了","放款","多","在","年","所","抵押","無擔保","服務","本人","多多","小額貸款","想","與","借","給","建立""支持","至今","安信","良好","最","多","探索","大","小","證大速貸","成立","于","信用","成立","每月","流水","一家","因為","我","和","是","做","所以","迅速","以來","需""快速","簡便","可以","專門","資料","經","了","也","現在","由于","測試","需要","元","也","還","個","月","人","申請","等","能","了","及","沒有","現在","就","進行","都","各位","急急","每個","準備","有限公司","目前","保證","按時","因","可","持續","一個","上","到","萬","要","現","來","想","個人","左右","不","年底","能力",]

4.4 生成詞云圖代碼

由于28W條數據過多，此處采用步距為3對數據切片！

txt = ""for each in conciseData[conciseData["性別"]=="男"]["借款理由"][::3]:if isinstance(each,str):txt += each + " "words = jieba.cut(txt) #分詞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、寫在最后

眾生皆苦，不止你一個，放下即是自在。

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