python微信好友数据分析详解

Python  /  管理员 发布于 7年前   180

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()

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