Python——微信数据分析

数据可视化：http://echarts.baidu.com/echarts2/doc/example.html

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
#初始化机器人bot
bot = Bot()
# 通过扫码的方式获取你的所有好友信息
My_friends = bot.friends()
print(type(My_friends))
def friends_count():
    sex_dict = {'male':0,'female':0}
    for friend in My_friends:
        if friend.sex ==1:
            sex_dict['male'] +=1
        elif friend.sex ==2:
            sex_dict['female'] +=1
    print(sex_dict)
def province_count():
    # 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 My_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 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 show_signature(My_friends):
    # 统计签名
    for friend in My_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)

    words_stat = words_df.groupby(by=['segment'])['segment'].agg({"计数":numpy.size})
    words_stat = words_stat.reset_index().sort_values(by=["计数"],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()
show_signature(My_friends)