python微信好友数据分析详解_Python

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：

python微信好友数据分析详解

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。

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									#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.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()

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。

原文链接：https://blog.csdn.net/zenobia119/article/details/80990970

python微信好友数据分析详解

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