#-*- coding: utf-8 -*-
import re
from wxpy import *
import jieba
import numpy
import pandas as pd
import matplotlib.pyplot as plt
from scipy.misc import imread
from wordcloud import WordCloud, ImageColorGenerator
def write_txt_file(path, txt):
\'\'\'
写入txt文本
\'\'\'
with open(path, \'a\', encoding=\'gb18030\', newline=\'\') as f:
f.write(txt)
def read_txt_file(path):
\'\'\'
读取txt文本
\'\'\'
with open(path, \'r\', encoding=\'gb18030\', newline=\'\') as f:
return f.read()
def login():
# 初始化机器人，扫码登陆
bot = Bot()
# 获取所有好友
my_friends = bot.friends()
print(type(my_friends))
return my_friends
def show_sex_ratio(friends):
# 使用一个字典统计好友男性和女性的数量
sex_dict = {\'male\': 0, \'female\': 0}
for friend in friends:
# 统计性别
if friend.sex == 1:
sex_dict[\'male\'] += 1
elif friend.sex == 2:
sex_dict[\'female\'] += 1
print(sex_dict)
def show_area_distribution(friends):
# 使用一个字典统计各省好友数量
province_dict = {\'北京\': 0, \'上海\': 0, \'天津\': 0, \'重庆\': 0,
\'河北\': 0, \'山西\': 0, \'吉林\': 0, \'辽宁\': 0, \'黑龙江\': 0,
\'陕西\': 0, \'甘肃\': 0, \'青海\': 0, \'山东\': 0, \'福建\': 0,
\'浙江\': 0, \'台湾\': 0, \'河南\': 0, \'湖北\': 0, \'湖南\': 0,
\'江西\': 0, \'江苏\': 0, \'安徽\': 0, \'广东\': 0, \'海南\': 0,
\'四川\': 0, \'贵州\': 0, \'云南\': 0,
\'内蒙古\': 0, \'新疆\': 0, \'宁夏\': 0, \'广西\': 0, \'西藏\': 0,
\'香港\': 0, \'澳门\': 0}
# 统计省份
for friend in friends:
if friend.province in province_dict.keys():
province_dict[friend.province] += 1
# 为了方便数据的呈现，生成JSON Array格式数据
data = []
for key, value in province_dict.items():
data.append({\'name\': key, \'value\': value})
# print(data)
def show_signature(friends):
# 统计签名
for friend in friends:
# 对数据进行清洗，将标点符号等对词频统计造成影响的因素剔除
pattern = re.compile(r\'[一-龥]+\')
filterdata = re.findall(pattern, friend.signature)
write_txt_file(\'signatures.txt\', \'\'.join(filterdata))
# 读取文件
content = read_txt_file(\'signatures.txt\')
segment = jieba.lcut(content)
words_df = pd.DataFrame({\'segment\':segment})
# 读取stopwords
stopwords = pd.read_csv("stopwords.txt",index_col=False,quoting=3,sep=" ",names=[\'stopword\'],encoding=\'utf-8\')
words_df = words_df[~words_df.segment.isin(stopwords.stopword)]
# print(words_df)
jishu=numpy.size
words_stat = words_df.groupby(by=[\'segment\'])[\'segment\'].agg([jishu])
words_stat = words_stat.reset_index().sort_values(by=[jishu],ascending=False)
# 设置词云属性
color_mask = imread(\'background.jfif\')
wordcloud = WordCloud(font_path="simhei.ttf", # 设置字体可以显示中文
background_color="white", # 背景颜色
max_words=100, # 词云显示的最大词数
mask=color_mask, # 设置背景图片
max_font_size=100, # 字体最大值
random_state=42,
width=1000, height=860, margin=2,# 设置图片默认的大小,但是如果使用背景图片的话, # 那么保存的图片大小将会按照其大小保存,margin为词语边缘距离
)
# 生成词云, 可以用generate输入全部文本,也可以我们计算好词频后使用generate_from_frequencies函数
word_frequence = {x[0]:x[1]for x in words_stat.head(100).values}
# print(word_frequence)
word_frequence_dict = {}
for key in word_frequence:
word_frequence_dict[key] = word_frequence[key]
wordcloud.generate_from_frequencies(word_frequence_dict)
# 从背景图片生成颜色值
image_colors = ImageColorGenerator(color_mask)
# 重新上色
wordcloud.recolor(color_func=image_colors)
# 保存图片
wordcloud.to_file(\'output.png\')
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
def main():
friends = login()
show_sex_ratio(friends)
show_area_distribution(friends)
show_signature(friends)
if __name__ == \'__main__\':
main()