1 简介

之前用代码做了个广东疫情新增病例的视频，具体可查看《用Python做了一个广东疫情新增病例动态视频》，但当时是手动去网站找数据然后输入到Excel的，这对于一个程序员来说显然是不可接受的：

• 手工容易出错；
• 时间成本高；
• Excel要钱。

因此，我决定写个代码，自动读取数据并存在csv文件中。

2 获取超链接

首先我们到官网查看内容展示：

它是一天一个报告，并没有整合，所以必要读取该网页的内容，获取所有相关的超链接。并且要解析出对应的日期，方便以后使用。代码如下：

def fetch_links():
    url = "http://wsjkw.gd.gov.cn/xxgzbdfk/"
    req = requests.get(url)
    soup = BeautifulSoup(req.content, 'lxml')
    links = soup.findAll('a')
    valid_links_filter = filter(lambda item: 'title' in item.attrs and '广东省新冠肺炎疫情情况' in item.attrs['title'],
                                links)
    valid_links = list(valid_links_filter)
    return valid_links

首先使用requests库读取网页内容，然后通过BeautifulSoup加载，获取所有超链接<a/>。因为整个网站的超链接很多，需要做一个过滤，通过一个filter，把关键字过滤出来即可。

3 获取文本内容

通过上面的方法找到每天的网页后，就需要把报告的内容提取出来，还是先来分析一下网页：

通过查看并分析，图片文中的第一段是我们需要的，它可能是在<p>中或<span>中。一样是通过关键字来匹配获取：

def fetch_content(url):
    req = requests.get(url)
    soup = BeautifulSoup(req.content, 'lxml')
    ps = soup.findAll('p')
    valid_ps_filter = filter(lambda item: '全省新增本土确诊病例' in item.text,
                             ps)
    valid_ps = list(valid_ps_filter)

    if not valid_ps:
        ps = soup.findAll('span')
    valid_ps_filter = filter(lambda item: '全省新增本土确诊病例' in item.text,
                             ps)
    valid_ps = list(valid_ps_filter)

    valid_p = valid_ps[0]
    return valid_p.text

解析出数据

获取到文本内容后，它是一段文字，我们需要从中获取到新增数字，这里使用的是正则的办法，直接上代码吧：

def parse_data(str):
    confirm_str = re.search('全省新增本土确诊病例\\d+例', str)
    confirm_str = re.search('\\d+', confirm_str.group())
    confirm_gd = int(confirm_str.group())

    asymptomatic_str = re.search('新增本土无症状感染者\\d+例', str)
    asymptomatic_str = re.search('\\d+', asymptomatic_str.group())
    asymptomatic_gd = int(asymptomatic_str.group())

    gz_strs = re.findall('广州\\d+例', str)
    gz_str = re.search('\\d+', gz_strs[0])
    confirm_gz = int(gz_str.group())

    gz_str = re.search('\\d+', gz_strs[1])
    asymptomatic_gz = int(gz_str.group())

    return confirm_gd, confirm_gz, asymptomatic_gd, asymptomatic_gz

5 保存数据

拿到数据后，就需要保存起来日后使用。但已经获取过的数据，其实是没有必要再次获取的，所以我们写文件前要做一个判断。要先读文件，然后判断某天数据是否已经存在，如果不存在则需要获取并保存，否则跳过。

读取的函数如下：

def read_data_from_csv(file_name):
    data_store = []
    with open(file_name) as csv_file:
        csv_reader = csv.reader(csv_file, delimiter=',')
        line_count = 0
        for row in csv_reader:
            if line_count == 0:
                print(f'Column names are {", ".join(row)}')
                line_count += 1
            else:
                data_store.append(row)
                print(f'{row[0]}, {row[1]}, {row[2]}, {row[3]}, {row[4]}')
                line_count += 1
        print(f'Read {line_count} lines.')

    return data_store

判断是否存在：

def is_exist(data_store, date):
    already_exist = False

    for row in data_store:
        if row[0] == date:
            return True

    return already_exist

最后，写文件和其它函数整合在一起如下：

if __name__ == '__main__':
    data_store = read_data_from_csv('covid-19.csv')
    valid_links = fetch_links()
    valid_links.reverse()

    with open('covid-19.csv', 'a') as covid_file:
        covid_writer = csv.writer(covid_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
        for link in valid_links:
            date = link.nextSibling.text
            if is_exist(data_store, date):
                continue

            content = fetch_content(link.attrs['href'])
            confirm_gd, confirm_gz, asymptomatic_gd, asymptomatic_gz = parse_data(content)
            covid_writer.writerow([link.nextSibling.text, confirm_gd, confirm_gz, asymptomatic_gd, asymptomatic_gz])

结果如下：

6 最后

愿大家健康快乐！

代码请看GitHub: https://github.com/LarryDpk/pkslow-samples/tree/master/python

References:

CSV:

https://docs.python.org/3/library/csv.html

https://realpython.com/python-csv/

BS4: https://www.crummy.com/software/BeautifulSoup/bs4/doc/

Libs:

$ pip3 show beautifulsoup4
Name: beautifulsoup4
Version: 4.11.1
Summary: Screen-scraping library
Home-page: https://www.crummy.com/software/BeautifulSoup/bs4/
Author: Leonard Richardson
Author-email: leonardr@segfault.org
License: MIT
Location: /usr/local/lib/python3.9/site-packages
Requires: soupsieve
Required-by: 

$ pip3 show lxml
Name: lxml
Version: 4.9.1
Summary: Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API.
Home-page: https://lxml.de/
Author: lxml dev team
Author-email: lxml-dev@lxml.de
License: BSD
Location: /usr/local/lib/python3.9/site-packages
Requires: 
Required-by: 

$ pip3 show requests
Name: requests
Version: 2.28.1
Summary: Python HTTP for Humans.
Home-page: https://requests.readthedocs.io
Author: Kenneth Reitz
Author-email: me@kennethreitz.org
License: Apache 2.0
Location: /usr/local/lib/python3.9/site-packages
Requires: certifi, charset-normalizer, idna, urllib3
Required-by: