Create Interactive COVID‑19 Maps and Timelines with Python pyecharts

Hello, I am a Python enthusiast sharing a practical visualization project.

Preface

A student asked for help with a visualization assignment, which led to creating a carousel of maps. The article references Huang Wei's work on cool migration and carousel charts using pyecharts.

Data Source

The data comes from Baidu's real‑time COVID‑19 big‑data report.

Implementation Process

Newly Confirmed Cases

Code to generate a map of newly confirmed cases in Shaanxi province:

from pyecharts.charts import Map, Timeline
from pyecharts import options as opts

# 准数据
shanxi_city = ["西安市", "延安市", "咸阳市", "渭南市", "安康市", "汉中市", "宝鸡市", "铜川市", "商洛市", "榆林市", "韩城市", "杨凌示范区"]
shanxi_data = [46, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

# 绘制陕西疫情地图
map = (
    Map()
    .add('陕西省', [(i, j) for i, j in zip(shanxi_city, shanxi_data)], '陕西')
    .set_global_opts(title_opts=opts.TitleOpts(title='陕西省新增感染病例疫情图'), visualmap_opts=opts.VisualMapOpts(max_=50, is_piecewise=True))
)

# 渲染数据
map.render('陕西省新增感染病例疫情图.html')

Resulting map:

Existing Cases

Map of current cases (image only):

Cumulative Cases

Map of cumulative cases (image only):

Recovered Cases

Map of recovered cases (image only):

Deaths

Map of death cases (image only):

Carousel (Timeline)

Code to combine the five maps into an interactive timeline carousel:

from pyecharts.charts import Map, Timeline
from pyecharts import options as opts

# 1. 准数据
shanxi_city = ["西安市", "延安市", "咸阳市", "渭南市", "安康市", "汉中市", "宝鸡市", "铜川市", "商洛市", "榆林市", "韩城市", "杨凌示范区"]
xinzeng = [46, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
xianyou = [1747, 13, 11, 1, 0, 0, 0, 0, 0, 0, 0, 0]
leiji = [2094, 21, 31, 18, 26, 26, 13, 8, 7, 3, 1, 1]
zhiyu = [304, 8, 20, 17, 26, 26, 13, 8, 7, 3, 1, 1]
siwang = [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

# 2. 绘制新增疫情地图：格式一
map1 = (
    Map(init_opts=opts.InitOpts(width="700px", height="300px", theme="blue"))
    .add('新增病例', [(i, j) for i, j in zip(shanxi_city, xinzeng)], '陕西')
    .set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=50))
)

# 3. 绘制现有疫情地图：格式二
map2 = (
    Map()
    .add('现有病例', [(i, j) for i, j in zip(shanxi_city, xianyou)], '陕西')
    .set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=1750, is_piecewise=True))
)

# 4. 绘制累计疫情地图：格式三
map3 = (
    Map()
    .add('累计病例', [(i, j) for i, j in zip(shanxi_city, leiji)], '陕西')
    .set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=2100, is_piecewise=True))
)

# 5. 绘制治愈疫情地图：格式四
map4 = (
    Map()
    .add('治愈病例', [(i, j) for i, j in zip(shanxi_city, zhiyu)], '陕西')
    .set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=310, is_piecewise=True))
)

# 6. 绘制死亡疫情地图：格式五
map5 = (
    Map()
    .add('死亡病例', [(i, j) for i, j in zip(shanxi_city, siwang)], '陕西')
    .set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=3, is_piecewise=True))
)

# 7. 创建组合类对象
timeline = Timeline(init_opts=opts.InitOpts(width='720px', height='350px'))

# 8. 在组合对象中添加需要组合的图表对象
timeline.add(chart=map1, time_point="陕西省新增病例疫情图")
 timeline.add(chart=map2, time_point="陕西省现有病例疫情图")
 timeline.add(chart=map3, time_point="陕西省累计病例疫情图")
 timeline.add(chart=map4, time_point="陕西省治愈病例疫情图")
 timeline.add(chart=map5, time_point="陕西省死亡病例疫情图")
 timeline.add_schema(is_auto_play=True, play_interval=2000)

# 9. 渲染数据
timeline.render('陕西省疫情轮播图.html')

Resulting carousel image:

Summary

This article demonstrates how to obtain COVID‑19 data from Baidu's real‑time report and use Python's pyecharts library to create province‑level maps and an animated timeline carousel, providing complete code and visual results for learners.