微信好友分析及机器人

一、微信好友分析

1、统计好友，城市分布，个性签名并保存为excell

#导入模块
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()
len(friend_all)
lis=[]
for a_friend in friend_all:
    NickName = a_friend.raw.get(\'NickName\',None)
    #Sex = a_friend.raw.get(\'Sex\',None)
    Sex ={1:"男",2:"女",0:"其它"}.get(a_friend.raw.get(\'Sex\',None),None)
    City = a_friend.raw.get(\'City\',None)
    Province = a_friend.raw.get(\'Province\',None)
    Signature = a_friend.raw.get(\'Signature\',None)
    HeadImgUrl = a_friend.raw.get(\'HeadImgUrl\',None)
    HeadImgFlag = a_friend.raw.get(\'HeadImgFlag\',None)
    list_0=[NickName,Sex,City,Province,Signature,HeadImgUrl,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(\'wechat\',lis)

效果如下：

2、将城市信息做成词云并保存为html

代码如下：

import pandas as pd
from wordcloud import WordCloud 
import matplotlib.pyplot as plt
from pyecharts import WordCloud
from pandas import read_excel
df=pd.read_excel(r\'‪C:\Users\80939\Desktop\wechat.xlsx\')
count=df.City.value_counts() #对 dataframe 进行全频率统计，排除了 nan
city_list=df[\'City\'].fillna(\'NAN\').tolist()
count_city=pd.value_counts(city_list)#对 list 进行全频率统计 
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\'‪C:\Users\80939\Desktop\wc.html\')

效果如下：

 

 

3、将好友显示在地图上并保存为html

代码如下：

from pandas import read_excel
import pandas as pd
from wordcloud import WordCloud 
import matplotlib.pyplot as plt
df=pd.read_excel(r\'C:\Users\admin\.spyder-py3\wechat.xlsx)
#将这些个好友在全国地图上做分布
province_list = df[\'Province\'].fillna(\'NAN\').tolist()#将 dataframe 的列转化为 list，其中的 nan 用“NAN”替换
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=1200, 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\'C:\Users\admin\.spyder-py3\map1.html\')

效果如图：

 

 二、微信机器人

代码如下

 

import itchat,code,unicodedata
import requests,re
from urllib.parse import quote,unquote

def get_reply(data):
ini = "{\'sessionId\':\'09e2aca4d0a541f88eecc77c03a8b393\',\'robotId\':\'webbot\',\'userId\':\'462d49d3742745bb98f7538c42f9f874\',\'body\':{\'content\':\'" + data + "\'},\'type\':\'txt\'}&ts=1529917589648"
url = "http://i.xiaoi.com/robot/webrobot?&callback=__webrobot_processMsg&data=" + quote(ini)
cookie = {"cnonce": "808116", "sig": "0c3021aa5552fe597bb55448b40ad2a90d2dead5",
"XISESSIONID": "hlbnd1oiwar01dfje825gavcn", "nonce": "273765", "hibext_instdsigdip2": "1"}
r = requests.get(url, cookies=cookie)
print(r.text)
pattern = re.compile(r\'\"fontColor\":0,\"content\":\"(.*?)\"\')
result = pattern.findall(r.text)
print(result[1])
return result[1]

@itchat.msg_register(itchat.content.TEXT)
def print_content(msg):
print(msg[\'Text\'])
print(msg[\'FromUserName\'])
datas=get_reply(msg[\'Text\'])[:-4]
print(datas)
itchat.send(datas, toUserName=msg[\'莫雨林\'])a

itchat.auto_login()
itchat.run()