利用Python爬取新冠肺炎疫情实时数据，Pyecharts画2019-nCoV疫情地图

地图绘制

数据源

腾讯疫情实时追踪
网站结构比较简单，可以直接获取json格式的数据

抓取每个城市的当前感染数据

• 导入相关模块

import time
import json
import requests
from datetime import datetime
import pandas as pd 
import numpy as np

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• 定义抓取方法，数据预处理

def catch_cityinfo():
    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=wuwei_ww_area_counts&callback=&_=%d'%int(time.time()*1000)
    city_data = requests.get(url=url).json()['data']
    city_data = json.loads(city_data)
    #返回dataframe格式
    city_data = pd.DataFrame(city_data)  
    return city_data

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• 获取城市感染数据

citydata = catch_cityinfo()
citydata.head()

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绘制国内疫情感染地图

from pyecharts import options as opts
from pyecharts.charts import *
from pyecharts.globals import ThemeType

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area_map = Map()
area_map .add("",[list(z) for z in zip(list(area_data["area"]), list(area_data["confirm"]))], "china",is_map_symbol_show=False


    
)
area_map.set_global_opts(title_opts=opts.TitleOpts(title="Map-疫情地图"),visualmap_opts=opts.VisualMapOpts(max_=3000,is_piecewise=True,
                pieces=[
                 {"min": "0", "max": "10", "label": "0-10", "color": "#FFC0CB"},
                 {"min": "10", "max": "100", "label": "10-100", "color": "#F08080"},
                 {"min": "100", "max": "1000", "label": "100-1000", "color": "#FF0000"},
                 {"min": "1000", "max": "10000", "label": ">1000", "color": "#8b0000"}
             ]))
area_map.render_notebook()

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绘制世界疫情感染地图

world_data = citydata.groupby("country")["confirm"].sum().reset_index()
world_data.columns = ["country","confirm"]
# 这里有坑，中文地图不显示，要把国家名字替换为英文
world_name = pd.read_excel("世界各国中英文对照.xlsx")
world_data_ = pd.merge(world_data,world_name,left_on ="country",right_on = "中文",how="inner")
world_data_.head()

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world_map = Map()
world_map.add("",[list(z) for


    
 z in zip(list(world_data_["英文"]), list(world_data_["confirm"]))], "world",is_map_symbol_show=False)
world_map.set_global_opts(title_opts=opts.TitleOpts(title="2019_nCoV-世界疫情地图"),visualmap_opts=opts.VisualMapOpts(max_=5),legend_opts=opts.LegendOpts(is_show=False))
world_map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
world_map.render_notebook()

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疫情趋势

抓取每日确诊数据

def catch_daily():
    url = 'https://view.inews.qq.com/g2/getOnsInfo?name=wuwei_ww_cn_day_counts&callback=&_=%d'%int(time.time()*1000)
    data = json.loads(requests.get(url=url).json()['data'])
    data.sort(key=lambda x:x['date'])
    return data

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daily_data = pd.DataFrame(catch_daily())
daily_data.head()

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绘制治愈与死亡趋势

line = Line()
line.add_xaxis(list(daily_data["date"]))
line.add_yaxis("heal",list(daily_data["heal"]))
line.add_yaxis("dead", list(daily_data["dead"]))


    

line.set_global_opts(title_opts=opts.TitleOpts(title="Line-治愈与死亡趋势"))
line.render_notebook()

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绘制每日确诊与疑似病例

def bar() -> Bar:
    c = (
        Bar({"theme": ThemeType.MACARONS})
        .add_xaxis(list(daily_data["date"]))
        .add_yaxis("confirm", list(daily_data["confirm"]))
        .add_yaxis("suspect", list(daily_data["suspect"]))
        .set_global_opts(
            title_opts={"text": "Bar-患病人数增长趋势"}
        )
    )
    return c
bar().render_notebook()

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