Python抓取国家医疗费用数据：国家名、人均开销

前言

整个世界正被大流行困扰着，不同国家拿出了不同的应对策略，也取得了不同效果。这也是本文的脑洞来源，打算研究一下各国在医疗基础设置上的开支，对几个国家的医疗费用进行数据可视化。

由于没有找到最近一年的可靠数据来源，所以这里使用的是2016年的数据。数据清楚哪个国家花得最多、哪个国家花得最少。我一直想试试在Python中网络抓取和数据可视化，这算是个不错的项目。虽然手动将数据输入Excel肯定快得多，但是这样就不会有宝贵的机会来练习一些技能了。

数据科学就是利用各种工具包来解决问题，网络抓取和正则表达式是我需要研究的两个领域。结果简短但复杂，这一项目展示了如何将三种技术结合起来解决数据科学问题。

要求

网络抓取主要分为两部分：

通过发出HTTP请求来获取数据

通过解析HTMLDOM来提取重要数据

库和工具

Requests能够非常简单地发送HTTP请求。

Pandas是一个Python包，提供快速、灵活和有表现力的数据结构。

Web Scraper可以帮助在不设置任何自动化浏览器的情况下抓取动态网站。

Beautiful Soup是一个Python库，用于从HTML和XML文件中提取数据。

matplotlib是一个综合的库，用于在Python中创建静态、动画和交互式可视化效果。

设置

设置非常简单，只需创建一个文件夹，并安装BeautifulSoup和Requests。此处假设已经安装了Python3.x，再根据指令来创建文件夹并安装库。

mkdir scraper
pip install beautifulsoup4
pip install requests
pip install matplotlib
pip install pandas

现在，在该文件夹中创建一个任意名称的文件。这里用的是scraping.py.，然后在文件中导入Beautiful Soup和 requests，如下所示：

import pandas as pd
from bs4 import BeautifulSoup
import matplotlib.pyplot as plt
import requests

抓取的内容：国家名；人均开销。

网络抓取

现在，所有scraper设置都已准备好，应向target URL发出GET请求以获得原始HTML数据。

r =requests.get( https://api.scrapingdog.com/scrape?api_key=<YOUR_API_KEY>&url=https://data.worldbank.org/indicator/SH.XPD.CHEX.PC.CD?most_recent_value_desc=false&dynamic=true ).text

这将得出target URL的HTML代码，我们必须使用Beautiful Soup来解析HTML。

soup = BeautifulSoup(r,’html.parser’)
country=list()
expense=list()

笔者用两张空表来存储国家名和每个国家24小时内的开支。可以看到，每个国家都存储在一个“项目”标签中，把所有的项目标签都存储在一张列表中。

try:
 Countries=soup.find_all(“div”,{“class”:”item”})
except:
 Countries=None

世界上有190个国家，为每个国家的医疗开支运行一个for循环：

for i in range(0,190):
country.append(Countries[i+1].find_all(“div”,{“class”:None})[0].text.replace(“”,””))
expense.append(round(float(Countries[i+1].find_all(“div”,{“class”:None})[2].text.replace(“”,””).replace(‘,’,’’)))/365)
Data = {‘country’:country,’expense’: expense}

因为我想看看这些国家每天是如何花钱的，所以把这笔费用除以365。如果把给定的数据直接除以365，这可能会更容易些，但这样就没有学习的意义了。现在的“数据”看起来是这样的：

{ country : [ Central AfricanRepublic ,  Burundi ,  Mozambique ,  Congo, Dem. Rep. ,  Gambia, The ,  Niger , Madagascar ,  Ethiopia ,  Malawi ,  Mali ,  Eritrea ,  Benin ,  Chad , Bangladesh ,  Tanzania ,  Guinea ,  Uganda ,  Haiti ,  Togo ,  Guinea-Bissau , Pakistan ,  Burkina Faso ,  Nepal ,  Mauritania ,  Rwanda ,  Senegal ,  PapuaNew Guinea ,  Lao PDR ,  Tajikistan ,  Zambia ,  Afghanistan ,  Comoros , Myanmar ,  India ,  Cameroon ,  Syrian Arab Republic ,  Kenya ,  Ghana ,"Cote d Ivoire",  Liberia ,  Djibouti ,  Congo, Rep. ,  Yemen, Rep. , Kyrgyz Republic ,  Cambodia ,  Nigeria ,  Timor-Leste ,  Lesotho ,  SierraLeone ,  Bhutan ,  Zimbabwe ,  Angola ,  Sao Tome and Principe ,  SolomonIslands ,  Vanuatu ,  Indonesia ,  Vietnam ,  Philippines ,  Egypt, Arab Rep. , Uzbekistan ,  Mongolia ,  Ukraine ,  Sudan ,  Iraq ,  Sri Lanka ,  CaboVerde ,  Moldova ,  Morocco ,  Fiji ,  Kiribati ,  Nicaragua ,  Guyana , Honduras ,  Tonga ,  Bolivia ,  Gabon ,  Eswatini ,  Thailand ,  Jordan , Samoa ,  Guatemala ,  St. Vincent and the Grenadines ,  Tunisia ,  Algeria , Kazakhstan ,  Azerbaijan ,  Albania ,  Equatorial Guinea ,  El Salvador , Jamaica ,  Belize ,  Georgia ,  Libya ,  Peru ,  Belarus ,  Paraguay ,  NorthMacedonia ,  Colombia ,  Suriname ,  Armenia ,  Malaysia ,  Botswana , Micronesia, Fed. Sts. ,  China ,  Namibia ,  Dominican Republic ,  Iran,Islamic Rep. ,  Dominica ,  Turkmenistan ,  South Africa ,  Bosnia andHerzegovina ,  Mexico ,  Turkey ,  Russian Federation ,  Romania ,  St. Lucia , Serbia ,  Ecuador ,  Tuvalu ,  Grenada ,  Montenegro ,  Mauritius , Seychelles ,  Bulgaria ,  Antigua and Barbuda ,  Brunei Darussalam ,  Oman , Lebanon ,  Poland ,  Marshall Islands ,  Latvia ,  Croatia ,  Costa Rica , St. Kitts and Nevis ,  Hungary ,  Argentina ,  Cuba ,  Lithuania ,  Nauru , Brazil ,  Panama ,  Maldives ,  Trinidad and Tobago ,  Kuwait ,  Bahrain , Saudi Arabia ,  Barbados ,  Slovak Republic ,  Estonia ,  Chile ,  CzechRepublic ,  United Arab Emirates ,  Uruguay ,  Greece ,  Venezuela, RB , Cyprus ,  Palau ,  Portugal ,  Qatar ,  Slovenia ,  Bahamas, The ,  Korea,Rep. ,  Malta ,  Spain ,  Singapore ,  Italy ,  Israel ,  Monaco ,  SanMarino ,  New Zealand ,  Andorra ,  United Kingdom ,  Finland ,  Belgium , Japan ,  France ,  Canada ,  Austria ,  Germany ,  Netherlands ,  Ireland , Australia ,  Iceland ,  Denmark ,  Sweden ,  Luxembourg ,  Norway , Switzerland ,  United States ,  World ],  expense : [0.043835616438356165,0.049315068493150684, 0.052054794520547946, 0.057534246575342465,0.057534246575342465, 0.06301369863013699, 0.06575342465753424,0.07671232876712329, 0.0821917808219178, 0.0821917808219178,0.0821917808219178, 0.0821917808219178, 0.08767123287671233,0.09315068493150686, 0.09863013698630137, 0.10136986301369863,0.10410958904109589, 0.10410958904109589, 0.10684931506849316,0.10684931506849316, 0.1095890410958904, 0.11232876712328767,0.1232876712328767, 0.12876712328767123, 0.13150684931506848,0.14520547945205478, 0.1506849315068493, 0.1506849315068493, 0.15342465753424658,0.15616438356164383, 0.15616438356164383, 0.16164383561643836,0.16986301369863013, 0.1726027397260274, 0.17534246575342466,0.18082191780821918, 0.18082191780821918, 0.1863013698630137,0.1863013698630137, 0.1863013698630137, 0.1917808219178082, 0.1917808219178082,0.19726027397260273, 0.2, 0.2136986301369863, 0.21643835616438356,0.2191780821917808, 0.2356164383561644, 0.2356164383561644, 0.2493150684931507,0.25753424657534246, 0.2602739726027397, 0.2876712328767123, 0.29041095890410956,0.3013698630136986, 0.30684931506849317, 0.336986301369863,0.35342465753424657, 0.3589041095890411, 0.3698630136986301,0.3863013698630137, 0.3863013698630137, 0.41643835616438357,0.4191780821917808, 0.4191780821917808, 0.43561643835616437, 0.4684931506849315,0.4684931506849315, 0.4931506849315068, 0.5150684931506849, 0.5150684931506849,0.5260273972602739, 0.547945205479452, 0.5561643835616439, 0.5835616438356165,0.6027397260273972, 0.6054794520547945, 0.6082191780821918, 0.6136986301369863,0.6219178082191781, 0.6602739726027397, 0.684931506849315, 0.7013698630136986,0.7123287671232876, 0.7178082191780822, 0.7342465753424657, 0.7452054794520548,0.7698630136986301, 0.8054794520547945, 0.810958904109589, 0.8328767123287671,0.8438356164383561, 0.8575342465753425, 0.8657534246575342, 0.8712328767123287,0.8958904109589041, 0.8986301369863013, 0.9315068493150684, 0.9753424657534246,0.9835616438356164, 0.9917808219178083, 1.0410958904109588, 1.0602739726027397,1.0904109589041096, 1.104109589041096, 1.1342465753424658, 1.1369863013698631,1.1479452054794521, 1.158904109589041, 1.1726027397260275, 1.2164383561643837,1.2657534246575342, 1.284931506849315, 1.284931506849315, 1.3041095890410959,1.3424657534246576, 1.3534246575342466, 1.3835616438356164, 1.389041095890411,1.4136986301369863, 1.4575342465753425, 1.515068493150685, 1.6356164383561644,1.6767123287671233, 1.7068493150684931, 1.7287671232876711, 1.7753424657534247,1.8136986301369864, 2.2164383561643834, 2.3315068493150686, 2.3945205479452056,2.421917808219178, 2.4356164383561643, 2.5506849315068494, 2.5835616438356164,2.6164383561643834, 2.66027397260274, 2.706849315068493, 2.7726027397260276,2.7835616438356166, 2.852054794520548, 2.871232876712329, 2.915068493150685,2.926027397260274, 3.010958904109589, 3.1424657534246574, 3.1890410958904107,3.23013698630137, 3.2465753424657535, 3.263013698630137, 3.621917808219178,3.6246575342465754, 3.778082191780822, 4.13972602739726, 4.323287671232877,4.476712328767123, 4.586301369863014, 4.934246575342466, 5.005479452054795,5.024657534246575, 5.027397260273973, 5.6, 6.3780821917808215,6.5479452054794525, 6.745205479452054, 7.504109589041096, 7.772602739726027,8.054794520547945, 8.254794520547945, 10.26027397260274, 10.506849315068493,10.843835616438357, 11.27945205479452, 11.367123287671232, 11.597260273972603,11.67945205479452, 12.213698630136987, 12.843835616438357, 12.915068493150685,12.991780821917809, 13.038356164383561, 13.704109589041096, 13.873972602739727,15.24931506849315, 15.646575342465754, 17.18082191780822, 20.487671232876714,26.947945205479453, 27.041095890410958, 2.8109589041095893]}

数据帧

绘制图表之前，必须使用Pandas准备一个数据帧。首先我们得明确DataFrame是什么：DataFrame是一个二维大小可变的、潜在的异构表格式数据结构，带有标记的轴（行和列）。创造一个数据帧非常简单直接：

df = pd.DataFrame(Data,columns=[‘country’, ‘expense’])

可视化

我们大部分时间都花在收集和格式化数据上，现在到了做图的时候啦，可以使用matplotlib和seaborn 来可视化数据。如果不太在意美观，可以使用内置的数据帧绘图方法快速显示结果：

df.plot(kind = ‘bar’, x=’country’, y=’expense’)
plt.show()

现在，结论出来了：许多国家每天的支出都低于一美元。这些国家中大多数都位于亚洲和非洲，看来世界卫生组织应更关注这些国家。