使用wxBot和Python做微信好友数据分析

为了拿pandas练手，做了这个东西，分享一下给大家，有需要的可以参考，不喜勿喷

1.使用wxBot获取到微信好友列表

2.下载好wxBot后根据文档提示运行起来，登录微信

3.在当前目录下 找到 temp/contact_list.json

4.在 json-csv网站 把Json文件转换成CSV文件

# -*- coding: utf-8 -*-
#导入库
import re
import pandas as pd
import matplotlib.pyplot as plt

import jieba.analyse
from snownlp import SnowNLP
from wordcloud import WordCloud

def readFile(filePath):
  return pd.read_csv(filePath)
 
if __name__ == "__main__":
  #Mac下设置Plot的中文显示，如果不设置，中文就显示成方块
  plt.rcParams['font.sans-serif']=['SimHei']
  df = readFile('./contact_list.csv')

  #处理空数据设置成默认数据
  df.Province= df.Province.fillna('未知')
  df.City= df.City.fillna('未知')

  showAreaBar(df,'Province','按省分布',5)
  showAreaBar(df,'City','按市分布',3)

  showGenderPie(df)

  analyseSignature(df['Signature'])
复制代码

---

展示地区分布的柱状图，按City分布的截图就不放上去了

def showAreaBar(datas, area, title, limit):
  plt.figure(figsize=(8,10))
  area_group = datas['PYQuanPin'].groupby(datas[area])
  name_list = []
  num_list = []
  less = 0
  lessLimit = limit
  for name, group in area_group:
    if group.size < lessLimit:
      less += group.size
    else:
      name_list.append(name)
      num_list.append(group.size)

  name_list.append('少于{}人'.format(lessLimit))
  num_list.append(less)

  # plt.subplot(221)
  plt.title(title)
  plt.barh(range(len(num_list)), num_list,color='rgb',tick_label=name_list) 
  #必须要先Save
  plt.savefig(area + ".jpg")
  plt.show()

  plt.title('我的微信好友地区分布')
  plt.pie(x=num_list, labels=name_list,autopct='%3.1f %%',
        shadow=False, labeldistance=1.2, startangle = 90,pctdistance = 0.6
        )
  plt.savefig(area + '_Pie.jpg')
  plt.show()
复制代码

---

好友中性别比例的饼状图

def showGenderPie(datas,):
  # print (df[df.Sex == 2])
  males = datas[datas.Sex == 1]
  females = datas[datas.Sex == 2]
  unknowns = datas[datas.Sex == 0]

  labels = ['男', '女', '未知']
  fracs = [males.size, females.size,unknowns.size]
  explode = [0, 0,0.1] # 0.1 凸出这部分，

  plt.title('我的微信好友性别比例')
  plt.pie(x=fracs, labels=labels,explode=explode,autopct='%3.1f %%',
        shadow=False, labeldistance=1.1, startangle = 90,pctdistance = 0.6
        )
  plt.savefig('gender.jpg')
  plt.show()
复制代码

---

微信好友签名信息情感分析

def analyseSignature(signatureList):
    signatures = ''
    emotions = []
    for signature in signatureList:
        if(isinstance(signature,str) and signature != None):
            signature = signature.strip().replace('span', '').replace('class', '').replace('emoji', '')
            signature = re.sub(r'1f(\d.+)','',signature)
            if(len(signature)>0):
                nlp = SnowNLP(signature)
                emotions.append(nlp.sentiments)
                signatures += ' '.join(jieba.analyse.extract_tags(signature,5))
    with open('signatures.txt','wt',encoding='utf-8') as file:
         file.write(signatures)

    # Sinature WordCloud
    # back_coloring = np.array(Image.open('flower.jpg'))
    font_path="/System/Library/fonts/PingFang.ttc"
    wordcloud = WordCloud(
        font_path=font_path,
        background_color="white",
        max_words=1200,
        # mask=back_coloring, 
        max_font_size=75,
        random_state=45,
        width=960, 
        height=720, 
        margin=15
    )
    wordcloud.generate(signatures)
    plt.imshow(wordcloud)
    plt.axis("off")
    plt.show()
    wordcloud.to_file('signatures.jpg')

    # Signature Emotional Judgment
    count_good = len(list(filter(lambda x:x>0.66,emotions)))
    count_normal = len(list(filter(lambda x:x>=0.33 and x<=0.66,emotions)))
    count_bad = len(list(filter(lambda x:x<0.33,emotions)))
    labels = [u'负面消极',u'中性',u'正面积极']
    values = (count_bad,count_normal,count_good)
    plt.rcParams['font.sans-serif'] = ['simHei'] 
    plt.rcParams['axes.unicode_minus'] = False
    plt.xlabel(u'情感判断')
    plt.ylabel(u'频数')
    plt.xticks(range(3),labels)
    plt.legend(loc='upper right',)
    plt.bar(range(3), values, color = 'rgb')
    plt.title(u'微信好友签名信息情感分析')
    plt.show()
复制代码

转载于:https://juejin.im/post/5cdccf135188256964773d99