Python数据分析之微信好友数据分析

效果：

直接上代码，建三个空文本文件stopwords.txt，newdit.txt、unionWords.txt，下载字体simhei.ttf或删除字体要求的代码，就可以直接运行。

 #wxfriends.py  2018-07-09
import itchat
import sys
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']#绘图时可以显示中文
plt.rcParams['axes.unicode_minus']=False#绘图时可以显示中文
import jieba
import jieba.posseg as pseg
from scipy.misc import imread
from wordcloud import WordCloud
from os import path
#解决编码问题
non_bmp_map = dict.fromkeys(range(0x10000, sys.maxunicode + 1), 0xfffd)#获取好友信息
def getFriends():friends = itchat.get_friends(update=True)[0:]flists = []for i in friends:fdict={}fdict['NickName']=i['NickName'].translate(non_bmp_map)if i['Sex'] == 1:fdict['Sex']='男'elif i['Sex'] == 2:fdict['Sex']='女'else:fdict['Sex']='雌雄同体'if i['Province'] == '':fdict['Province'] ='未知'else:fdict['Province']=i['Province']fdict['City']=i['City']fdict['Signature']=i['Signature']flists.append(fdict)return flists#将好友信息保存成CSV
def saveCSV(lists):df = pd.DataFrame(lists)try:df.to_csv("wxfriends.csv",index = True,encoding='gb18030')except Exception as ret:print(ret)return df#统计性别、省份字段    
def anysys(df):df_sex = pd.DataFrame(df['Sex'].value_counts())df_province = pd.DataFrame(df['Province'].value_counts()[:15])df_signature = pd.DataFrame(df['Signature'])return df_sex,df_province,df_signature#绘制柱状图，并保存   
def draw_chart(df_list,x_feature):try:x = list(df_list.index)ylist = df_list.valuesy = []for i in ylist :for j in i:y.append(j)plt.bar(x,y,label=x_feature)plt.legend()plt.savefig(x_feature)plt.close()except:print("绘图失败")#解析取个性签名构成列表     
def getSignList(signature):sig_list = []for i in signature.values:for j in i:sig_list.append(j.translate(non_bmp_map))return sig_list#分词处理，并根据需要填写停用词、自定义词、合并词替换
def segmentWords(txtlist):stop_words = set(line.strip() for line in open('stopwords.txt', encoding='utf-8'))newslist = []#新增自定义词jieba.load_userdict("newdit.txt")for subject in txtlist:if subject.isspace():continueword_list = pseg.cut(subject)for word, flag in word_list:if not word in stop_words and flag == 'n' or flag == 'eng' and word !='span' and word !='class':newslist.append(word)#合并指定的相似词for line in open('unionWords.txt', encoding='utf-8'):newline = line.encode('utf-8').decode('utf-8-sig')    #解决\ufeff问题unionlist = newline.split("*")for j in range(1,len(unionlist)):#wordDict[unionlist[0]] += wordDict.pop(unionlist[j],0)for index,value in enumerate(newslist):if value == unionlist[j]:newslist[index] = unionlist[0]  return newslist#高频词统计
def countWords(newslist):wordDict = {}for item in newslist:wordDict[item] = wordDict.get(item,0) + 1itemList = list(wordDict.items())itemList.sort(key=lambda x:x[1],reverse=True)        for i in range(100):word, count = itemList[i]print("{}:{}".format(word,count))#绘制词云
def drawPlant(newslist):d = path.dirname(__file__)mask_image = imread(path.join(d, "timg.png"))content = ' '.join(newslist)wordcloud = WordCloud(font_path='simhei.ttf', background_color="white",width=1300,height=620, max_words=200).generate(content)   #mask=mask_image,# Display the generated image:plt.imshow(wordcloud)plt.axis("off")wordcloud.to_file('wordcloud.jpg')plt.show()def main():#登陆微信itchat.auto_login()   # 登陆后不需要扫码   hotReload=Trueflists = getFriends()fdf = saveCSV(flists)df_sex,df_province,df_signature = anysys(fdf)draw_chart(df_sex,"性别")draw_chart(df_province,"省份")wordList = segmentWords(getSignList(df_signature))countWords(wordList)drawPlant(wordList)main()