python分析好友情况，并且制作机器人聊天

1首先安装Pyechart库

打开cmd，在cmd中使用pip命令安装pyecharts库，如下所示：

pip install pyecharts -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com

 

2我们先分析我们好友性别比例情况吧，可用该函数实现。

 1 def fun_analyse_sex(friends):
 2     sexs = list(map(lambda x:x['Sex'],friends[1:]))#收集性别数据
 3     counts = list(map(lambda x:x[1],Counter(sexs).items()))#统计不同性别的数量
 4     counts = sorted(counts)
 5     labels = ['secret','male','female']#2:女，1：男，0：保密
 6     colors = ['red','yellow','blue']
 7     plt.figure(figsize=(8,5), dpi=80)
 8     plt.axes(aspect=1)
 9     plt.pie(counts, #性别统计结果
10     labels=labels, #性别展示标签
11     colors=colors, #饼图区域配色
12     labeldistance = 1.1, #标签距离圆点距离
13     autopct = '%3.1f%%', #饼图区域文本格式
14     shadow = False, #饼图是否显示阴影
15     startangle = 90, #饼图起始角度
16     pctdistance = 0.6 #饼图区域文本距离圆点距离
17     )
18     plt.legend(loc='upper left')#标签位置
19     plt.title('mywechat''%sfemale and male' % friends[0]['NickName'])
20     plt.show()

运行的显示效果如下：

 

 

哈哈，比例好像有点不对。
3我们再来试试分析微信朋友的个性签名吧，并把它分为积极，消极和中性，且做成词云图。

def fun_analyse_Signature(friends):
    signatures = ''
    emotions = []
    for friend in friends:
        signature = friend['Signature']
        if signature != None:
            signature = signature.strip().replace("span","").replace("class","").replace("emoji","")#去除无关数据
            signature = re.sub(r'1f(\d.+)',"",signature)
        if len(signature) > 0:
            nlp = snownlp.SnowNLP(signature)
            emotions.append(nlp.sentiments)#nlp.sentiments：权值
            signatures += " ".join(jieba.analyse.extract_tags(signature,5))#关键字提取
    back_coloring = np.array(Image.open("xiaohuangren.jpg"))#图片可替换
    word_cloud2 = WordCloud(font_path = 'simkai.ttf',
                         background_color = 'white',
                         max_words = 1200,
                         mask = back_coloring,
                         margin = 15)
    word_cloud2.generate(signatures)
    image_colors = ImageColorGenerator(back_coloring)
    plt.figure(figsize=(8,5),dpi=160)
    plt.imshow(word_cloud2.recolor(color_func=image_colors))
    plt.axis("off")
    plt.show()
    word_cloud2.to_file("signatures.jpg")
#人生观
    count_positive = len(list(filter(lambda x:x>0.66,emotions)))#大于0.66为积极
    count_negative = len(list(filter(lambda x:x<0.33,emotions)))#小于0.33为消极
    count_neutral = len(list(filter(lambda x:x>=0.33 and x <= 0.66,emotions)))
    labels = [u'积极',u'中性',u'消极']
    values =(count_positive,count_neutral,count_negative)
    plt.rcParams['font.sans-serif'] = ['simHei']
    plt.rcParams['axes.unicode_minus'] = False
    plt.xlabel("情感判断")
    plt.ylabel("频数")
    plt.xticks(range(3),labels)
    plt.legend(loc='upper right')
    plt.bar(range(3),values,color='rgb')
    plt.title(u'%s的微信好友签名信息情感分析情况' % friends[0]['NickName'])
    plt.show()

 

效果运行如下：

生成柱状图效果


4我们再来分析微信好友数量和群数量情况吧，代码如下

import itchat
from itchat.content import TEXT
from itchat.content import *
import sys
import time
import re
import os

@itchat.msg_register([TEXT,PICTURE,FRIENDS,CARD,MAP,SHARING,RECORDING,ATTACHMENT,VIDEO],isGroupChat=True)
def receive_msg(msg):
    groups  = itchat.get_chatrooms(update=True)
    friends = itchat.get_friends(update=True)
    print ("群数量:",len(groups))
    for i in range(0,len(groups)):
        print (i+1,"--",groups[i]['NickName'],groups[i]['MemberCount'],"人")
    print ("好友数量",len(friends)-1)
    for f in range(1,len(friends)):#第0个好友是自己,不统计
        if friends[f]['RemarkName']: # 优先使用好友的备注名称，没有则使用昵称
            user_name = friends[f]['RemarkName']
        else:
            user_name = friends[f]['NickName']
        sex = friends[f]['Sex']
        print (f,"--",user_name,sex)
itchat.auto_login(hotReload=True)
itchat.run()

 

运行效果如下：  下面一些就不截图了。




5我们再来做个好友所在地分析吧：代码如下：（先把好友信息存进csv文件里面，在把csv文件内容用地图显示）

def analyseLocation(friends):
    headers = ['NickName','Province','City']
    with open('F:\\www\cgi-bin\wechat_data.xlsx','w',encoding='utf-8',newline='',) as csvFile:
        writer = csv.DictWriter(csvFile, headers)
        writer.writeheader()
        for friend in friends[1:]:
           row = {}
           row['NickName'] = friend['NickName']
           row['Province'] = friend['Province']
           row['City'] = friend['City']
           writer.writerow(row)
def shou_data_in_countrymap(save_road,file_name,sheet_name='wechat1',\
                           column_name='Province'):
    '''将这些个好友在全国地图上做分布'''
    f=open(file_name,'rb')
    data=pd.read_excel(f,sheetname=sheet_name)
    province_list = data[column_name].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
    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(save_road+'map1'+'.html')
    f.close()

 

效果如下：

 

 

6最后我们来做个微信机器人聊天：代码如下（我们要申请一个茉莉机器人）   可以在这里申请一个：http://www.itpk.cn/

import itchat
import requests

def get_response(msg):
    apiurl = 'http://i.itpk.cn/api.php'  #moli机器人的网址
    data={
        "question": msg,    #获取到聊天的文本信息
        "api_key": "215bbeaf9f4788d1c57d3c06ec76b659",
        "api_secret": "cjoxg5lqrt58"
    }

    r=requests.post(apiurl,data=data)  #构造网络请求
    return r.text
@itchat.msg_register(itchat.content.TEXT)     #好友消息的处理
def print_content(msg):
    return get_response(msg['Text'])
@itchat.msg_register([itchat.content.TEXT], isGroupChat=True)  #群消息的处理
def print_content(msg):
    return get_response(msg['Text'])
itchat.auto_login(True)           #自动登录
itchat.run()

 

运行效果后会有个二维码扫描就会自动回复消息了。
好了 到了完整代码了：

import itchat
import numpy as np
#import os
from collections import Counter
import matplotlib.pyplot as plt
#from wordcloud import WordCloud,STOPWORDS,ImageColorGenerator
from PIL import Image
#import time
import re
import snownlp
import jieba
import jieba.analyse
import pandas as pd
#from pandas import DataFrame
#import openpyxl
import csv
from pyecharts import Map

def fun_analyse_sex(friends):
    sexs = list(map(lambda x:x['Sex'],friends[1:]))#收集性别数据
    counts = list(map(lambda x:x[1],Counter(sexs).items()))#统计不同性别的数量
    counts = sorted(counts)
    labels = ['保密','男','女']#2:女，1：男，0：保密
    colors = ['red','yellow','blue']
    plt.figure(figsize=(8,5), dpi=80)
    plt.axes(aspect=1)
    plt.pie(counts, #性别统计结果
    labels=labels, #性别展示标签
    colors=colors, #饼图区域配色
    labeldistance = 1.1, #标签距离圆点距离
    autopct = '%3.1f%%', #饼图区域文本格式
    shadow = False, #饼图是否显示阴影
    startangle = 90, #饼图起始角度
    pctdistance = 0.6 #饼图区域文本距离圆点距离
    )
    plt.legend(loc='upper left')#标签位置
    plt.title('mywechat''%sfemale and male' % friends[0]['NickName'])
    plt.show()
def fun_analyse_Signature(friends):
    signatures = ''
    emotions = []
    for friend in friends:
        signature = friend['Signature']
        if signature != None:
            signature = signature.strip().replace("span","").replace("class","").replace("emoji","")#去除无关数据
            signature = re.sub(r'1f(\d.+)',"",signature)
        if len(signature) > 0:
            nlp = snownlp.SnowNLP(signature)
            emotions.append(nlp.sentiments)#nlp.sentiments：权值
            signatures += " ".join(jieba.analyse.extract_tags(signature,5))#关键字提取
    back_coloring = np.array(Image.open("xiaohuangren.jpg"))#图片可替换
    word_cloud2 = WordCloud(font_path = 'simkai.ttf',
                         background_color = 'white',
                         max_words = 1200,
                         mask = back_coloring,
                         margin = 15)
    word_cloud2.generate(signatures)
    image_colors = ImageColorGenerator(back_coloring)
    plt.figure(figsize=(8,5),dpi=160)
    plt.imshow(word_cloud2.recolor(color_func=image_colors))
    plt.axis("off")
    plt.show()
    word_cloud2.to_file("signatures.jpg")
#人生观
    count_positive = len(list(filter(lambda x:x>0.66,emotions)))#大于0.66为积极
    count_negative = len(list(filter(lambda x:x<0.33,emotions)))#小于0.33为消极
    count_neutral = len(list(filter(lambda x:x>=0.33 and x <= 0.66,emotions)))
    labels = [u'积极',u'中性',u'消极']
    values =(count_positive,count_neutral,count_negative)
    plt.rcParams['font.sans-serif'] = ['simHei']
    plt.rcParams['axes.unicode_minus'] = False
    plt.xlabel("情感判断")
    plt.ylabel("频数")
    plt.xticks(range(3),labels)
    plt.legend(loc='upper right')
    plt.bar(range(3),values,color='rgb')
    plt.title(u'%s的微信好友签名信息情感分析情况' % friends[0]['NickName'])
    plt.show()

def ger_friend_msg():                     #得到的信息存进csv文件里面
    import time
    RemarkName = get_data('RemarkName')
    NickName = get_data('NickName')  # 是一个列表
    City = get_data('City')
    Province = get_data('Province')
    Sex = get_data('Sex')
    Signature = get_data('Signature')
    data = {'备注': RemarkName, '昵称': NickName, '城市': City, '省份': Province,
            '性别': Sex, '微信签名': Signature}  # 字典，字典的value是列表
    frame = DataFrame(data)
    time = time.strftime("%Y-%m-%d_%H_%M_%S", time.localtime())  # 生成一个当前事件，以这个时间来命名最后地csv文件，能很直白地看出最新执行脚本的时间和最新的csv文件
    file_name = time + '微信好友.csv'
    frame.to_csv('f:\\www\\靓仔贵雨\\friends.csv', encoding='utf_8_sig', index=True)

def get_data(arg):       #得到所有信息
    data_list = []
    for i in friends:
        value = i[arg]
        if value == 1:#1代表男/2代表女
            value = '男'#这里把1/2换成了男女
            data_list.append(value)
        elif value == 2:
            value = '女'
            data_list.append(value)
        elif value == 0:
            value = '未知'
            data_list.append(value)
        else:
            data_list.append(value)
    return data_list
def analyseLocation(friends):
    headers = ['NickName','Province','City']
    with open('F:\\www\cgi-bin\wechat_data.xlsx','w',encoding='utf-8',newline='',) as csvFile:
        writer = csv.DictWriter(csvFile, headers)
        writer.writeheader()
        for friend in friends[1:]:
           row = {}
           row['NickName'] = friend['NickName']
           row['Province'] = friend['Province']
           row['City'] = friend['City']
           writer.writerow(row)
def shou_data_in_countrymap(save_road,file_name,sheet_name='wechat1',\
                           column_name='Province'):
    '''将这些个好友在全国地图上做分布'''
    f=open(file_name,'rb')
    data=pd.read_excel(f,sheetname=sheet_name)
    province_list = data[column_name].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
    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(save_road+'map1'+'.html')
    f.close()

#if __name__ == "__main__":
def main():
    itchat.auto_login(hotReload=True)
    friends = itchat.get_friends(update=True)
    fun_analyse_sex(friends)
    fun_analyse_Signature(friends)
    #ger_friend_msg()
    save_road=r'F:\www\cgi-bin\pictuer'
    file_name='F:\\www\cgi-bin\wechat_data.xlsx'
    shou_data_in_countrymap(save_road,file_name)
main()

 

今天分享就到这里了~~~~~

 

 

 

 

转载于:https://www.cnblogs.com/sgy614092725/p/shiguiyu19.html