【世界加油⛽️】基于Pyecharts的疫情数据可视化～
更新数据
Python系列教程,免费获取,遇到bug及时反馈,讨论交流可加扣裙<60 61 15 02 7>
由于目前时间序列数据越来越大了，通过requests请求实在太慢 & 不稳定，现改用git来更新数据；
首次使用请先执行以下命令，数据会下载到/home/kesci/work/covid-19/json/路径下（第一次是更新全量，所以会很慢，耐心的等待☕️）；
# 建立文件夹 covid-19 mkdir covid-19 cd covid-19 git init # 指定远程仓库地址 git remote add -f origin https://github.com/BlankerL/DXY-COVID-19-Data.git # 设置允许克隆子项目 git config core.sparsecheckout true # 子项目路径 echo json >> .git/info/sparse-checkout # 通过pull拉取代码 git pull origin master
In [1]:
%cd covid-19 !git init # 设置允许克隆子项目 !git config core.sparsecheckout true # 子项目路径 !echo json >> .git/info/sparse-checkout # 拉取数据 !git pull origin master
/home/kesci/work/covid-19 Reinitialized existing Git repository in /home/kesci/work/covid-19/.git/ remote: Enumerating objects: 307, done. remote: Counting objects: 100% (307/307), done. remote: Compressing objects: 100% (22/22), done. error: RPC failed; curl 18 transfer closed with outstanding read data remaining fatal: The remote end hung up unexpectedly fatal: early EOF fatal: index-pack failed
In [2]:
import warnings
warnings.filterwarnings("ignore")
In [3]:
import requests import pyecharts from pyecharts.charts import * from pyecharts import options as opts from pyecharts.commons.utils import JsCode from pyecharts.datasets import register_url import pandas as pd from datetime import date import datetime from dateutil import tz import json
数据来源
-
通过https://lab.isaaclin.cn/nCoV/api/area接口获取最新疫情数据；
-
通过https://lab.isaaclin.cn/nCoV/api/area?latest=0接口获取时间序列疫情数据；
-
通过http://lab.isaaclin.cn/nCoV/api/overall?latest=0接口获取全国时间序列疫情数据；
详细的API使用介绍可直接访问https://lab.isaaclin.cn/nCoV/zh查看。
In [4]:
update_date = datetime.datetime.now(tz=tz.gettz(
'Asia/Shanghai')).strftime('%Y-%m-%d %H:%M:%S')
# 最新数据
with open('/home/kesci/work/covid-19/json/DXYArea.json', 'r') as f:
data = json.load(f)
# 全球时间序列数据
with open('/home/kesci/work/covid-19/json/DXYArea-TimeSeries.json', 'r') as f:
area_data_timeline = json.load(f)
# 全国时间序列数据
with open('/home/kesci/work/covid-19/json/DXYOverall-TimeSeries.json', 'r') as f:
all_data_timeline = json.load(f)
如果要继续沿用requests请将替换成
# 生成更新时间
update_date = datetime.datetime.now(tz=tz.gettz('Asia/Shanghai')).strftime('%Y-%m-%d %H:%M:%S')
# 建立连接太多时会报错
# 同一个会话下请求
sess = requests.session()
# 数据来源于 Github DXY-COVID-19-Data， 原作者为 Isaac Lin
url = 'https://github.com/BlankerL/DXY-COVID-19-Data/raw/master/json/DXYArea.json'
data = sess.get(url, verify=False).json()
# 获取时间序列数据
# 细分到城市
# 数据量较大，大约1-3mins
area_data_timeline = sess.get('http://raw.githubusercontent.com/BlankerL/DXY-COVID-19-Data/master/json/DXYArea-TimeSeries.json',
verify=False).json()
# 全国数据
all_data_timeline = sess.get('http://github.com/BlankerL/DXY-COVID-19-Data/raw/master/json/DXYOverall-TimeSeries.json',
verify=False).json()
最新疫情数据
海内外疫情对比
In [5]:
oversea_data = {'currentConfirmedCount': {'国内': 0, '海外': 0},
'confirmedCount': {'国内': 0, '海外': 0},
'curedCount': {'国内': 0, '海外': 0},
'deadCount': {'国内': 0, '海外': 0}}
for item in data['results']:
if item['provinceEnglishName'] == 'China':
oversea_data['currentConfirmedCount']['国内'] += item['currentConfirmedCount']
oversea_data['confirmedCount']['国内'] += item['confirmedCount']
oversea_data['curedCount']['国内'] += item['curedCount']
oversea_data['deadCount']['国内'] += item['deadCount']
elif item['countryEnglishName'] != 'China':
oversea_data['currentConfirmedCount']['海外'] += item['currentConfirmedCount']
oversea_data['confirmedCount']['海外'] += item['confirmedCount']
oversea_data['curedCount']['海外'] += item['curedCount']
oversea_data['deadCount']['海外'] += item['deadCount']
fn = """
function(params) {
if(params.name == '海外')
return '\\n\\n\\n' + params.name + ' : ' + params.value;
return params.seriesName + '\\n' + params.name + ' : ' + params.value;
}
"""
def new_label_opts():
return opts.LabelOpts(formatter=JsCode(fn), position="center")
pie = (Pie(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add(
"当前确诊",
[(x, y) for x, y in oversea_data['currentConfirmedCount'].items()],
center=["30%", "30%"],
radius=[60, 90],
label_opts=new_label_opts(),
)
.add(
"累计确诊",
[(x, y) for x, y in oversea_data['confirmedCount'].items()],
center=["70%", "30%"],
radius=[60, 90],
label_opts=new_label_opts(),
)
.add(
"治愈",
[(x, y) for x, y in oversea_data['curedCount'].items()],
center=["30%", "75%"],
radius=[60, 90],
label_opts=new_label_opts(),
)
.add(
"死亡",
[(x, y) for x, y in oversea_data['deadCount'].items()],
center=["70%", "75%"],
radius=[60, 90],
label_opts=new_label_opts(),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="海内外疫情数据对比",
subtitle="更新时间：{}".format(update_date)),
legend_opts=opts.LegendOpts(is_show=True),)
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a}
{b}: {c} ({d}%)"
),)
)
pie.render_notebook()
Out[5]:
全球疫情地图
In [6]:
oversea_confirm = []
for item in data['results']:
if item['provinceEnglishName']:
oversea_confirm.append((item['provinceEnglishName'].replace('United States of America', 'United States')
.replace('United Kiongdom', 'United Kingdom'),
item['currentConfirmedCount']))
_map = (
Map(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add("当前确诊人数", oversea_confirm, "world", is_map_symbol_show=False, is_roam=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全球疫情地图",
subtitle="更新时间：{}".format(update_date)),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=True, max_=10000,
is_piecewise=False,
range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000']),
graphic_opts=[
opts.GraphicGroup(
graphic_item=opts.GraphicItem(
bounding="raw",
right=150,
bottom=50,
z=100,
),
children=[
opts.GraphicRect(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_shape_opts=opts.GraphicShapeOpts(
width=200, height=50
),
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="rgba(0,0,0,0.3)"
),
),
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
text=JsCode("['钻石号邮轮', '当前确诊人数：{}人'].join('\\n')"
.format(dict(oversea_confirm)['Diamond Princess Cruise Ship'])),
font="bold 16px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#fff"
),
),
),
],
)
],
)
)
_map.render_notebook()
Out[6]:
全国疫情地图
In [7]:
cofirm, currentCofirm, cured, dead = [], [], [], []
for item in data['results']:
if item['countryName'] == '中国':
cofirm.append((item['provinceShortName'], item['confirmedCount']))
currentCofirm.append(
(item['provinceShortName'],
item['currentConfirmedCount']))
cured.append((item['provinceShortName'], item['curedCount']))
dead.append((item['provinceShortName'], item['deadCount']))
tab = Tab()
_map = (
Map(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add("累计确诊人数", cofirm, "china", is_map_symbol_show=False, is_roam=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国疫情地图",
subtitle="更新时间：{}".format(update_date)),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=True, max_=1000,
is_piecewise=False,
range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000'])
)
)
tab.add(_map, '累计确诊')
_map = (
Map(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add("当前确诊人数", currentCofirm, "china", is_map_symbol_show=False, is_roam=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国疫情地图",
subtitle="更新时间：{}".format(update_date)),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=True, max_=100,
is_piecewise=False,
range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000'])
)
)
tab.add(_map, '当前确诊')
_map = (
Map(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add("治愈人数", cured, "china", is_map_symbol_show=False, is_roam=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国疫情地图",
subtitle="更新时间：{}".format(update_date)),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=True, max_=1000,
is_piecewise=False,
range_color=['#FFFFE0', 'green'])
)
)
tab.add(_map, '治愈')
_map = (
Map(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add("死亡人数", dead, "china", is_map_symbol_show=False, is_roam=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国疫情地图",
subtitle="更新时间：{}".format(update_date)),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=True, max_=50,
is_piecewise=False,
range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000'])
)
)
tab.add(_map, '死亡')
tab.render_notebook()
Out[7]:
累计确诊当前确诊治愈死亡
境外输入病例
In [8]:
out_in_data = []
for item in data['results']:
if item['cities']:
for city in item['cities']:
if city['cityName'] == '境外输入':
out_in_data.append(
(item['provinceShortName'], city['confirmedCount']))
out_in_data = sorted(out_in_data, key=lambda x: x[1], reverse=True)
bar = (
Bar(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add_xaxis([x[0] for x in out_in_data if x[1] > 0])
.add_yaxis("境外输入", [x[1] for x in out_in_data if x[1] > 0],
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: 'rgb(255,99,71)'
}, {
offset: 1,
color: 'rgb(32,178,170)'
}])""")))
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='top',
font_style='italic'))
.set_global_opts(
title_opts=opts.TitleOpts(title="境外输入病例",
subtitle="更新时间：{}".format(update_date)),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
legend_opts=opts.LegendOpts(is_show=False),
)
)
bar.render_notebook()
Out[8]:
湖北省内确诊情况
In [9]:
for item in data['results']:
if item['provinceShortName'] == '湖北':
hubei_data = item['cities']
bar = (
Bar(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add_xaxis([x['cityName'] for x in hubei_data])
.add_yaxis("治愈", [x['curedCount'] for x in hubei_data], stack="stack1",
itemstyle_opts=opts.ItemStyleOpts(
border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: 'rgb(255,215,0)'
}, {
offset: 1,
color: 'rgb(32,178,170)'
}])""")))
.add_yaxis("当前确诊", [x['currentConfirmedCount'] for x in hubei_data], stack="stack1",
itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: 'rgb(255,99,71)'
}, {
offset: 1,
color: 'rgb(255,215,0)'
}])""")))
.add_yaxis("死亡", [x['deadCount'] for x in hubei_data], stack="stack1",
itemstyle_opts=opts.ItemStyleOpts(border_color='rgb(220,220,220)',
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: 'rgb(128,0,0)'
}, {
offset: 1,
color: 'rgb(255,99,71)'
}])""")))
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="湖北省内确诊情况",
subtitle="更新时间：{}".format(update_date)),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
legend_opts=opts.LegendOpts(is_show=True),
graphic_opts=[
opts.GraphicGroup(
graphic_item=opts.GraphicItem(
bounding="raw",
right=200,
top=120
),
children=[
opts.GraphicRect(
graphic_item=opts.GraphicItem(
left="center", top="center"
),
graphic_shape_opts=opts.GraphicShapeOpts(
width=200, height=60
),
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="rgba(0,0,0,0.3)"
),
),
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center", top="center", z=1
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
text=JsCode(
"['当前确诊人数：', '','累计确诊人数-死亡人数-治愈人数'].join('\\n')"),
font="bold 12px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#fff"
),
),
),
],
)
],
)
)
bar.render_notebook()
Out[9]:
全国疫情热力图
In [10]:
cities_data = []
for item in data['results']:
if item['countryName'] == '中国':
try:
cities_data.extend((item['cities']))
except TypeError:
pass
geo = (
Geo(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add_schema(maptype="china", zoom=2, center=[114.31, 30.52])
.add("当前确诊人数",
[(i['cityName'], i['currentConfirmedCount']) for i in cities_data
if i['cityName'] in pyecharts.datasets.COORDINATES.keys()],
type_='heatmap',
symbol_size=3,
progressive=50)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国疫情热力图",
subtitle="更新时间：{}".format(update_date),
pos_left='right'),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=True,
is_piecewise=False,
range_color=['blue', 'green', 'yellow', 'yellow', 'red'])
)
)
geo.render_notebook()
Out[10]:
时间序列数据
数据处理
因为各地疫情数据更新时间不一致且存在缺失情况，需先对数据进行处理。
In [11]:
def get_value(dic, key):
try:
return dic[key]
except KeyError:
return 0
def insert_data(to_update_date, to_update_area, dic, is_city):
if to_update_date in format_data:
if to_update_area in format_data[to_update_date]:
pass
else:
format_data[to_update_date][to_update_area] = {}
else:
format_data[to_update_date] = {}
format_data[to_update_date][to_update_area] = {}
format_data[to_update_date][to_update_area]['currentConfirmedCount'] = get_value(
dic, 'currentConfirmedCount')
format_data[to_update_date][to_update_area]['confirmedCount'] = get_value(
dic, 'confirmedCount')
format_data[to_update_date][to_update_area]['deadCount'] = get_value(
dic, 'deadCount')
format_data[to_update_date][to_update_area]['suspectedCount'] = get_value(
dic, 'suspectedCount')
format_data[to_update_date][to_update_area]['curedCount'] = get_value(
dic, 'curedCount')
format_data[to_update_date][to_update_area]['countryName'] = get_value(
dic, 'countryName')
format_data[to_update_date][to_update_area]['countryEnglishName'] = get_value(
dic, 'countryEnglishName')
# 用于区分区域层级
if is_city:
format_data[to_update_date][to_update_area]['is_city'] = 1
else:
format_data[to_update_date][to_update_area]['is_city'] = 0
format_data = {}
for item in area_data_timeline[::-1]:
to_update_date = date.fromtimestamp(item['updateTime']/1000)
to_update_area = item['provinceShortName']
insert_data(to_update_date, to_update_area, item, 0)
if 'cities' in item:
if item['cities']:
for city_data in item['cities']:
insert_data(
to_update_date,
city_data['cityName'],
city_data,
1)
for item in all_data_timeline[::-1]:
to_update_date = date.fromtimestamp(item['updateTime']/1000)
insert_data(to_update_date, '全国', item, 0)
time_range = list(format_data.keys())
In [12]:
def area_data(area_name='湖北', type_='confirmedCount',
get_total=True, date_list=time_range):
# 用于pyecharts获取时间序列数据
data_array = []
for day in date_list:
try:
data_array.append(format_data[day][area_name][type_])
except KeyError:
if day + datetime.timedelta(days=-1) in format_data:
if area_name in format_data[day + datetime.timedelta(days=-1)]:
# 当天未更新数据情况时，取前一天数据填充
data_array.append(
format_data[day + datetime.timedelta(days=-1)][area_name][type_])
else:
data_array.append(0)
else:
data_array.append(0)
# 返回每日新增数据
if not get_total:
data_array = [data_array[i+1] - data_array[i]
for i in range(len(data_array)-1)]
return data_array
全国/湖北/武汉疫情趋势折线图
In [13]:
data_type = {'当前确诊': 'currentConfirmedCount',
'累计确诊': 'confirmedCount',
'死亡病例': 'deadCount',
'治愈病例': 'curedCount'}
tab = Tab()
for key_, value_ in data_type.items():
line = (Line(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add_xaxis([day.strftime('%Y-%m-%d') for day in time_range])
.add_yaxis("全国", area_data('全国', value_), is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5,
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: 'rgb(255,99,71)'
}, {
offset: 1,
color: 'rgb(32,178,170)'
}])""")))
.add_yaxis("湖北", area_data('湖北', value_), is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0))
.add_yaxis("武汉", area_data('武汉', value_), is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0))
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="全国{}趋势图".format(key_),
subtitle="更新时间：{}".format(update_date)),
datazoom_opts=opts.DataZoomOpts(range_start=70, range_end=100),
xaxis_opts=opts.AxisOpts(
splitline_opts=opts.SplitLineOpts(
is_show=False)),
))
tab.add(line, key_)
tab.render_notebook()
Out[13]:
当前确诊累计确诊死亡病例治愈病例
全国疫情新增趋势
In [14]:
line = (Line(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add_xaxis([day.strftime('%Y-%m-%d') for day in time_range][1:])
.add_yaxis("全国", area_data('全国', get_total=False), is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5,
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: 'rgb(255,99,71)'
}, {
offset: 1,
color: 'rgb(32,178,170)'
}])""")),)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(
datazoom_opts=opts.DataZoomOpts(range_start=70, range_end=100),
title_opts=opts.TitleOpts(title="全国每日新增确证病例趋势图",
subtitle="更新时间：{}".format(update_date)),
xaxis_opts=opts.AxisOpts(
splitline_opts=opts.SplitLineOpts(is_show=False)),
legend_opts=opts.LegendOpts(is_show=False),
))
line.render_notebook()
Out[14]:
全国疑似病例趋势
In [15]:
line = (Line(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add_xaxis([day.strftime('%Y-%m-%d') for day in time_range][1:])
.add_yaxis("全国", area_data('全国', 'suspectedCount', get_total=True), is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.5,
color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,
[{
offset: 0,
color: 'rgb(255,99,71)'
}, {
offset: 1,
color: 'rgb(32,178,170)'
}])""")),)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(
datazoom_opts=opts.DataZoomOpts(range_start=70, range_end=100),
title_opts=opts.TitleOpts(title="全国疑似病例趋势图",
subtitle="更新时间：{}".format(update_date)),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(
splitline_opts=opts.SplitLineOpts(is_show=False)),
))
line.render_notebook()
Out[15]:
全国疫情蔓延趋势
In [16]:
tl = Timeline(init_opts=opts.InitOpts(theme='dark', width='1000px'))
tl.add_schema(axis_type='time', is_auto_play=True, is_timeline_show=False)
for day in time_range:
geo = (
Geo(init_opts=opts.InitOpts(theme='dark'))
.add_schema(maptype="china", zoom=1)
.add("当前确诊人数",
[(key_, value_['currentConfirmedCount']) for key_, value_, in format_data[day].items()
if key_ in pyecharts.datasets.COORDINATES.keys() and value_['is_city'] == 1],
type_='heatmap',
symbol_size=3,
progressive=50)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国疫情热力图【自动轮播】",
subtitle="更新时间：{}".format(update_date)),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(max_=50000, is_show=False,
is_piecewise=True,
pieces=[{"min": 50000},
{"min": 5000,
"max": 50000},
{"min": 500, "max": 5000},
{"min": 10, "max": 500},
{"max": 10}],
range_color=['blue', 'green', 'green', 'yellow', 'red']),
graphic_opts=[opts.GraphicGroup(
graphic_item=opts.GraphicItem(
rotation=JsCode(
"Math.PI / 4"),
bounding="raw",
right=110,
bottom=110,
z=100,
),
children=[
opts.GraphicRect(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_shape_opts=opts.GraphicShapeOpts(
width=400, height=50
),
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="rgba(0,0,0,0.3)"
),
),
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
text=day,
font="bold 26px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#fff"
),
),
),
],
)
],
)
)
tl.add(geo, day)
tl.render_notebook()
Out[16]:
海外疫情
新型冠状病毒海外主要国家确诊情况
- 在国内疫情得到有效控制的时候，海外地区却开始呈现爆发趋势。
In [17]:
def oversea_top(day=date.today(), top=10, chinese=True,
d_type='confirmedCount', china_involved=False):
if china_involved:
oversea_data = filter(
lambda x: x[0] != '全国' and x[1]['is_city'] == 0,
format_data[day].items())
else:
oversea_data = filter(lambda x: x[0] != '全国' and x[1]['is_city'] == 0 and x[1]['countryName'] != '中国',
format_data[day].items())
if top == 'all':
oversea_data = sorted(
oversea_data,
key=lambda x: x[1][d_type],
reverse=True)
else:
oversea_data = sorted(
oversea_data,
key=lambda x: x[1][d_type],
reverse=True)[
:top]
if chinese:
oversea_data = [(x[0], x[1][d_type]) for x in oversea_data]
else:
oversea_data = [(x[1]['countryEnglishName'], x[1][d_type]) for x in oversea_data]
return oversea_data
In [18]:
tl = Timeline(
init_opts=opts.InitOpts(
theme='dark',
width='1000px',
height='1000px'))
tl.add_schema(axis_type='time', is_timeline_show=True, is_rewind_play=True, is_inverse=True,
label_opts=opts.LabelOpts(is_show=False))
for day in time_range[::-3]:
oversea_data = oversea_top(day, top=20)[::-1]
if oversea_data:
bar = (
Bar(init_opts=opts.InitOpts(theme='dark'))
.add_xaxis([x[0] for x in oversea_data])
.add_yaxis("", [x[1] for x in oversea_data])
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='right',
font_style='italic'),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode("""new echarts.graphic.LinearGradient(1, 0, 0, 0,
[{
offset: 0,
color: 'rgb(255,99,71)'
}, {
offset: 1,
color: 'rgb(32,178,170)'
}])"""))
)
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒海外主要国家确诊情况",
subtitle="更新时间：{}".format(update_date)),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(
rotate=45)),
legend_opts=opts.LegendOpts(is_show=True),
graphic_opts=[opts.GraphicGroup(graphic_item=opts.GraphicItem(
rotation=JsCode("Math.PI / 4"),
bounding="raw",
right=110,
bottom=110,
z=100),
children=[
opts.GraphicRect(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_shape_opts=opts.GraphicShapeOpts(
width=400, height=50
),
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="rgba(0,0,0,0.3)"
),
),
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
text=day,
font="bold 26px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#fff"
),
),
),
],
)
],)
.reversal_axis()
)
tl.add(bar, day)
tl.render_notebook()
Out[18]:
新型冠状病毒海外主要国家死亡情况
In [19]:
tl = Timeline(
init_opts=opts.InitOpts(
theme='dark',
width='1000px',
height='1000px'))
tl.add_schema(axis_type='time', is_timeline_show=True, is_rewind_play=True, is_inverse=True,
label_opts=opts.LabelOpts(is_show=False))
for day in time_range[::-3]:
oversea_data = oversea_top(day, top=20, d_type='deadCount')[::-1]
if oversea_data:
bar = (
Bar(init_opts=opts.InitOpts(theme='dark'))
.add_xaxis([x[0] for x in oversea_data])
.add_yaxis("", [x[1] for x in oversea_data])
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='right',
font_style='italic'),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode("""new echarts.graphic.LinearGradient(1, 0, 0, 0,
[{
offset: 0,
color: 'rgb(255,99,71)'
}, {
offset: 1,
color: 'rgb(32,178,170)'
}])"""))
)
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒海外主要国家死亡情况",
subtitle="更新时间：{}".format(update_date)),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(
rotate=45)),
legend_opts=opts.LegendOpts(is_show=True),
graphic_opts=[opts.GraphicGroup(graphic_item=opts.GraphicItem(
rotation=JsCode("Math.PI / 4"),
bounding="raw",
right=110,
bottom=110,
z=100),
children=[
opts.GraphicRect(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_shape_opts=opts.GraphicShapeOpts(
width=400, height=50
),
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="rgba(0,0,0,0.3)"
),
),
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
text=day,
font="bold 26px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#fff"
),
),
),
],
)
],)
.reversal_axis()
)
tl.add(bar, day)
tl.render_notebook()
Out[19]:
海外主要国家确诊/治愈率/死亡率趋势
In [20]:
country_list = [x for x, _ in oversea_top(time_range[-1], top=20)]
date_list = [date.today() + datetime.timedelta(days=-i)
for i in range(14)][::-1]
tab = Tab()
for country in country_list:
cofirm = area_data(
area_name=country,
type_='confirmedCount',
date_list=date_list)
dead = area_data(area_name=country, type_='deadCount', date_list=date_list)
cured = area_data(
area_name=country,
type_='curedCount',
date_list=date_list)
dead_rate = [i/j if j != 0 else 0. for i, j in zip(dead, cofirm)]
cure_rate = [i/j if j != 0 else 0. for i, j in zip(cured, cofirm)]
line = (Line(init_opts=opts.InitOpts(theme='dark'))
.add_xaxis(date_list)
.add_yaxis("死亡率", dead_rate, yaxis_index=1, is_smooth=True, z_level=1)
.add_yaxis("治愈率", cure_rate, yaxis_index=1, is_smooth=True, z_level=1)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
bar = (Bar(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add_xaxis(date_list)
.add_yaxis("确诊病例", cofirm, yaxis_index=0)
.extend_axis(yaxis=opts.AxisOpts(
name="",
type_="value",
min_=0,
max_=round(max([max(cure_rate), max(dead_rate)])*1.2, 2),
position="right",
axislabel_opts=opts.LabelOpts(
formatter=JsCode("""function (value)
{return Number(value *100)+'%';}""")),))
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='top',
font_style='italic'),
itemstyle_opts=opts.ItemStyleOpts(opacity=1,
color=JsCode("""new echarts.graphic.LinearGradient
(0, 0, 0, 1,
[{
offset: 0,
color: 'rgb(255,99,71)'
}, {
offset: 1,
color: 'rgb(32,178,170)'
}])"""))
)
.set_global_opts(
title_opts=opts.TitleOpts(title="【{}】确诊/治愈率/死亡率 趋势".format(country),
subtitle="更新时间：{}".format(update_date)),
tooltip_opts=opts.TooltipOpts(formatter=JsCode("""function (params) {
if (!isNaN(params.value[1])) {return params.seriesName +':'+ Number(params.value[1] *100).toFixed(3)+'%';}
else {return params.seriesName +':' + params.value;}
}""")),
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=45)),
legend_opts=opts.LegendOpts(is_show=True)
)
)
bar.overlap(line)
tab.add(bar, country)
tab.render_notebook()
Out[20]:
美国巴西俄罗斯英国西班牙意大利印度德国秘鲁土耳其伊朗法国智利墨西哥沙特阿拉伯加拿大巴基斯坦卡塔尔孟加拉国比利时
海外疫情蔓延趋势图
In [21]:
tl = Timeline(
init_opts=opts.InitOpts(
theme='dark',
width='1000px',
height='700px'))
tl.add_schema(axis_type='time', is_timeline_show=True, is_rewind_play=True, is_inverse=True,
label_opts=opts.LabelOpts(is_show=False))
for day in time_range[::5][::-1]:
t_data = oversea_top(day, top='all', chinese=False,
d_type='confirmedCount', china_involved=True)
t_data = [[c.replace('United States of America', 'United States'), n] for c, n in t_data if c]
_map = (
Map(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add("累计确诊人数", t_data, "world", is_map_symbol_show=False, is_roam=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全球疫情蔓延地图",
subtitle="更新时间：{}".format(update_date)),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=True, max_=10000,
is_piecewise=False,
range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000']),
graphic_opts=[opts.GraphicGroup(graphic_item=opts.GraphicItem(
rotation=JsCode("Math.PI / 4"),
bounding="raw",
right=110,
bottom=110,
z=100),
children=[
opts.GraphicRect(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_shape_opts=opts.GraphicShapeOpts(
width=400, height=50
),
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="rgba(0,0,0,0.3)"
),
),
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
text=day,
font="bold 26px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#fff"
),
),
),
],
)
],
)
)
tl.add(_map, day)
tl.render_notebook()
Out[21]:
美国疫情地图
In [22]:
# 加载美国地图js资源
register_url("https://echarts-maps.github.io/echarts-countries-js/")
data_us = pd.read_csv('/home/kesci/input/uscovid198505/us-states.csv')
In [23]:
us_case, us_death = [], []
for _, row in data_us[data_us.date == data_us.date.max()].iterrows():
us_case.append([row.state, row.cases])
us_death.append([row.state, row.deaths])
tab = Tab()
_map = (
Map(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add("确诊人数", us_case, "美国", is_map_symbol_show=False, is_roam=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒美国疫情确诊地图",
subtitle="更新时间：{}".format(update_date)),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=True, max_=50000,
is_piecewise=False,
range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000'])
)
)
tab.add(_map, '累计确诊')
_map = (
Map(init_opts=opts.InitOpts(theme='dark', width='1000px'))
.add("死亡人数", us_death, "美国", is_map_symbol_show=False, is_roam=False)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒美国疫情死亡地图",
subtitle="更新时间：{}".format(update_date)),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=True, max_=3000,
is_piecewise=False,
range_color=['#FFFFE0', '#FFA07A', '#CD5C5C', '#8B0000'])
)
)
tab.add(_map, '死亡')
tab.render_notebook()
Out[23]:
累计确诊死亡