疫情数据源
网站上都可以看到当时的总数，当天增加数，但增加数有时只是一部分，如果看最近10天20天数据，比较容易掌握趋势和判断严重程度。
中国数据，代码里包含url ， 其中latest=1 为最新数据，0则为历史数据
分析代码如下：
import pandas as pd
import requests
import time
url = 'https://lab.isaaclin.cn/nCoV/api/area?latest=0'
r = requests.request('GET', url)
data = r.json()
df = pd.DataFrame.from_records(data['results'])
数据结构如下：
df.info()
<class ‘pandas.core.frame.DataFrame’>
RangeIndex: 253 entries, 0 to 252
Data columns (total 17 columns):
locationId 253 non-null int64
continentName 253 non-null object
continentEnglishName 253 non-null object
countryName 253 non-null object
countryEnglishName 220 non-null object
provinceName 253 non-null object
provinceEnglishName 220 non-null object
provinceShortName 253 non-null object
currentConfirmedCount 253 non-null int64
confirmedCount 253 non-null int64
suspectedCount 253 non-null int64
curedCount 253 non-null int64
deadCount 253 non-null int64
comment 252 non-null object
cities 34 non-null object
updateTime 253 non-null int64
dtypes: int64(7), object(10)
memory usage: 33.7+ KB
欧洲CDC 网站下载信息
下载json 文件，取名为corid2019.json，或者直接在程序中读取，见另篇博文欧洲cdc 疫情数据分析：
加拿大数据
https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html#a1
import pandas as pd
df = pd.read_csv('covid19.csv')
print(df[['pruid','prname','numconf','numdeaths','date']])
数据结构是：
df.info()
<class ‘pandas.core.frame.DataFrame’>
RangeIndex: 627 entries, 0 to 626
Data columns (total 14 columns):
pruid 627 non-null int64
prname 627 non-null object
prnameFR 627 non-null object
date 627 non-null object
numconf 627 non-null int64
numprob 627 non-null int64
numdeaths 619 non-null float64
numtotal 627 non-null int64
numtested 558 non-null float64
numrecover 30 non-null float64
percentrecover 28 non-null float64
ratetested 0 non-null float64
numtoday 612 non-null float64
percentoday 612 non-null float64
dtypes: float64(7), int64(4), object(3)
memory usage: 68.7+ KB
美国的数据
https://coronavirus.1point3acres.com/ 好像提供数据，但要获得申请，我没得到信息
疫情数据源
网站上都可以看到当时的总数，当天增加数，但增加数有时只是一部分，如果看最近10天20天数据，比较容易掌握趋势和判断严重程度。
中国数据，代码里包含url ， 其中latest=1 为最新数据，0则为历史数据
分析代码如下：
import pandas as pd
import requests
import time
url = 'https://lab.isaaclin.cn/nCoV/api/area?latest=0'
r = requests.request('GET', url)
data = r.json()
df = pd.DataFrame.from_records(data['results'])
数据结构如下：
df.info()
<class ‘pandas.core.frame.DataFrame’>
RangeIndex: 253 entries, 0 to 252
Data columns (total 17 columns):
locationId 253 non-null int64
continentName 253 non-null object
continentEnglishName 253 non-null object
countryName 253 non-null object
countryEnglishName 220 non-null object
provinceName 253 non-null object
provinceEnglishName 220 non-null object
provinceShortName 253 non-null object
currentConfirmedCount 253 non-null int64
confirmedCount 253 non-null int64
suspectedCount 253 non-null int64
curedCount 253 non-null int64
deadCount 253 non-null int64
comment 252 non-null object
cities 34 non-null object
updateTime 253 non-null int64
dtypes: int64(7), object(10)
memory usage: 33.7+ KB
欧洲CDC 网站下载信息
下载json 文件，取名为corid2019.json，或者直接在程序中读取，见另篇博文欧洲cdc 疫情数据分析：
加拿大数据
https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html#a1
import pandas as pd
df = pd.read_csv('covid19.csv')
print(df[['pruid','prname','numconf','numdeaths','date']])
数据结构是：
df.info()
<class ‘pandas.core.frame.DataFrame’>
RangeIndex: 627 entries, 0 to 626
Data columns (total 14 columns):
pruid 627 non-null int64
prname 627 non-null object
prnameFR 627 non-null object
date 627 non-null object
numconf 627 non-null int64
numprob 627 non-null int64
numdeaths 619 non-null float64
numtotal 627 non-null int64
numtested 558 non-null float64
numrecover 30 non-null float64
percentrecover 28 non-null float64
ratetested 0 non-null float64
numtoday 612 non-null float64
percentoday 612 non-null float64
dtypes: float64(7), int64(4), object(3)
memory usage: 68.7+ KB
美国的数据
https://coronavirus.1point3acres.com/ 好像提供数据，但要获得申请，我没得到信息
https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 这个实时显示疫情数据
https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 这个实时显示疫情数据