微信好友数据分析
步骤:
- 模拟登陆微信web版
- 获取需要的数据
- 对数据进行分析
所需第三方模块:
- wxpy: 微信网页版接口封装Python版本，在本文中用以获取微信好友信息
- jieba: 结巴分词的 Python 版本，在本文中用以对文本信息进行分词处理
- snownlp: 一个 Python 中的中文分词模块，在本文中用以对文本信息进行情感判断。
- matplotlib: Python 中图表绘制模块，在本文中用以绘制柱形图和饼图
- 登陆网页版微信:
from wxpy import *
# 初始化机器人，扫码登陆
# bot = Bot()
bot = Bot(console_qr=True, cache_path=True) # 保留缓存自动登录
- 获取数据
friends = bot.friends()
返回的friends对象是一个包含当前用户的集合.所以取数据的时候采用friends[1:]
好友的数据包括remark_name备注名称,sex性别,province省,city市, signature签名,headimage头像
这次我只分析了前面的name,sex,province,city,signature
- 数据分析
- 3.1 总体分析
# 总体分析
def analyseTotal(friends):
result = friends.stats_text()
print(result)
- 3.2 具体分析
def analyseConcrete(friends):
text = friends.stats()
print('sex:',text['sex'])
print('province:',text['province'])
print('city:',text['city'])
for friend in friends[1:]:
print(friend.name,friend.sex,friend.province,friend.city,friend.signature)
- 3.3 对性别分析
# 性别分析,饼状图显示
def analyseSex(friends):
text = friends.stats()
male = text['sex'][1]
female = text['sex'][2]
unknown = text['sex'][0]
labels = 'male','female','unknown'
sizes = [male,female,unknown]
explode = (0, 0.1, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
- 3.4 对签名进行分析
# 分析个性签名
def analyseSignature(friends):
signatures = ''
emotions = []
pattern = re.compile("lf\d.+")
for friend in friends[1:]:
signature = friend.signature
if signature != None:
signature = signature.strip().replace('span','').replace('class','').replace('emoji','')
signature = re.sub(r'lf(\d.+)','',signature)
# print(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)
# 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'%s的微信好友签名信息情感分析' % friends[0])
plt.show()
最后:
上个完整代码:
from wxpy import *
import jieba
import re
from snownlp import SnowNLP
import jieba.analyse
import matplotlib.pyplot as plt
bot = Bot(console_qr=True, cache_path=True) # 登陆一次后利用缓存登陆
# bot =Bot() # 每次都需要登陆
friends = bot.friends()
# 总体分析
def analyseTotal(friends):
result = friends.stats_text()
print(result)
# 具体分析每个好友
def analyseConcrete(friends):
text = friends.stats()
print('sex:',text['sex'])
print('province:',text['province'])
print('city:',text['city'])
for friend in friends[1:]:
print(friend.name,friend.sex,friend.province,friend.city,friend.signature)
# 性别分析,饼状图显示
def analyseSex(friends):
text = friends.stats()
male = text['sex'][1]
female = text['sex'][2]
unknown = text['sex'][0]
labels = 'male','female','unknown'
sizes = [male,female,unknown]
explode = (0, 0.1, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
# 分析个性签名
def analyseSignature(friends):
signatures = ''
emotions = []
pattern = re.compile("lf\d.+")
for friend in friends[1:]:
signature = friend.signature
if signature != None:
signature = signature.strip().replace('span','').replace('class','').replace('emoji','')
signature = re.sub(r'lf(\d.+)','',signature)
# print(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)
# 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'%s的微信好友签名信息情感分析' % friends[0])
plt.show()
def main():
analyseTotal(friends=friends)
# analyseConcrete(friends=friends)
analyseSex(friends=friends)
analyseSignature(friends=friends)
x = input('输入任意字符退出')
main()
