How to Scrape COVID‑19 Data with Python and Visualize It on a Map

Introduction

The article starts by questioning a statement made by a Chinese academic about vaccine trials and proposes to verify it with data using Python.

Required Libraries

Data acquisition : requests, lxml, json, openpyxl.

Data visualization : pandas, pyecharts.

pip install requests lxml json openpyxl pandas pyecharts

Data Acquisition

The target URL is https://voice.baidu.com/act/newpneumonia/newpneumonia. After sending a GET request with a custom User‑Agent, the HTML is parsed with lxml.etree and the embedded JSON is extracted via XPath.

import requests
from lxml import etree
import json
import openpyxl

url = 'https://voice.baidu.com/act/newpneumonia/newpneumonia'
headers = {"User-Agent": "your_user_agent"}
response = requests.get(url=url, headers=headers).text
html = etree.HTML(response)
json_text = html.xpath('//script[@type="application/json"]/text()')[0]
result = json.loads(json_text)["component"][0]["caseList"]

Saving to Excel

The extracted fields (province, confirmed, deaths, cured) are written into an Excel workbook.

wb = openpyxl.Workbook()
ws = wb.active
ws.title = "国内疫情"
ws.append(["省份", "累计确诊", "死亡", "治愈"])
for line in result:
    line_name = [line["area"], line["confirmed"], line["died"], line["crued"]]
    line_name = [0 if v == '' else v for v in line_name]
    ws.append(line_name)
wb.save('./china.xlsx')

Data Visualization

Using pandas to read the Excel file and pyecharts Map to draw three choropleth maps (confirmed, deaths, cured) of China.

import pandas as pd
from pyecharts.charts import Map, Page
from pyecharts import options as opts

pd.set_option('display.unicode.ambiguous_as_wide', True)
pd.set_option('display.unicode.east_asian_width', True)

df = pd.read_excel('china.xlsx')
province = df['省份'].tolist()
confirmed = df['累计确诊'].tolist()
deaths = df['死亡'].tolist()
cured = df['治愈'].tolist()

cured_map = (Map().add("治愈", [list(z) for z in zip(province, cured)], "china")
               .set_global_opts(title_opts=opts.TitleOpts(),
                                visualmap_opts=opts.VisualMapOpts(max_=200)))

confirmed_map = (Map().add("累计确诊", [list(z) for z in zip(province, confirmed)], "china")
                  .set_global_opts(title_opts=opts.TitleOpts(),
                                   visualmap_opts=opts.VisualMapOpts(max_=200)))

deaths_map = (Map().add("死亡", [list(z) for z in zip(province, deaths)], "china")
                .set_global_opts(title_opts=opts.TitleOpts(),
                                 visualmap_opts=opts.VisualMapOpts(max_=200)))

page = Page(layout=Page.DraggablePageLayout)
page.add(confirmed_map, deaths_map, cured_map)
page.render()

After rendering, render.html can be further adjusted and saved as my_test.html using Page.save_resize_html with a configuration JSON.

Conclusion

The guide demonstrates the complete workflow from crawling COVID‑19 statistics, storing them in Excel, to visualizing the data on a Chinese map with pyecharts, providing a practical example of Python web scraping and data visualization.