参考
数据可视化pyecharts使用 python爬虫之全国疫情数据可视化 利用Python实现新冠肺炎疫情数据可视化 python爬虫:新冠肺炎疫情累计确诊数据爬行和数据可视化 用pyecharts画地图
效果
疫情可视化数据地图
实现疫情可视化地图python代码
from datetime import date import requests from pyecharts import options as opts from pyecharts.charts import Map from pyecharts.globals import ThemeType update_date = date.today() # 1.疫情接口 url = "https://lab.isaaclin.cn/nCoV/api/area" # 2.向url发送请求，并将获得的数据转换为json格式 resultJson = requests.get(url).json() # 3.定义列表存储的最终结果 province_data = [] # 4.只取国内数据 for item in resultJson['results']: if item['countryName'] == "中国": province_data.append( [ item['provinceShortName'], item['confirmedCount'] ] ) c = ( Map(init_opts=opts.InitOpts(theme=ThemeType.DARK)) .add("全国确诊人数", province_data, "china", is_map_symbol_show=False) .set_global_opts( title_opts=opts.TitleOpts( title="新冠状病毒全国疫情地图", subtitle
=
"更新日期:{}"
.
format
(update_date
)
,
)
,
# 视觉映射配置项 visualmap_opts
=opts
.VisualMapOpts
( is_show
=
True
,
# 是否显示 min_
=
0
,
# 左下角刻度最小值 max_
=
2000
)
)
.render
(
"全国疫情可视化.html"
)
)
生成HTML文件
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Awesome-pyecharts</title>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/echarts.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/maps/china.js"></script>
</head>
<body>
<div id="fe5046e1bcd04c87ba52713eceae039c" class="chart-container" style="width:900px; height:500px;"></div>
<script>
var chart_fe5046e1bcd04c87ba52713eceae039c = echarts.init(
document.getElementById('fe5046e1bcd04c87ba52713eceae039c'), 'dark', {
renderer: 'canvas'});
var option_fe5046e1bcd04c87ba52713eceae039c = {
"animation": true,
"animationThreshold": 2000,
"animationDuration": 1000,
"animationEasing": "cubicOut",
"animationDelay": 0,
"animationDurationUpdate": 300,
"animationEasingUpdate": "cubicOut",
"animationDelayUpdate": 0,
"series": [
{
"type": "map",
"name": "\u5168\u56fd\u786e\u8bca\u4eba\u6570",
"label": {
"show": true,
"position": "top",
"margin": 8
},
"mapType": "china",
"data": [
{
"name": "\u5317\u4eac",
"value": 2191
},
{
"name": "\u4e2d\u56fd",
"value": 699067
},
{
"name": "\u53f0\u6e7e",
"value": 150808
},
{
"name": "\u9ed1\u9f99\u6c5f",
"value": 2952
},
{
"name": "\u4e0a\u6d77",
"value": 59070
},
{
"name": "\u9752\u6d77",
"value": 95
},
{
"name": "\u6d59\u6c5f",
"value": 3111
},
{
"name": "\u6c5f\u82cf",
"value": 2206
},
{
"name": "\u4e91\u5357",
"value": 2119
},
{
"name": "\u798f\u5efa",
"value": 3022
},
{
"name": "\u5185\u8499\u53e4",
"value": 1751
},
{
"name": "\u8fbd\u5b81",
"value": 1646
},
{
"name": "\u6c5f\u897f",
"value": 1371
},
{
"name": "\u6cb3\u5357",
"value": 2907
},
{
"name": "\u65b0\u7586",
"value": 1007
},
{
"name": "\u6e56\u5357",
"value": 1385
},
{
"name": "\u5c71\u4e1c",
"value": 2722
},
{
"name": "\u9999\u6e2f",
"value": 330725
},
{
"name": "\u5409\u6797",
"value": 40242
},
{
"name": "\u5929\u6d25",
"value": 1804
},
{
"name": "\u5e7f\u4e1c",
"value": 7082
},
{
"name": "\u9655\u897f",
"value": 3277
},
{
"name": "\u56db\u5ddd",
"value": 2057
},
{
"name": "\u6cb3\u5317",
"value": 1998
},
{
"name": "\u5b89\u5fbd",
"value": 1065
},
{
"name": "\u5e7f\u897f",
"value": 1587
},
{
"name": "\u5c71\u897f",
"value": 418
},
{
"name": "\u6d77\u5357",
"value": 288
},
{
"name": "\u91cd\u5e86",
"value": 698
},
{
"name": "\u6e56\u5317",
"value": 68398
},
{
"name": "\u8d35\u5dde",
"value": 179
},
{
"name": "\u7518\u8083",
"value": 681
},
{
"name": "\u6fb3\u95e8",
"value": 82
},
{
"name": "\u5b81\u590f",
"value": 122
},
{
"name": "\u897f\u85cf",
"value": 1
}
],
"roam": true,
"aspectScale": 0.75,
"nameProperty": "name",
"selectedMode": false,
"zoom": 1,
"mapValueCalculation": "sum",
"showLegendSymbol": false,
"emphasis": {
}
}
],
"legend": [
{
"data": [
"\u5168\u56fd\u786e\u8bca\u4eba\u6570"
],
"selected": {
"\u5168\u56fd\u786e\u8bca\u4eba\u6570": true
},
"show": true,
"padding": 5,
"itemGap": 10,
"itemWidth": 25,
"itemHeight": 14
}
],
"tooltip": {
"show": true,
"trigger": "item",
"triggerOn": "mousemove|click",
"axisPointer": {
"type": "line"
},
"showContent": true,
"alwaysShowContent": false,
"showDelay": 0,
"hideDelay": 100,
"textStyle": {
"fontSize": 14
},
"borderWidth": 0,
"padding": 5
},
"title": [
{
"text": "\u65b0\u51a0\u72b6\u75c5\u6bd2\u5168\u56fd\u75ab\u60c5\u5730\u56fe",
"subtext": "\u66f4\u65b0\u65e5\u671f:2022-05-02",
"padding": 5,
"itemGap": 10
}
],
"visualMap": {
"show": true,
"type": "continuous",
"min": 0,
"max": 2000,
"inRange": {
"color": [
"#50a3ba",
"#eac763",
"#d94e5d"
]
},
"calculable": true,
"inverse": false,
"splitNumber": 5,
"orient": "vertical",
"showLabel": true,
"itemWidth": 20,
"itemHeight": 140,
"borderWidth": 0
}
};
chart_fe5046e1bcd04c87ba52713eceae039c.setOption(option_fe5046e1bcd04c87ba52713eceae039c);
</script>
</body>
</html>
世界地图
from pyecharts.charts import Map
from pyecharts import options as opts
import random
country = ['China', 'Canada', 'France', 'Japan', 'Russia', 'USA']
data_world = [(i, random.randint(100, 200)) for i in country]
world = (
Map()
.add('', # 此处没取名，所以空着
data_world, # 数据
'world') # 地图类型
.set_global_opts(
title_opts=opts.TitleOpts(title='World Map'),
visualmap_opts=opts.VisualMapOpts(
max_=200,
min_=100,
is_piecewise=True) # 定义图例为分段型，默认为连续的图例
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.render(path='世界地图.html')
)
[('China', 187), ('Canada', 111), ('France', 161), ('Japan', 199), ('Russia', 109), ('USA', 118)]
中国省级地图
from pyecharts.charts import Map
from pyecharts import options as opts
import random
province = ['广东', '湖北', '湖南', '四川', '重庆', '黑龙江', '浙江', '山西']
data_province = [(i, random.randint(100, 200)) for i in province]
print(data_province)
china_province = (
Map()
.add('', data_province, 'china')
.set_global_opts(
title_opts=opts.TitleOpts(title='Provinces of China'),
visualmap_opts=opts.VisualMapOpts(
min_=100,
max_=200,
is_piecewise=True)
)
.render(path='中国省级地图.html')
)
[('广东', 167), ('湖北', 161), ('湖南', 140), ('四川', 130), ('重庆', 130), ('黑龙江', 158), ('浙江', 113), ('山西', 122)]
中国市级地图
from pyecharts.charts import Map
from pyecharts import options as opts
import random
city = ['北京', '天津', '上海', '广州', '阿拉善盟', '株洲']
data_city = [(i, random.randint(100, 200)) for i in city]
print(data_city)
china_city = (
Map()
.add(
"",
data_city,
"china-cities",
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="中国市级地图"),
visualmap_opts=opts.VisualMapOpts(
min_=100,
max_=200,
is_piecewise=True
),
)
.render("中国地级市地图.html")
)
[('北京', 169), ('天津', 170), ('上海', 183), ('广州', 109), ('阿拉善盟', 124), ('株洲', 199)]
某省市级地图
from pyecharts.charts import Map
from pyecharts import options as opts
import random
prov_city = ['长沙市', '株洲市', '湘潭市', '衡阳市']
data_prov_city = [(i, random.randint(100, 200)) for i in prov_city]
print(data_prov_city)
province_city = (
Map()
.add("",
data_prov_city,
"湖南")
.set_global_opts(
title_opts=opts.TitleOpts(title="某省地级市地图"),
visualmap_opts=opts.VisualMapOpts(
min_=100,
max_=200,
is_piecewise=True
)
)
.render(path="某省地级市地图.html")
)
[('长沙市', 107), ('株洲市', 113), ('湘潭市', 125), ('衡阳市', 115)]
某市县级地图
from pyecharts.charts import Map
from pyecharts import options as opts
counties = (
Map()
.add(
"",
[['攸县', 1]],
"株洲",
label_opts=opts.LabelOpts(is_show=False)
)
.set_global_opts(
title_opts=opts.TitleOpts(title="中国某市区县地图"),
visualmap_opts=opts.VisualMapOpts(),
)
.render("某市的区县地图.html")
)