Python实现微信好友的数据分析

 #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()