用Pychsrts对疫情数据进行可视化分析
作者：互联网
文章目录
数据来源
本文使用的数据源来自https://lab.isaaclin.cn/nCoV/zh
数据可视化
import requests
import pyecharts
from pyecharts.charts import *
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from datetime import date, datetime
import datetime
url = 'https://lab.isaaclin.cn/nCoV/api/area'
data = requests.get(url).json()
# 生成更新日期
update_date = date.today()
最新疫情数据
全球疫情地图
oversea_confirm = []
for item in data['results']:
if item['countryEnglishName']:
oversea_confirm.append((item['countryEnglishName'].replace('United States of America', 'United States')
.replace('United Kiongdom', 'United Kingdom'),
item['confirmedCount']))
_map = (
Map(init_opts=opts.InitOpts(theme='dark', width='400'))
.add("累计确诊人数", 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="更新日期：{}".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', '#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', '累计确诊人数：{}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()
全国疫情地图
province_data = []
for item in data['results']:
if item['countryName'] == '中国':
province_data.append((item['provinceShortName'], item['confirmedCount']))
_map = (
Map(init_opts=opts.InitOpts(theme='dark', width='400'))
.add("累计确诊人数", province_data, "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="更新日期：{}".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'])
)
)
_map.render_notebook()
湖北省内确诊情况¶
for item in data['results']:
if item['provinceShortName'] == '湖北':
hubei_data = item['cities']
bar = (
Bar(init_opts=opts.InitOpts(theme='dark', width='400'))
.add_xaxis([x['cityName'] for x in hubei_data])
.add_yaxis("累计确诊人数", [x['confirmedCount'] for x in hubei_data])
.add_yaxis("当前确诊人数", [x['currentConfirmedCount'] for x in hubei_data])
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒湖北省内确诊情况",
subtitle="更新日期：{}".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()
全国疫情热力图
cities_data = []
for item in data['results']:
if item['countryName'] == '中国':
cities_data.extend((item['cities']))
geo = (
Geo(init_opts=opts.InitOpts(theme='dark', width='400'))
.add_schema(maptype="china", zoom=3, center=[114.31,30.52])
.add("累计确诊人数",
[(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="更新日期：{}".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()
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。
# 获取时间序列数据
# 细分到城市
area_data_timeline = requests.get('https://lab.isaaclin.cn/nCoV/api/area?latest=0').json()
# 全国数据
all_data_timeline = requests.get('http://lab.isaaclin.cn/nCoV/api/overall?latest=0').json()
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')
# 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['results'][::-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['results'][::-1]:
to_update_date = date.fromtimestamp(item['updateTime']/1000)
insert_data(to_update_date, '全国', item, 0)
time_range = list(format_data.keys())
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¶
data_type = {'累计确诊': 'confirmedCount',
'死亡病例': 'deadCount',
'治愈病例': 'curedCount'}
tl = Timeline(init_opts=opts.InitOpts(theme='dark', width='400'))
tl.add_schema(is_auto_play=True, play_interval=5000)
for key_, value_ in data_type.items():
line = (Line(init_opts=opts.InitOpts())
.add_xaxis(time_range)
.add_yaxis("全国", area_data('全国', value_),is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(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)'
}])""")),
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最新数据")], symbol_size=70))
.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="新型冠状病毒全国{}趋势图".format(key_),
subtitle="更新日期：{}".format(update_date)),
xaxis_opts=opts.AxisOpts(
type_="time",
splitline_opts=opts.SplitLineOpts(is_show=False)),
))
tl.add(line, key_)
tl.render_notebook()
全国疫情新增趋势
line = (Line(init_opts=opts.InitOpts(theme='dark'))
.add_xaxis(time_range[1:])
.add_yaxis("全国", area_data('全国', get_total=False),is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(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_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国每日新增确证病例趋势图",
subtitle="更新日期：{}".format(update_date)),
xaxis_opts=opts.AxisOpts(
type_="time",
splitline_opts=opts.SplitLineOpts(is_show=False)),
))
line.render_notebook()
全国疑似病例趋势¶
line = (Line(init_opts=opts.InitOpts(theme='dark'))
.add_xaxis(time_range[1:])
.add_yaxis("全国", area_data('全国', 'suspectedCount', get_total=True),is_smooth=True,
areastyle_opts=opts.AreaStyleOpts(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_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="新型冠状病毒全国疑似病例趋势图",
subtitle="更新日期：{}".format(update_date)),
xaxis_opts=opts.AxisOpts(
type_="time",
splitline_opts=opts.SplitLineOpts(is_show=False)),
))
line.render_notebook()
全国疫情蔓延趋势
tl = Timeline(init_opts=opts.InitOpts(theme='dark', width='400'))
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("累计确诊人数",
[(key_, value_['confirmedCount']) 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="更新日期：{}".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()
海外疫情
海外疫情趋势
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。
def oversea_top(day=date.today(), top=10, d_type='confirmedCount'):
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())
oversea_data = sorted(oversea_data, key=lambda x: x[1]['confirmedCount'], reverse=True)[:top]
oversea_data = [(x[0], x[1][d_type]) for x in oversea_data]
return oversea_data
tl = Timeline(init_opts=opts.InitOpts(theme='dark', width='400'))
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[::-1]:
oversea_data = oversea_top(day)[::-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='insideRight',
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="更新日期：{}".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()
海外主要国家确诊/治愈率/死亡率趋势
country_list = ['韩国', '日本', '伊朗', '新加坡', '意大利']
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, color='red')
.add_yaxis("治愈率", cure_rate, yaxis_index=1, is_smooth=True, color='green')
.extend_axis(yaxis=opts.AxisOpts(
name="",
type_="value",
min_=0,
max_=max([max(cure_rate),max(dead_rate)])*1.2,
position="right",
axislabel_opts=opts.LabelOpts(
formatter=JsCode("""function (value)
{return Number(value *100)+'%';}""")),)
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="【{}】确诊/治愈率/死亡率 趋势".format(country),
subtitle="更新日期：{}".format(update_date)),
tooltip_opts=opts.TooltipOpts(formatter=JsCode("""function (params)
{return Number(params.value[1] *100).toFixed(3)+'%';}""")),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
legend_opts=opts.LegendOpts(is_show=True)
)
)
bar = (Bar()
.add_xaxis(date_list)
.add_yaxis("确诊病例", cofirm, yaxis_index=0)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='top',
font_style='italic'),
itemstyle_opts=opts.ItemStyleOpts(opacity=0.8,
color=JsCode("""new echarts.graphic.LinearGradient
(0, 0, 0, 1,
[{
offset: 0,
color: 'rgb(255,99,71)'
}, {
offset: 1,
color: 'rgb(32,178,170)'
}])"""))
)
)
line.overlap(bar)
tab.add(line, country)
tab.render_notebook()
标签：graphic,Pychsrts,疫情,update,item,可视化,date,data,opts 来源： https://blog.csdn.net/weixin_41503009/article/details/104650835