First, go to the application how to go and get a micro-channel gadgets it? Necessary to install so many libraries. Cry chirp. Then I learned how to install so many libraries in the anaconda.
Now, we come to know about how to install the various libraries in the anaconda:
https://jingyan.baidu.com/article/495ba841bcc11c38b30ede32.html
Find online tutorials.
Double-click to open the Anaconda Navigator, choose Environments interface
Select the python environment you want to configure, third-party packages after the right of the search box to enter to install, find the check, click on the bottom right of the apply button to complete the installation.
Then , after I installed the library so much, I learned itchat the library, also run a little test:
This is itchat a library of learning:
import itchat itchat.auto_login() itchat.send('Hello, filehelper', toUserName='filehelper')
The results are as follows:
In this regard, I can only sigh a "Tete What magical, as if to open the door to a new world."
Now let's learn about various libraries:
pillow：
A image processing library:
from PIL import Image # 打开一个jpg图像文件，注意是当前路径: im = Image.open('test.jpg') # 获得图像尺寸: w, h = im.size print('Original image size: %sx%s' % (w, h)) # 缩放到50%: im.thumbnail((w//2, h//2)) print('Resize image to: %sx%s' % (w//2, h//2)) # 把缩放后的图像用jpeg格式保存: im.save('thumbnail.jpg', 'jpeg')
Pycharts这个库嘛：
（这个库我去学习了一下，找了个简单的代码运行时出不来结果，我就去请问了别人。然后我感觉我被虐成了渣渣，短期之内，我不想见到他，但他真的很厉害）
from pyecharts.charts import Bar from pyecharts import options as opts # V1 版本开始支持链式调用 bar = ( Bar() .add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"]) .add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105]) .add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49]) .set_global_opts(title_opts=opts.TitleOpts(title="某商场销售情况")) ) print(bar) bar.render("D:/Python文件/文字文稿1.wps.html") print(bar) # 不习惯链式调用的开发者依旧可以单独调用方法 bar = Bar() bar.add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"]) bar.add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105]) bar.add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49]) bar.set_global_opts(title_opts=opts.TitleOpts(title="某商场销售情况")) bar.render("D:/Python文件/文字文稿1.wps.html") print(bar)
# encoding=utf-8 import jieba seg_list = jieba.cut("我来到北京清华大学", cut_all=True) print("Full Mode: " + "/ ".join(seg_list)) # 全模式 seg_list = jieba.cut("我来到北京清华大学", cut_all=False) print("Default Mode: " + "/ ".join(seg_list)) # 精确模式 seg_list = jieba.cut("他来到了网易杭研大厦") # 默认是精确模式 print(", ".join(seg_list)) seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所，后在日本京都大学深造") # 搜索引擎模式 print(", ".join(seg_list))
- 用法： jieba.load_userdict(file_name) # file_name 为文件类对象或自定义词典的路径
三个库以前学过了。
现在进入正题：按照论文一步步实操：
#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()
我的确只有276位联系人！！
# -*- coding: utf-8 -*- """ Created on Sun Jun 2 10:30:21 2019 @author: history """ import itchat #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() print(friend_all)
对此，我只能说牛逼
以下是真正的代码及运行的结果：
# -*- coding: utf-8 -*- """ Created on Sun Jun 2 15:10:33 2019 @author: history """ # -*- coding: utf-8 -*- #导入模块 # 1. ================================此处可优化======================================================= from wxpy import Bot #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() #查看好友信息都记录了哪些字段 print(friend_all[0].raw) #friend_all[0]是我的微信昵称，.raw 则是获取我的全部信息 print(len(friend_all)) #获取好友信息中的部分需要的信息 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) #将列表信息存储到 xlsx 格式中 def lis2e07(filename,lis): ''' 将列表写入 07 版 excel 中，其中列表中的元素是列表. filename:保存的文件名（含路径) lis：元素为列表的列表，如下： lis = [["名称", "价格", "出版社", "语言"], ["暗时间", "32.4", "人民邮电出版社", "中文"], ["拆掉思维里的墙", "26.7", "机械工业出版社", "中文"]] ''' import openpyxl wb = openpyxl.Workbook() sheet = wb.active sheet.title = 'list2excel07' # 2. 定义列表题 ========================================================================================== title = ['NickName','Sex','City','Province','Signature','HeadImgUrl','HeadImgFlag'] for i in range(len(title)): sheet.cell(row=1, column=i+1, value=title[i]) # ============================================================================================修改部分 for i in range(len(lis)): for j in range(len(lis[i])): # 3. ======修改1处================================================================================ sheet.cell(row=i+2, column=j+1, value=str(lis[i][j])) wb.save(filename) print("写入数据成功！") # 4. 我改了路径，你自己改回去吧================================================================================== lis2e07('D:/Python文件/微信朋友.xlsx',lis) #■■■■■■■■■■■■■■ #用“city”数据做成词云 #方法一 #粗略获取好友的统计信息 Friends = bot.friends() data = Friends.stats_text(total=True, sex=True,top_provinces=30, top_cities=500) print(data) #方法二 #读取数据 from pandas import read_excel df = read_excel('D:/Python文件/微信朋友.xlsx',sheetname='list2excel07') df.tail(10) #词云图 plt+wordcloud from wordcloud import WordCloud import matplotlib.pyplot as plt import pandas as pd word_list= df['City'].fillna('').tolist() #将 dataframe 的列转化为 list，其中的 nan 用“0”替换 new_text = ' '.join(word_list) wordcloud = WordCloud(font_path='simhei.ttf', background_color="black").generate(new_text) plt.imshow(wordcloud) plt.axis("off") plt.show() #方法三 #利用 pyecharm 做词云 count = df['City'].value_counts() #对 dataframe 进行全频率统计，排除了 nan City_list = df['City'].fillna('').tolist() #将 dataframe 的列转化为 list，其中的 nan 用“NAN”替换 count_City = pd.value_counts(City_list) #对 list 进行全频率统计 from pyecharts import WordCloud 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'D:/Python文件/微信应用.html') #■■■■■■■■■■■■■■ #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() #显示配置 map.render(r'D:/Python文件/微信应用.html')
