from wxpy import *
#初始化机器人，选择缓存模式（扫码）登录
bot=Bot(cache_path=True)
#获取我的所有微信好友信息
friend_all=bot.friends()
print(friend_all[0].raw)#friend_all[0]是我的微信昵称，.raw 则是获取我的全部信息
############运行后会自动弹出二维码页面，手机扫描登陆即可###########

lis=[]
for a_friend in friend_all:
    NickName=a_friend.raw.get(\'NickName\',None)
    #Sex=afriend.raw.get(\'Sex\',None)
    Sex={1:"男",2:"女",0:"其他"}.get(a_friend.raw.get(\'Sex\',None),None)
    City=a_friend.raw.get(\'City\',None)
    Province=a_friend.raw.get("Province",None)
    Sigenature=a_friend.raw.get("Signature",None)
    HeadImgFlagUrl=a_friend.raw.get("HeadImgFlagUrl",None)
    HeadImgFlag=a_friend.raw.get("HeadImgFlag",None)
    list_0=[NickName,Sex,City,Province,Sigenature,HeadImgFlagUrl,HeadImgFlag]
    lis.append(list_0)

def lis2e07(filename,lis):
    import openpyxl
    wb = openpyxl.Workbook()
    sheet = wb.active
    sheet.title = \'list2excel07\'
    file_name = filename +\'.xlsx\'
    for i in range(0, len(lis)):
        for j in range(0, len(lis[i])):
                sheet.cell(row=i+1, column=j+1, value=str(lis[i][j]))
    wb.save(file_name)
    print("写入数据成功！")
lis2e07(\'list3\',lis)

#######################粗略获取好友信息###############################
Friends=bot.friends()
data=Friends.stats_text(total=True,sex=True,top_provinces=30,top_cities=300)
print(data)
######可以从存储在本地的 excel 中读取数据进行分析在执行以
下代码之前，我们需要先把 excel 文件加一个列标题行。可直接另存为上面的excel表格
然后增加后续直接用另存为的表格########################
from pandas import read_excel
df=read_excel(\'list4.xlsx\',sheet_name=\'list2excel07\')
print(df.tail(7))
df.city.count()
df.city.describe()

from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame
word_list= df[\'city\'].fillna(\'0\').tolist()#将 dataframe 的列转化为 list，其中的 nan 用“0”替换
new_text = \' \'.join(word_list)
wordcloud = WordCloud(font_path=\'simhei.ttf\', background_color="black").generate(new_text)
plt.imshow(wordcloud)
plt.axis("off")
plt.show() 

import pandas as pd
#count=df.city.value_count()#对dataframe对 dataframe 进行全频率统计，排除了 nan
city_list=df[\'city\'].fillna(\'NAN\').tolist()#将dataframe的列转化为list,其中nan用NAN替换
count_city=pd.value_counts(city_list)#对list进行全频率统计
from pyecharts import WordCloud
name=count_city.index.tolist()
value=count_city.tolist()
wordcloud=WordCloud(width=1300,height=620)
wordcloud.add("",name,value,word_size_range=[20,100])
wordcloud.show_config()
wordcloud.render(r\'cy.html\')

province_list=df[\'province\'].fillna(\'NAN\').tolist()
count_province=pd.value_counts(province_list)#对list进行全频率统计
from pyecharts import Map
value=count_province.tolist()
attr=count_province.index.tolist()
map=Map("各省微信好友分布",width=1000,height=600) 
map.add("",attr,value,maptype=\'china\',is_visualmap=True,visual_text_color=\'#000\',is_label_show=True)#显示地图上的省份
map.show_config()
map.render(r\'FriendMap.html\')