python微信好友数据分析详解

基于微信开放的个人号接口python库itchat，实现对微信好友的获取，并对省份、性别、微信签名做数据分析。

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 2018-07-09import itchatimport sysimport pandas as pdimport matplotlib.pyplot as pltplt.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 jiebaimport jieba.posseg as psegfrom scipy.misc import imreadfrom wordcloud import WordCloudfrom 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CSVdef 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.values    y = []    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():      continue    word_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) + 1  itemList = 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=True  flists = 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()