Python疫情数据爬取与可视化展示

亦凉 2022-11-11 06:37 275阅读 0赞

# [疫情数据爬取与可视化展示][Link 1] #

使用Python爬取腾讯新闻疫情数据，并使用pyecharts可视化，绘制国内、国际日增长人数地图，matplotlib绘制方寸图。

> 随笔记录所的所学，此博客为我记录文章所用，发布到此，仅供网友阅读参考。作者:北山啦

> `写在前面`：这个已经不是什么新鲜的话题了，所以请大佬勿喷

### 文章目录 ###

*  疫情数据爬取与可视化展示
 *  1. 疫情数据抓取
 *  2. 初步分析
 *  3. 数据处理
 *   *  3.1 国内各省疫情数据提取
     *  3.2 国际疫情数据提取
     *  3.3 数据整合
 *  4. 可视化展示
 *   *  4.1 国内疫情态势可视化
     *  4.2 国际疫情态势可视化
     *  4.3 国内疫情方寸间
     *  4.4 国际疫情方寸间
 *  参考链接

导入相关模块

import time
    import json
    import requests
    from datetime import datetime
    import pandas as pd
    import numpy as np

# 1. 疫情数据抓取 #

通过腾讯新闻公布的数据进行爬取

网址:https://news.qq.com/zt2020/page/feiyan.htm\#/

> 对于静态网页，我们只需要把网页地址栏中的url传到get请求中就可以轻松地获取到网页的数据。 对于动态网页抓取的关键是先分析网页数据获取和跳转的逻辑，再去写代码 。

右击检查，选择Network，Ctrl+R即可  
![在这里插入图片描述][20210319221913201.gif_pic_center]

记得安装快速第三方库

pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple 包名

[快速下载模块][Link 2]

# 定义抓取数据函数:https://beishan.blog.csdn.net/
    def Domestic():
        url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'
        reponse = requests.get(url=url).json()
        data = json.loads(reponse['data'])
        return data
    
    
    def Oversea():
        url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_foreign'
        reponse = requests.get(url=url).json()
        data = json.loads(reponse['data'])
        return data
    
    
    domestic = Domestic()
    oversea = Oversea()
    
    print(domestic.keys())
    print(oversea.keys())

dict_keys(['lastUpdateTime', 'chinaTotal', 'chinaAdd', 'isShowAdd', 'showAddSwitch', 'areaTree'])
    dict_keys(['foreignList', 'globalStatis', 'globalDailyHistory', 'importStatis', 'countryAddConfirmRankList', 'countryConfirmWeekCompareRankList', 'continentStatis'])

# 2. 初步分析 #

提取各地区数据明细

# 提取各地区数据明细
    areaTree = domestic['areaTree']
    # 查看并分析具体数据
    areaTree

提取国外地区数据明细

# 提取国外地区数据明细
    foreignList = oversea['foreignList']
    # 查看并分析具体数据
    foreignList

就可以看到在json数据存储的结构了

# 3. 数据处理 #

## 3.1 国内各省疫情数据提取 ##

# Adresss：https://beishan.blog.csdn.net/
    china_data = areaTree[0]['children'] 
    china_list = []
    for a in range(len(china_data)):
        province = china_data[a]['name']  
        confirm = china_data[a]['total']['confirm'] 
        heal = china_data[a]['total']['heal']  
        dead = china_data[a]['total']['dead']  
        nowConfirm = confirm - heal - dead 
        china_dict = {
        } 
        china_dict['province'] = province  
        china_dict['nowConfirm'] = nowConfirm 
        china_list.append(china_dict) 
    
    china_data = pd.DataFrame(china_list) 
    china_data.to_excel("国内疫情.xlsx", index=False) #存储为EXCEL文件
    china_data.head()

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>province</th> 
   <th>nowConfirm</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>香港</td> 
   <td>323</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>上海</td> 
   <td>40</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>四川</td> 
   <td>34</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>台湾</td> 
   <td>30</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>广东</td> 
   <td>29</td> 
  </tr> 
 </tbody> 
</table>

## 3.2 国际疫情数据提取 ##

world_data = foreignList  
    world_list = []  
    
    for a in range(len(world_data)):
        # 提取数据
        country = world_data[a]['name']
        nowConfirm = world_data[a]['nowConfirm']  
        confirm = world_data[a]['confirm']
        dead = world_data[a]['dead']  
        heal = world_data[a]['heal'] 
        # 存放数据
        world_dict = {
        }
        world_dict['country'] = country
        world_dict['nowConfirm'] = nowConfirm
        world_dict['confirm'] = confirm
        world_dict['dead'] = dead
        world_dict['heal'] = heal
        world_list.append(world_dict)
    
    world_data = pd.DataFrame(world_list)
    world_data.to_excel("国外疫情.xlsx", index=False)
    world_data.head()

## 3.3 数据整合 ##

将国内数据和海外数据合并

查询数据中是否含有中国疫情数据

world_data.loc[world_data['country'] == "中国"]

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>country</th> 
   <th>nowConfirm</th> 
   <th>confirm</th> 
   <th>dead</th> 
   <th>heal</th> 
  </tr> 
 </thead> 
 <tbody></tbody> 
</table>

从新增areaTree中提取中国数据，并添加至world\_data

confirm = areaTree[0]['total']['confirm']  # 提取中国累计确诊数据
    heal = areaTree[0]['total']['heal']  # 提取中国累计治愈数据
    dead = areaTree[0]['total']['dead']  # 提取中国累计死亡数据
    nowConfirm = confirm - heal - dead  # 计算中国现有确诊数量
    
    world_data = world_data.append(
        {
        
            'country': "中国",
            'nowConfirm': nowConfirm,
            'confirm': confirm,
            'heal': heal,
            'dead': dead
        },
        ignore_index=True)

再次查询数据中是否含有中国疫情数据

world_data.loc[world_data['country'] == "中国"]

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>country</th> 
   <th>nowConfirm</th> 
   <th>confirm</th> 
   <th>dead</th> 
   <th>heal</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>161</th> 
   <td>中国</td> 
   <td>518</td> 
   <td>102479</td> 
   <td>4849</td> 
   <td>97112</td> 
  </tr> 
 </tbody> 
</table>

# 4. 可视化展示 #

## 4.1 国内疫情态势可视化 ##

导入pyecharts相关库

[快速掌握数据可视化工具pyecharts][pyecharts]

import pyecharts.options as opts
    from pyecharts.charts import Map
    from pyecharts.globals import CurrentConfig, NotebookType
    CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB

国内各地区现有确诊人数地图

m = Map()
    m.add("", [
        list(z)
        for z in zip(list(china_data["province"]), list(china_data["nowConfirm"]))
    ],
          maptype="china",
          is_map_symbol_show=False)
    m.set_global_opts(
        title_opts=opts.TitleOpts(title="COVID-19中国现有地区现有确诊人数地图"),
        visualmap_opts=opts.VisualMapOpts(
            is_piecewise=True,
            pieces=[
                {
        
                    "min": 5000,
                    "label": '>5000',
                    "color": "#893448"
                },  # 不指定 max，表示 max 为无限大
                {
        
                    "min": 1000,
                    "max": 4999,
                    "label": '1000-4999',
                    "color": "#ff585e"
                },
                {
        
                    "min": 500,
                    "max": 999,
                    "label": '500-1000',
                    "color": "#fb8146"
                },
                {
        
                    "min": 101,
                    "max": 499,
                    "label": '101-499',
                    "color": "#ffA500"
                },
                {
        
                    "min": 10,
                    "max": 100,
                    "label": '10-100',
                    "color": "#ffb248"
                },
                {
        
                    "min": 1,
                    "max": 9,
                    "label": '1-9',
                    "color": "#fff2d1"
                },
                {
        
                    "max": 1,
                    "label": '0',
                    "color": "#ffffff"
                }
            ]))
    m.render_notebook()

![在这里插入图片描述][20210319222739552.gif_pic_center]

## 4.2 国际疫情态势可视化 ##

将各国的中文名称转换成英文名称，使用pandas中的merge方法

> pd.merge( left, right, how=‘inner’, on=None, left\_on=None, right\_on=None, left\_index=False, right\_index=False, sort=False, suffixes=(’\_x’, ‘\_y’), copy=True, indicator=False, validate=None,)

how: One of ‘left’, ‘right’, ‘outer’, ‘inner’. 默认inner。inner是取交集，outer取并集

world_name = pd.read_excel("国家中英文对照表.xlsx")
    world_data_t = pd.merge(world_data,
                            world_name,
                            left_on="country",
                            right_on="中文",
                            how="inner")
    
    world_data_t

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>country</th> 
   <th>nowConfirm</th> 
   <th>confirm</th> 
   <th>dead</th> 
   <th>heal</th> 
   <th>英文</th> 
   <th>中文</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>美国</td> 
   <td>7282611</td> 
   <td>30358880</td> 
   <td>552470</td> 
   <td>22523799</td> 
   <td>United States</td> 
   <td>美国</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>西班牙</td> 
   <td>193976</td> 
   <td>3212332</td> 
   <td>72910</td> 
   <td>2945446</td> 
   <td>Spain</td> 
   <td>西班牙</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>法国</td> 
   <td>2166003</td> 
   <td>2405255</td> 
   <td>57671</td> 
   <td>181581</td> 
   <td>France</td> 
   <td>法国</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>秘鲁</td> 
   <td>111940</td> 
   <td>422183</td> 
   <td>19408</td> 
   <td>290835</td> 
   <td>Peru</td> 
   <td>秘鲁</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>英国</td> 
   <td>90011</td> 
   <td>104145</td> 
   <td>13759</td> 
   <td>375</td> 
   <td>United Kingdom</td> 
   <td>英国</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>164</th> 
   <td>伯利兹</td> 
   <td>2</td> 
   <td>2</td> 
   <td>0</td> 
   <td>0</td> 
   <td>Belize</td> 
   <td>伯利兹</td> 
  </tr> 
  <tr> 
   <th>165</th> 
   <td>东帝汶</td> 
   <td>1</td> 
   <td>1</td> 
   <td>0</td> 
   <td>0</td> 
   <td>Timor-Leste</td> 
   <td>东帝汶</td> 
  </tr> 
  <tr> 
   <th>166</th> 
   <td>东帝汶</td> 
   <td>1</td> 
   <td>1</td> 
   <td>0</td> 
   <td>0</td> 
   <td>East Timor</td> 
   <td>东帝汶</td> 
  </tr> 
  <tr> 
   <th>167</th> 
   <td>巴布亚新几内亚</td> 
   <td>1</td> 
   <td>1</td> 
   <td>0</td> 
   <td>0</td> 
   <td>Papua New Guinea</td> 
   <td>巴布亚新几内亚</td> 
  </tr> 
  <tr> 
   <th>168</th> 
   <td>中国</td> 
   <td>518</td> 
   <td>102479</td> 
   <td>4849</td> 
   <td>97112</td> 
   <td>China</td> 
   <td>中国</td> 
  </tr> 
 </tbody> 
</table>

169 rows × 7 columns

世界各国现有确诊人数地图

m2 = Map()
    m2.add("", [
        list(z)
        for z in zip(list(world_data_t["英文"]), list(world_data_t["nowConfirm"]))
    ],
           maptype="world",
           is_map_symbol_show=False)
    m2.set_global_opts(title_opts=opts.TitleOpts(title="COVID-19世界各国现有确诊人数地图"),
                       visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
                                                         pieces=[{
        
                                                             "min": 5000,
                                                             "label": '>5000',
                                                             "color": "#893448"
                                                         }, {
        
                                                             "min": 1000,
                                                             "max": 4999,
                                                             "label": '1000-4999',
                                                             "color": "#ff585e"
                                                         }, {
        
                                                             "min": 500,
                                                             "max": 999,
                                                             "label": '500-1000',
                                                             "color": "#fb8146"
                                                         }, {
        
                                                             "min": 101,
                                                             "max": 499,
                                                             "label": '101-499',
                                                             "color": "#ffA500"
                                                         }, {
        
                                                             "min": 10,
                                                             "max": 100,
                                                             "label": '10-100',
                                                             "color": "#ffb248"
                                                         }, {
        
                                                             "min": 0,
                                                             "max": 9,
                                                             "label": '0-9',
                                                             "color": "#fff2d1"
                                                         }]))
    """取消显示国家名称"""
    m2.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    m2.render_notebook()

![在这里插入图片描述][20210319222752695.gif_pic_center]

## 4.3 国内疫情方寸间 ##

单独取出中国疫情数据

# 单独取出中国疫情数据
    China_data = world_data.loc[world_data['country'] == "中国"]
    
    # 使索引从0开始递增
    China_data.reset_index(drop=True, inplace=True)
    
    China_data

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>country</th> 
   <th>nowConfirm</th> 
   <th>confirm</th> 
   <th>dead</th> 
   <th>heal</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>中国</td> 
   <td>518</td> 
   <td>102479</td> 
   <td>4849</td> 
   <td>97112</td> 
  </tr> 
 </tbody> 
</table>

提取China\_data的累计确诊、累计治愈与累计死亡数据

# 提取China_data的累计确诊、累计治愈与累计死亡数据
    # data.at[n,'name']代表根据行索引和列名，获取对应元素的值
    w_confirm = China_data.at[0, 'confirm']
    w_heal = China_data.at[0, 'heal']
    w_dead = China_data.at[0, 'dead']

导入matplotlib相关库

import matplotlib.pyplot as plt
    import matplotlib.patches as patches

构建国内疫情方寸间图示

# -*- coding: utf-8 -*-
    %matplotlib inline
    
    fig1 = plt.figure()
    
    ax1 = fig1.add_subplot(111, aspect='equal', facecolor='#fafaf0')
    ax1.set_xlim(-w_confirm / 2, w_confirm / 2)
    ax1.set_ylim(-w_confirm / 2, w_confirm / 2)
    ax1.spines['top'].set_color('none')
    ax1.spines['right'].set_color('none')
    ax1.spines['bottom'].set_position(('data', 0))
    ax1.spines['left'].set_position(('data', 0))
    ax1.set_xticks([])
    ax1.set_yticks([])
    
    p0 = patches.Rectangle((-w_confirm / 2, -w_confirm / 2),
                           width=w_confirm,
                           height=w_confirm,
                           facecolor='#29648c',
                           label='confirm')
    p1 = patches.Rectangle((-w_heal / 2, -w_heal / 2),
                           width=w_heal,
                           height=w_heal,
                           facecolor='#69c864',
                           label='heal')
    p2 = patches.Rectangle((-w_dead / 2, -w_dead / 2),
                           width=w_dead,
                           height=w_dead,
                           facecolor='#000000',
                           label='dead')
    
    plt.gca().add_patch(p0)
    plt.gca().add_patch(p1)
    plt.gca().add_patch(p2)
    plt.title('COVID-19 Square - China', fontdict={
        'size': 20})
    plt.legend(loc='best')
    plt.show()

![在这里插入图片描述][20210319222804383.png_pic_center]

## 4.4 国际疫情方寸间 ##

重新排序数据

world_data.sort_values("confirm", ascending=False, inplace=True)
    world_data.reset_index(drop=True, inplace=True)
    world_data

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>country</th> 
   <th>nowConfirm</th> 
   <th>confirm</th> 
   <th>dead</th> 
   <th>heal</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>美国</td> 
   <td>7282611</td> 
   <td>30358880</td> 
   <td>552470</td> 
   <td>22523799</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>西班牙</td> 
   <td>193976</td> 
   <td>3212332</td> 
   <td>72910</td> 
   <td>2945446</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>法国</td> 
   <td>2166003</td> 
   <td>2405255</td> 
   <td>57671</td> 
   <td>181581</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>秘鲁</td> 
   <td>111940</td> 
   <td>422183</td> 
   <td>19408</td> 
   <td>290835</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>英国</td> 
   <td>90011</td> 
   <td>104145</td> 
   <td>13759</td> 
   <td>375</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>157</th> 
   <td>利比里亚</td> 
   <td>3</td> 
   <td>3</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <th>158</th> 
   <td>几内亚比绍</td> 
   <td>2</td> 
   <td>2</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <th>159</th> 
   <td>伯利兹</td> 
   <td>2</td> 
   <td>2</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <th>160</th> 
   <td>东帝汶</td> 
   <td>1</td> 
   <td>1</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <th>161</th> 
   <td>巴布亚新几内亚</td> 
   <td>1</td> 
   <td>1</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
 </tbody> 
</table>

162 rows × 5 columns

构建国际疫情方寸间图示

# -*- coding: utf-8 -*-
    plt.rcParams['font.sans-serif'] = [u'SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    fig1 = plt.figure(figsize=(25, 25))
    for a in range(20):
    
        w_confirm = world_data.at[a, 'confirm']
        w_heal = world_data.at[a, 'heal']
        w_dead = world_data.at[a, 'dead']
        ax1 = fig1.add_subplot(20 / 4,
                               4,
                               a + 1,
                               aspect='equal',
                               facecolor='#fafaf0')
        ax1.set_xlim(-w_confirm / 2, w_confirm / 2)
        ax1.set_ylim(-w_confirm / 2, w_confirm / 2)
    
        ax1.spines['top'].set_color('none')
        ax1.spines['right'].set_color('none')
        ax1.spines['bottom'].set_position(('data', 0))
        ax1.spines['left'].set_position(('data', 0))
        ax1.set_xticks([])
        ax1.set_yticks([])
        p0 = patches.Rectangle((-w_confirm / 2, -w_confirm / 2),
                               width=w_confirm,
                               height=w_confirm,
                               alpha=w_confirm / 90000,
                               facecolor='#29648c',
                               label='confirm')
        p1 = patches.Rectangle((-w_heal / 2, -w_heal / 2),
                               width=w_heal,
                               height=w_heal,
                               alpha=1,
                               facecolor='#69c864',
                               label='heal')
        p2 = patches.Rectangle((-w_dead / 2, -w_dead / 2),
                               width=w_dead,
                               height=w_dead,
                               alpha=1,
                               facecolor='black',
                               label='dead')
        plt.gca().add_patch(p0)
        plt.gca().add_patch(p1)
        plt.gca().add_patch(p2)
    
    
        plt.title(world_data.at[a, 'country'], fontdict={
        'size': 20})
    
    
        plt.legend(loc='best')
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center]  
这样就可以清楚的看到各个国家新冠确诊人数、治愈和死亡人数的关系了

`推荐阅读`：

1.  [冰冰B站视频弹幕爬取原理解析][B]
2.  [Python建立时间序列ARIMA模型实战案例][Python_ARIMA]
3.  [使用xpath爬取数据][xpath]
4.  [jupyter notebook使用][jupyter notebook]
5.  [BeautifulSoup爬取豆瓣电影Top250][BeautifulSoup_Top250]
6.  [一篇文章带你掌握requests模块][requests]
7.  [Python网络爬虫基础–BeautifulSoup][Python_BeautifulSoup]

# 参考链接 #

1.  [腾讯疫情动态][Link 3]
2.  [Python 制作全国疫情地图][Python]
3.  [利用Python实现新冠疫情数据可视化][Python 1]
4.  [欢迎关注][Link 4]

![在这里插入图片描述][resize_m_lfit_w_962_pic_center]

> 到这里就结束了，如果对你有帮助，欢迎点赞关注评论，你的点赞对我很重要。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70]

[Link 1]: https://beishan.blog.csdn.net/article/details/115017160
[20210319221913201.gif_pic_center]: /images/20221022/56c603c056ad4ad090a4541ae4cd5e26.png
[Link 2]: https://blog.csdn.net/qq_45176548/article/details/111152247
[pyecharts]: https://blog.csdn.net/qq_45176548/article/details/112221932
[20210319222739552.gif_pic_center]: https://img-blog.csdnimg.cn/20210319222739552.gif#pic_center
[20210319222752695.gif_pic_center]: https://img-blog.csdnimg.cn/20210319222752695.gif#pic_center
[20210319222804383.png_pic_center]: https://img-blog.csdnimg.cn/20210319222804383.png#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70_pic_center]: https://img-blog.csdnimg.cn/20210319222820935.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4,size_16,color_FFFFFF,t_70#pic_center
[B]: https://blog.csdn.net/qq_45176548/article/details/112235146
[Python_ARIMA]: https://blog.csdn.net/qq_45176548/article/details/111504846#comments_14302892
[xpath]: https://blog.csdn.net/qq_45176548/article/details/112000086
[jupyter notebook]: https://blog.csdn.net/qq_45176548/article/details/111993214
[BeautifulSoup_Top250]: https://blog.csdn.net/qq_45176548/article/details/111519379
[requests]: https://blog.csdn.net/qq_45176548/article/details/111146017
[Python_BeautifulSoup]: https://blog.csdn.net/qq_45176548/article/details/109144737
[Link 3]: https://xw.qq.com/act/qgfeiyan
[Python]: https://zhuanlan.zhihu.com/p/107372262
[Python 1]: https://blog.csdn.net/maiyida123/article/details/108716347
[Link 4]: https://beishan.blog.csdn.net/
[resize_m_lfit_w_962_pic_center]: https://img-blog.csdnimg.cn/cover5/842582578215911448.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,image_MjAyMDA3MTUxNjIxMDEzOC5wbmc=,size_16,color_FFFFFF,t_70,image/resize,m_lfit,w_962#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20210606212745631.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1MTc2NTQ4,size_16,color_FFFFFF,t_70