def show_map_charts():
city_num = df['city_dealed'].value_counts()
city_num.drop('国外', inplace=True)
city_num.drop('未填写', inplace=True)
map1 = Map(init_opts=opts.InitOpts(width='1350px', height='750px'))
map1.add("", [
list(z) for z in zip(city_num.index.tolist(), city_num.values.tolist())
],
maptype='china')
map1.set_global_opts(title_opts=opts.TitleOpts(title='评论者国内城市分布'),
visualmap_opts=opts.VisualMapOpts(max_=50),
toolbox_opts=opts.ToolboxOpts())
map1.render()
def map(self, datasets):
print(datasets)
m = Map(init_opts=opts.InitOpts(width="1100px", height="700px"))
m.add("count", datasets, maptype="china")
m.set_global_opts(
title_opts=opts.TitleOpts(title=get_title()),
visualmap_opts=opts.VisualMapOpts(
max_=3000,
min_=100,
range_text=['High', 'Low'],
is_calculable=True,
range_color=["lightskyblue", "yellow", "orangered"]),
legend_opts=opts.LegendOpts(is_show=False),
).render('docs/new-map.html')
def map_visualmap(sequence, date) -> Map:
c = Map()
c.add(date, sequence, maptype="china")
c.set_global_opts(title_opts=opts.TitleOpts(title="CBIC：全国各省现存确诊_至4月8号",subtitle="数据来源于丁香园"),legend_opts=opts.LegendOpts(is_show=False),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,\
pieces=pieces_list))
# c = (
# Map()
# .add(date, sequence, maptype="china")
# .set_global_opts(
# title_opts=opts.TitleOpts(title="全国疫情动态地图_确诊累计人数_截至至3月14号"),
# visualmap_opts=opts.VisualMapOpts(max_=1400)
# )
# )
return c
def get_map(labels, counts, where, title, size, pieces):
my_map = Map(init_opts=opts.InitOpts(width=size[0], height=size[1]))
my_map.add("新冠数据", [list(z) for z in zip(labels, counts)], where)
my_map.set_series_opts(label_opts=opts.LabelOpts(font_size=8))
my_map.set_global_opts(
title_opts=opts.TitleOpts(title=title),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
pieces=pieces,
is_piecewise=True,
is_show=False
),
)
return my_map
def map_type():
school_type_local = analysis_data.school_rank()
c = Map()
for key, value in school_type_local.items():
c.add(key, list(value.iteritems()), "china")
c.set_global_opts(visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pieces=[
{
"min": 100,
"max": 250
},
{
"min": 30,
"max": 100
},
{
"min": 20,
"max": 30
},
{
"min": 10,
"max": 20
},
{
"min": 5,
"max": 10
},
{
"value": 4
},
{
"value": 3
},
{
"min": 2,
"max": 2
},
{
"value": 1,
"label":
'1 一枝独秀',
"color": 'grey'
},
]),
title_opts=opts.TitleOpts(title='各类院校全国分布'),
legend_opts=opts.LegendOpts(type_="scroll",
pos_left="80%",
orient="vertical"))
return c
def get_data():
# API地址
url = 'https://api.inews.qq.com/newsqa/v1/automation/foreign/country/ranklist'
# 添加请求头
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.25 Safari/537.36 Core/1.70.3741.400 QQBrowser/10.5.3863.400'
}
# 获取结果
res = requests.get(url=url, headers=headers).text
# 转为json
data = json.loads(res)
# 获取数据
name = jsonpath.jsonpath(data, '$..name')
confirm = jsonpath.jsonpath(data, '$..confirm')
# 配对
data_list = zip(name, confirm)
# print(list(data_list))
# 绘制地图
map = Map(init_opts=opts.InitOpts(width="1900px", height="900px", page_title="全球疫情分布图", bg_color="#ADD8E6", theme="white")).add(
series_name='世界疫情分布图', data_pair=data_list, maptype='world', name_map=nameMap, is_map_symbol_show=False)
# 设置系列配置项
map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 设置全局配置项
# map.set_global_opts(title_opts=opts.TitleOpts(title="国外疫情情况"),
# visualmap_opts=opts.VisualMapOpts(max_=2000000, is_piecewise=True))
map.set_global_opts(title_opts=opts.TitleOpts(title="国外疫情情况"),
visualmap_opts=opts.VisualMapOpts(pieces=[
{"min": 500000, "color": "#800000"},
{"min": 100000, "max": 500000,
"color": "#CC0000"},
{"min": 50000, "max": 100000,
"color": "#FF0000"},
{"min": 10000, "max": 50000,
"color": "#FF4500"},
{"min": 1000, "max": 10000, "color": "#FF7F50"},
{"min": 100, "max": 1000, "color": "#FFA07A"},
{"max": 100, "color": "#FFCCCC"},
],
is_piecewise=True)) # 显示分段式图例
# 生成文件
map.render('世界疫情分布图.html')
def get_map():
maps = Map(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
value = []
for i in area:
where = {
'job_area': {
'$regex': ".*" + i + ".*"
},
}
count = mongodb.get_count_by_where(where)
value.append((i + "区", count))
maps.add("区域", value, "深圳")
maps.set_global_opts(title_opts=opts.TitleOpts(title="岗位地区需求图", ),
legend_opts=opts.LegendOpts(pos_left="20%"))
return (maps)
def analysis(self):
"""
数据分析可视化
:return:
"""
data_list = [(i[0], i[3]) for i in self.result]
with open('CountryMap.json', 'r') as f:
country_map = json.load(f)
map = Map(options.InitOpts(width='1366px', height='768px'))
map.add("确诊病例", data_list, 'world', name_map=country_map, is_map_symbol_show=False)
map.set_series_opts(label_opts=options.LabelOpts(is_show=False))
map.set_global_opts(
title_opts=options.TitleOpts(title='全球疫情数据可视化展示'),
visualmap_opts=options.VisualMapOpts(max_=100000)
)
map.render('World_Epidemic_Analysis.html')
def map_cs_level():
levels = ['A+', 'A', 'A-', 'B+', 'B', 'B-', 'C+', 'C', 'C-']
data = analysis_data.get_cs_level_formap()
c = Map()
for i in levels:
c.add(i,
data[i],
"china",
tooltip_opts=opts.TooltipOpts(
formatter=JsCode("""function (param) {
if (param.data) return param.data.value[1];
else return '无'
}""")))
c.set_global_opts(legend_opts=opts.LegendOpts(selected_mode='single'),
title_opts=opts.TitleOpts(title="学科评估"))
return c
def analysisArea(self, title='分析好友地域分布', friends_info=None):
area_infos = {'未知': 0}
for item in friends_info.get('province'):
if not item: area_infos['未知'] += 1
else:
if item in area_infos: area_infos[item] += 1
else: area_infos[item] = 1
map_ = Map(init_opts=dict(theme='purple-passion', page_title=title))
map_.add(title,
data_pair=tuple(zip(area_infos.keys(), area_infos.values())),
maptype='china')
map_.set_global_opts(
title_opts=opts.TitleOpts(title=title),
visualmap_opts=opts.VisualMapOpts(max_=200),
)
map_.render(os.path.join(self.savedir, '%s.html' % title))
def get_map_base(self, _data):
'''
plot geo-VisualMap
:param _data: gps对应的城市分布(dataframe)
:return: 热点图
'''
register_url(self.echarts_url)
c_map = Map(init_opts=opts.InitOpts(
width="1000px", height="600px", page_title='', bg_color=''))
c_map.add(series_name=self.series_name,
data_pair=_data,
maptype=self.country)
c_map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
c_map.set_global_opts(
title_opts=opts.TitleOpts(title="Map-VisualMap"),
visualmap_opts=opts.VisualMapOpts(max_=self.threshold_plot))
return c_map
def draw():
get_data.GetCondition()
result = {'province': [], 'province_confirmedCount': []}
result['province'].append('台湾')
result['province_confirmedCount'].append(18)
result['province'].append('香港')
result['province_confirmedCount'].append(56)
result['province'].append('澳门')
result['province_confirmedCount'].append(10)
# 批量删除多余字符的函数
def replace_something(source_str, replace_list):
for line in replace_list:
source_str = source_str.replace(line, "")
return source_str
with open("virus.csv", 'r', encoding='UTF-8') as csvFile:
reader = csv.reader(csvFile) # 读取文件数据
for item in reader:
if reader.line_num == 1:
continue
# 定义删除的字串列表
replace_list = ['省', '市', '壮族自治区', '维吾尔自治区', '回族自治区', '自治区']
# 调用删除字串的函数
province_name = replace_something(item[0], replace_list)
if len(result['province']) == 34:
break
if province_name in result['province']:
continue
else:
result['province'].append(province_name)
result['province_confirmedCount'].append(int(item[2]))
# 绘制地图
from pyecharts import options as opts
from pyecharts.charts import Map
map = Map()
map.add("确诊人数", [
list(z)
for z in zip(result['province'], result['province_confirmedCount'])
], 'china')
map.set_global_opts(
title_opts=opts.TitleOpts(title="疫情地图"),
visualmap_opts=opts.VisualMapOpts(max_=2000),
)
map.render(path="疫情地图.html")
def plt_data(name, confirm_all):
data_list = list(zip(name, confirm_all))
map = Map(opts.InitOpts(width='1600px',
height='600px')).add(series_name='累计确诊',
data_pair=data_list,
maptype="china",
is_map_symbol_show=False)
map.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
map.set_global_opts(title_opts=opts.TitleOpts("中国疫情"),
visualmap_opts=opts.VisualMapOpts(
range_color=Faker.visual_color, max_=1000)) # 图例显示
map.render("html/中国疫情分布图.html")
plt.rcParams['font.sans-serif'] = ['FangSong']
plt.bar(range(len(confirm_all)), confirm_all, tick_label=name)
plt.xticks(rotation=270)
plt.savefig("imgdata/中国疫情柱状图")
print("中国数据可视化成功，文件名：中国疫情分布图.html、中国疫情柱状图.png")
def make_geo_map(self):
"""
生成世界地图，根据各国电影发行量
:return:
"""
# print(get_current_time() + '|-------> 正在生成 世界各国电影发行量 图表...')
# 导入TOP500电影数据
csv_path = os.path.abspath(os.path.join(os.path.dirname("__file__"), os.path.pardir, "moviespider", "movie_info_top500.csv"))
rows = pd.read_csv(csv_path, encoding='utf-8', dtype=str)
# 分析并统计数据
col_country = rows['国别'].to_frame()
res = col_country.groupby('国别')['国别'].count().sort_values(ascending=False)
raw_data = [i for i in res.items()]
# 导入映射数据，英文名 -> 中文名
country_name = pd.read_json('countries_zh_to_en.json', orient='index')
stand_data = [i for i in country_name[0].items()]
# 数据转换
res_code = []
for raw_country in raw_data:
for stand_country in stand_data:
if stand_country[1] in raw_country[0]:
res_code.append(stand_country[0])
code = pd.DataFrame(res_code).groupby(0)[0].count().sort_values(ascending=False)
data = []
for k, v in code.items():
data.append([k, v])
# 制作图表
c = Map()
c.add("电影发行量", data, "world")
c.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
c.set_global_opts(title_opts=opts.TitleOpts(title="电影TOP500榜单中 - 世界各国电影发行量"),
visualmap_opts=opts.VisualMapOpts(max_=55))
htmlPath = os.path.abspath(os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), "analyse_data", "世界各国电影发行量.html"))
pngPath = os.path.abspath(os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), "analyse_data", "世界各国电影发行量.png"))
# 生成html
c.render(htmlPath)
# 生成png
# make_snapshot(snapshot, c.render(), pngPath)
# print(get_current_time() + '|-------> 已生成 世界各国电影发行量 图表...')
return c
def nowconfirm_map(data_json,virus_nowconfirm_dict):
map = Map(init_opts=opts.InitOpts(width="1900px", height="900px"))
map.set_global_opts(
title_opts=opts.TitleOpts(title="实时疫情地图 %s\n\n累计确诊 %d\n现有确诊 %d\n现有疑似 %d\n输入病例 %d\n累计治愈 %d\n累计死亡 %d" \
%(data_json['lastUpdateTime'],data_json['chinaTotal']['confirm'],data_json['chinaTotal']['nowConfirm'], \
data_json['chinaTotal']['suspect'],data_json['chinaTotal']['importedCase'], \
data_json['chinaTotal']['heal'],data_json['chinaTotal']['dead'])),
visualmap_opts=opts.VisualMapOpts(max_=3600, is_piecewise=True,
pieces=[
{"max": 1999999, "min": 1000, "label": "10000人及以上", "color": "#8A0808"},
{"max": 999, "min": 200, "label": "200-999人", "color": "#B40404"},
{"max": 199, "min": 50, "label": "50-199人", "color": "#DF0101"},
{"max": 49, "min": 30, "label": "30-49人", "color": "#F78181"},
{"max": 29, "min": 10, "label": "10-29人", "color": "#F5A9A9"},
{"max": 9, "min": 0, "label": "1-9人", "color": "#FFFFCC"},
], ) #最大数据范围，分段
)
virus_nowconfirm = list(virus_nowconfirm_dict.items())
# {names[i]:v[i] for i in range(len(names))}
map.add("现有确诊", data_pair=virus_nowconfirm, maptype="china", is_roam=True)
map.render('html\全国实时现有确诊疫情地图.html')
def china(self):
dirs = self.dirs
rname = self.rname
data = self.data
max_l = self.max_l
rich = {"{b}": {"color": "red"}}
os.chdir(dirs)
itemstyle_opts = {
"normal": {
"areaColor": "#323c48",
"borderColor": "#404a59"
},
"emphasis": {
"label": {
"show": Timeline
},
"areaColor": "rgba(255,255,255, 0.5)"
}
}
map = Map(init_opts=opts.InitOpts(width="900px", height="800px"))
map.add("",
data,
maptype='china',
is_map_symbol_show=False,
label_opts=opts.LabelOpts(is_show=True,
formatter="{b}\n{c}",
rich=rich,
font_size=10))
#map.Item(name,value)
map.set_global_opts(
title_opts=opts.TitleOpts(title="",
subtitle="",
pos_right="center",
pos_top="5%"),
visualmap_opts=opts.VisualMapOpts(max_=max_l,
range_color=["white", "skyblue"],
is_piecewise=False),
)
map.render(rname + ".html")
def get_picture1():
df = pd.read_csv(UPLOAD_FOLDER + '地区生产总值.csv', encoding='gbk')
locate = list(df.地区)
for i in range(0, len(locate)):
locate[i] = locate[i].replace('市', '')
locate[i] = locate[i].replace('省', '')
locate[i] = locate[i].replace('维吾尔自治区', '')
locate[i] = locate[i].replace('壮族自治区', '')
locate[i] = locate[i].replace('回族自治区', '')
locate[i] = locate[i].replace('自治区', '')
print(locate)
地区生产总值 = list(df.地区生产总值)
list1 = [[locate[i], 地区生产总值[i]] for i in range(len(locate))]
map_1 = Map()
map_1.set_global_opts(
title_opts=opts.TitleOpts(title="2018年中国各地区生产总值"),
visualmap_opts=opts.VisualMapOpts(max_=100000, min_=1000) # 最大数据范围
)
map_1.add("地区生产总值", list1, maptype="china")
fig = map_1.render('./temp/example.html')
return fig
def draw_virus_map(virus_data, virus_title):
"""
绘制疫情地图
:param virus_title:
:param virus_data:
:return:
"""
virus_map = Map()
virus_data_list = []
"""
A = [1,2,3]
B = [4,5,6]
zip(A, B)
(1,4),(2,5),(3,6)
"""
virus_zip = zip(virus_data.keys(), virus_data.values())
for z in virus_zip:
print(z)
virus_data_list.append(z)
virus_map.add(virus_title, virus_data_list, "china")
virus_map.set_global_opts(title_opts=opts.TitleOpts("中国疫情地图"),
visualmap_opts=opts.VisualMapOpts(split_number=6,is_piecewise=True,
pieces=[{"min": 1, "max": 9, "label": "1-9人",
"color": "#ffefd7"},
{"min": 10, "max": 99, "label": "10-99人",
"color": "#ffd2a0"},
{"min": 100, "max": 499, "label": "100-499人",
"color": "#fe8664"},
{"min": 500, "max": 999, "label": "500-999人",
"color": "#e64b47"},
{"min": 1000, "max": 9999,
"label": "1000-9999人", "color": "#c91014"},
{"min": 10000, "label": "10000人及以上",
"color": "#9c0a0d"}]),
tooltip_opts=opts.TooltipOpts(formatter="{b}:{c}"))
virus_map.render(path="疫情地图.html")
def createProvincePie(province, provinceMatchConfirm):
provinceCitys = list(provinceMatchConfirm.keys())
#城市名称标准化
provinceCitys = [i + '市' if i != "境外输入" else i for i in provinceCitys]
citysConfirm = list(provinceMatchConfirm.values())
# provinceCitys =['济南市', '青岛市', '泰安市', '烟台市', '淄博市', '枣庄市', '潍坊市', '济宁市', '威海市','日照市','临沂市','德州市','聊城市','滨州市','菏泽市','境外输入']
#citysConfirms=['280','63','35','47','30','24','44','260','38','16','49','37','38','15','11','22']
list1 = [[provinceCitys[i], citysConfirm[i]]
for i in range(len(provinceCitys))] #首先创建数据
#map_1
map_1 = Map(init_opts=opts.InitOpts(
width="400px", height="460px")) #创建地图，其中括号内可以调整大小，也可以修改主题颜色。
map_1.add("山东疫情", list1, maptype=province) #添加山东地图
map_1.set_global_opts( #设置全局配置项
#title_opts=opts.TitleOpts(title="山东疫情"), 添加标题
visualmap_opts=opts.VisualMapOpts(max_=300,
is_piecewise=True), #最大数据范围 并且使用分段
legend_opts=opts.LegendOpts(is_show=False), #是否显示图例
)
#map_2
map_2 = (
Pie(init_opts=opts.InitOpts(width="600px", height="500px")) #创建一个饼图
.add(
"", #图名
[[provinceCitys[i], citysConfirm[i]]
for i in range(len(provinceCitys))], #添加数据
radius=["40%", "75%"], # 调整半径
).set_global_opts(
legend_opts=opts.LegendOpts(
orient="vertical",
pos_top="10%",
pos_left="88%" #图例设置
), ).set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}")) #设置标签
)
map_1.render('blog/static/map1.html') # 地图
map_2.render('blog/static/map2.html') #饼图 保存到本地
name_map=name_map,
)
#设置全局配置项
map.set_global_opts(visualmap_opts=options.VisualMapOpts(max_=1100000,
is_piecewise=True,
pieces=[
{
"min": 500000
},
{
"min": 200000,
"max": 499999
},
{
"min": 100000,
"max": 199999
},
{
"min": 50000,
"max": 99999
},
{
"min": 10000,
"max": 49999
},
{
"max": 9999
},
]))
#设置系列配置项
map.set_series_opts(label_opts=options.LabelOpts(is_show=False)) #不显示国家名
map.render('map.html') #命名并保存
from pyecharts.charts import Map, Geo
from pyecharts import options as opts
#将数据处理成列表
locate = [
'北京', '天津', '河北', '山西', '内蒙古', '辽宁', '吉林', '黑龙江', '上海', '江苏', '浙江', '安徽',
'福建', '江西', '山东', '河南', '湖北', '湖南', '广东', '广西', '海南', '重庆', '四川', '贵州',
'云南', '陕西', '甘肃', '青海', '宁夏', '新疆', '西藏'
]
app_price = [
10.84, 8.65, 18.06, 8.90, 5.04, 29.20, 8.98, 17.80, 27.81, 24.24, 12.72,
11.10, 6.30, 7.00, 22.45, 16.92, 11.00, 14.99, 18.85, 5.85, 1.40, 7.32,
14.61, 4.62, 6.05, 8.07, 6.73, 15.54, 13.00, 39.07, 25.61, 21.3
]
list1 = [[locate[i], app_price[i]] for i in range(len(locate))]
map_1 = Map()
map_1.set_global_opts(
title_opts=opts.TitleOpts(title="2019年全国各省苹果价格表"),
visualmap_opts=opts.VisualMapOpts(max_=50) #最大数据范围
)
map_1.add("2019年全国各省苹果价格", list1, maptype="china")
map_1.render('map.html')
{
'min': 1000,
'max': 5000,
'label': '1000-5000',
'color': '#e57373'
},
{
'min': 1000,
'label': '5000+',
'color': '#c62828'
},
]
# noinspection PyTypeChecker
map.add('', zip(provices, valuse))
map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
map.set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pieces=pieces,
pos_left='100px'),
tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{b}<br/>{c}"),
)
filepath = 'Maps/' + time + '.html'
map.render(path=filepath)
tl.add(map, "{}".format(time))
tl.render("ChinaChart/ChinaMap.html")
"U.S. Virgin Is.": "美属维尔京群岛",
"Samoa": "萨摩亚"
}
#确诊人数
confirmedCount=jsonpath.jsonpath(python_list,"$..confirmedCount'")
print(confirmedCount)
#组成字典
data_list = list(zip(countryname,confirmedCount))
print(data_list)
#中英文映射的字典
# 4 数据可视化
map = Map().add(series_name="世界疫情分布",
data_pair=data_list,
maptype="world",
name_map=namemap,
is_map_symbol_show=False
)
map.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #不显示国家名称
map.set_global_opts(title_opts=opts.TitleOpts(title="国外疫情") , #设置标题
visualmap_opts=opts.VisualMapOpts(max_=30000000,is_piecewise=True))
map.render('世界疫情分布.html')
'color': '#f27777'
},
{
'max': 9,
'min': 1,
'color': '#f7adad'
},
{
'max': 0,
'color': '#f7e4e4'
},
]
labels = [data[0] for data in china_data]
counts = [data[1] for data in china_data]
m = Map()
m.add("累计确诊", [list(z) for z in zip(labels, counts)], 'china')
#系列配置项,可配置图元样式、文字样式、标签样式、点线样式等
m.set_series_opts(label_opts=opts.LabelOpts(font_size=12), is_show=False)
#全局配置项,可配置标题、动画、坐标轴、图例等
m.set_global_opts(
title_opts=opts.TitleOpts(title='全国实时确诊数据', subtitle='数据来源：丁香园'),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
pieces=pieces,
is_piecewise=True, #是否为分段型
is_show=True)) #是否显示视觉映射配置
#render（）会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件，也可以传入路径参数，如 m.render("mycharts.html")
m.render(path='全国实时确诊数据MAP.html')
json_array_province = province['cities']
hubei_data = [(format_city_name(city['cityName'], defined_cities), city['confirmedCount']) for city in
json_array_province]
hubei_data = sorted(hubei_data, key=lambda x: x[1], reverse=True)
print(hubei_data)
labels = [data[0] for data in hubei_data]
counts = [data[1] for data in hubei_data]
pieces = [
{'min': 10000, 'color': '#540d0d'},
{'max': 9999, 'min': 1000, 'color': '#9c1414'},
{'max': 999, 'min': 500, 'color': '#d92727'},
{'max': 499, 'min': 100, 'color': '#ed3232'},
{'max': 99, 'min': 10, 'color': '#f27777'},
{'max': 9, 'min': 1, 'color': '#f7adad'},
{'max': 0, 'color': '#f7e4e4'},
]
m = Map()
m.add("累计确诊", [list(z) for z in zip(labels, counts)], '湖北')
m.set_series_opts(label_opts=opts.LabelOpts(font_size=12),
is_show=False)
m.set_global_opts(title_opts=opts.TitleOpts(title='湖北省实时确诊数据',
subtitle='数据来源：丁香园'),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(pieces=pieces,
is_piecewise=True,
is_show=True))
m.render(path='/home/aistudio/data/湖北省实时确诊数据.html')
m.set_global_opts(title_opts=opts.TitleOpts(title="各国现有确诊人数地图"),
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pieces=[{
"min": 5000,
"label": '>5000',
"color": "#893448"
}, {
"min": 1000,
"max": 4999,
"label": '1000-4999',
"color": "#ff585e"
}, {
"min": 500,
"max": 999,
"label": '500-1000',
"color": "#fb8146"
}, {
"min": 101,
"max": 499,
"label": '101-499',
"color": "#ffA500"
}, {
"min": 10,
"max": 100,
"label": '10-100',
"color": "#ffb248"
}, {
"min": 0,
"max": 9,
"label": '0-9',
"color": "#fff2d1"
}]))
map_chart.set_global_opts(
title_opts=opts.TitleOpts(title=f"全国疫情地图( {alldata['lastUpdateTime']} )"),
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pieces=[{
"min": 1,
"max": 9,
"label": "1-9人",
"color": "#FFE6BE"
}, {
"min": 10,
"max": 99,
"label": "10-99人",
"color": "#FFB769"
}, {
"min": 100,
"max": 499,
"label": "100-499人",
"color": "#FF8F66"
}, {
"min": 500,
"max": 999,
"label": "500-999人",
"color": "#ED514E"
}, {
"min": 1000,
"max": 9999,
"label": "1000-9999人",
"color": "#CA0D11"
}, {
"min": 10000,
"max": 100000,
"label": "10000人以上",
"color": "#A52A2A"
}]))
colors = sns.color_palette("Blues", 10).as_hex()
time_map = Timeline()
attr = populations.columns.tolist()
for time in populations.index:
values = populations.loc[time].tolist()
popu_map = Map()
popu_map.add("", [list(z) for z in zip(attr, values)],
maptype="china",
is_map_symbol_show=False)
popu_map.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
popu_map.set_global_opts(title_opts=opts.TitleOpts(
title="China population by province from 2000 to 2018",
subtitle="Units: 10K"),
visualmap_opts=opts.VisualMapOpts(
max_=12000,
is_piecewise=True,
split_number=12,
range_color=colors),
toolbox_opts=opts.ToolboxOpts())
time_map.add(popu_map, "{}".format(time))
time_map.render("plots/population-map-over-time.html")
#动态展示各省市人口变化
sorted_pops = []
for year in range(2000, 2019):
populations.sort_values(year, axis=1, inplace=True)
sorted_pops.append(
[year, populations.loc[year].tolist(),
populations.columns.tolist()])
map_CFR.set_global_opts(
visualmap_opts=opts.VisualMapOpts(
max_=max_visual,
is_piecewise=True,
pieces=[{
"min":
data_map.loc[:, "case_fatality_ratio"].describe()[3],
"max":
(data_map.loc[:, "case_fatality_ratio"].describe()[4] - 1e-10),
"label":
"Below first quartile"
}, {
"min":
data_map.loc[:, "case_fatality_ratio"].describe()[4],
"max":
(data_map.loc[:, "case_fatality_ratio"].describe()[5] - 1e-10),
"label":
"Below median"
}, {
"min":
data_map.loc[:, "case_fatality_ratio"].describe()[5],
"max":
(data_map.loc[:, "case_fatality_ratio"].describe()[6] - 1e-10),
"label":
"Above median"
}, {
"min": data_map.loc[:, "case_fatality_ratio"].describe()[6],
"max": data_map.loc[:, "case_fatality_ratio"].describe()[7],
"label": "Above third quartile"
}]),
title_opts=opts.TitleOpts(
title=
"Worldwide Covid-19 Case Fatality Rate Corrected for Positive Rate",
subtitle=
"Most recently available data used from doi: 10.1038/s41597-020-00688-8",
pos_left="center",
padding=0,
item_gap=2,
title_textstyle_opts=opts.TextStyleOpts(color="firebrick",
font_weight="bold",
font_family="Times New Roman",
font_size=30),
subtitle_textstyle_opts=opts.TextStyleOpts(
color="darkblue",
font_weight="bold",
font_family="Times New Roman",
font_size=13)),
legend_opts=opts.LegendOpts(is_show=False))
def generate_heat_map(date, country='世界', province=''):
# 累计病例热力图
if country == '世界':
countries = ['Albania', 'Algeria', 'Afghanistan', 'Argentina', 'Akrotiri and Dhekelia', 'United Arab Emirates',
'Aruba', 'Oman', 'Azerbaijan', 'Egypt', 'Ethiopia', 'Ireland', 'Estonia', 'Andorra', 'Angola',
'Anguilla', 'Antigua and Barbuda', 'Austria', 'Aland', 'Australia', 'Barbados', 'Papua New Guinea',
'Bahamas', 'Pakistan', 'Paraguay', 'Palestine', 'Bahrain', 'Panama', 'Brazil', 'Belarus', 'Bermuda',
'Bulgaria', 'Northern Mariana Islands', 'Northern Cyprus', 'Benin', 'Belgium', 'Iceland', 'Poland',
'Bosnia and Herzegovina', 'Bolivia', 'Belize', 'Botswana', 'Bonaire, Sint Eustatius and Saba',
'Bhutan', 'Burkina Faso', 'Bouvet Island', 'North Korea', 'Equatorial Guinea', 'Denmark', 'Germany',
'Timor-Leste', 'Togo', 'Dominica', 'Dominica', 'Russia', 'Ecuador', 'Eritrea', 'France',
'Faroe Islands', 'French Polynesia', 'French Guiana', 'French Southern Territories', 'Vatican City',
'Philippines', 'Fiji', 'Finland', 'Cape Verde', 'Falkland Islands', 'Gambia', 'Republic of Congo',
'Democratic Republic of the Congo', 'Colombia', 'Costa Rica', 'Guernsey', 'Grenada', 'Greenland',
'Georgia', 'Cuba', 'Guadeloupe', 'Guam', 'Guyana', 'Kazakhstan', 'Haiti', 'South Korea',
'Netherlands', 'Heard Island and McDonald Islands', 'Montenegro', 'Ruby Princess', 'Honduras',
'Kiribati', 'Djibouti', 'Kyrgyzstan', 'Guinea', 'Guinea-Bissau', 'Canada', 'Ghana', 'Gabon',
'Burundi', 'Cambodia', 'Czech Republic', 'Zimbabwe', 'Cameroon', 'Qatar', 'Cayman Islands',
'Cocos Islands', 'Comoros', 'Kosovo', "Côte d'Ivoire", 'Kuwait', 'Clipperton Island', 'Croatia',
'Kenya', 'Cook Islands', 'Curaçao', 'Latvia', 'Lesotho', 'Laos', 'Lebanon', 'Caspian Sea',
'Lithuania', 'Liberia', 'Libya', 'Liechtenstein', 'Reunion', 'Luxembourg', 'Rwanda', 'Romania',
'Madagascar', 'Isle of Man', 'Maldives', 'Malta', 'Malawi', 'Malaysia', 'Mali', 'Macedonia',
'Marshall Islands', 'Martinique', 'Mayotte', 'Mauritius', 'Mauritania', 'United States',
'Virgin Islands, U.S.', 'United States Minor Outlying Islands', 'American Samoa', 'Mongolia',
'Montserrat', 'Bangladesh', 'Peru', 'Micronesia', 'Myanmar', 'Moldova', 'Morocco', 'Monaco',
'Mozambique', 'Mexico', 'Namibia', 'South Africa', 'Antarctica',
'South Georgia and the South Sandwich Islands', 'Spratly Islands', 'South Sudan', 'Nauru', 'Nepal',
'Nicaragua', 'Niger', 'Nigeria', 'Niue', 'Norway', 'Norfolk Island', 'Palestina', 'Palau',
'Pitcairn Islands', 'Portugal', 'Japan', 'Sweden', 'Switzerland', 'El Salvador', 'Samoa', 'Serbia',
'Sierra Leone', 'Senegal', 'Cyprus', 'Seychelles', 'Saudi Arabia', 'Saint-Barthélemy',
'Christmas Island', 'São Tomé and Príncipe', 'Saint Helena', 'Saint Kitts and Nevis', 'Saint Lucia',
'Saint-Martin', 'San Marino', 'Saint Pierre and Miquelon', 'Saint Vincent and the Grenadines',
'Sri Lanka', 'Slovakia', 'Slovenia', 'Svalbard and Jan Mayen', 'Swaziland', 'Sudan', 'Suriname',
'Solomon Islands', 'Somalia', 'Tajikistan', 'Thailand', 'Tanzania', 'Tonga',
'Turks and Caicos Islands', 'Trinidad and Tobago', 'Tunisia', 'Tuvalu', 'Turkey', 'Turkmenistan',
'Tokelau', 'Wallis and Futuna', 'Vanuatu', 'Guatemala', 'Venezuela', 'Brunei', 'Uganda', 'Ukraine',
'Uruguay', 'Uzbekistan', 'Spain', 'Western Sahara', 'Paracel Islands', 'Greece', 'Sint Maarten',
'Singapore', 'New Caledonia', 'New Zealand', 'Hungary', 'Syria', 'Jamaica', 'Armenia', 'Yemen',
'Iraq', 'Iran', 'Israel', 'Italy', 'India', 'Indonesia', 'United Kingdom', 'British Virgin Islands',
'British Indian Ocean Territory', 'Jordan', 'Vietnam', 'Zambia', 'Jersey', 'Chad', 'Gibraltar',
'Chile', 'Central African Republic', 'China', 'Diamond Princess']
# case_num = [fetch_country_data(date, date, country) for country in countries]
case_num = [650, 2546, 558, 1728, 0, 4804, 89, 1117, 1620, 2002, 97, 17110, 750, 545, 13, 0, 19, 13991, 0, 6163,
57, 8, 37, 8023, 155, 263, 2065, 4501, 61685, 6406, 0, 444, 0, 0, 62, 13642, 1773, 5698, 1106, 258,
16, 12, 0, 5, 569, 0, 0, 13, 8415, 147171, 21, 87, 2584, 14, 34306, 3433, 37, 56148, 157, 56, 122,
0, 2, 1924, 14, 4000, 56, 0, 9, 33, 136, 2569, 480, 0, 13, 11, 309, 1140, 0, 0, 35, 1901, 17, 9610,
322, 0, 274, 0, 195, 0, 834, 675, 698, 25, 31262, 378, 110, 7, 120, 4448, 9, 1465, 2753, 0, 0, 0, 0,
769, 2729, 0, 1764, 239, 0, 0, 464, 0, 13, 234, 0, 828, 83, 24, 55, 0, 3550, 140, 7051, 101, 0, 29,
433, 14, 5025, 298, 1136, 0, 0, 0, 320, 6, 238081, 0, 0, 0, 13, 2, 2650, 20246, 0, 72, 1925, 2545,
82, 34, 21824, 11, 3983, 0, 0, 0, 2, 0, 31, 7, 617, 745, 0, 32, 0, 0, 0, 0, 2549, 5906, 4971, 26400,
293, 0, 2732, 67, 650, 401, 10, 11457, 0, 0, 4, 0, 14, 17, 0, 126, 0, 9, 321, 941, 256, 0, 14, 119,
9, 0, 110, 0, 2794, 183, 0, 5, 104, 660, 0, 89480, 0, 0, 0, 0, 104, 190, 134, 55, 3060, 513, 1881,
176439, 5, 0, 1374, 0, 2296, 17, 1371, 933, 29, 78, 1325, 1, 1702, 86143, 11384, 103031, 19358,
2698, 1002, 0, 0, 387, 241, 117, 0, 53, 142, 12667, 10, 79527, 645]
world = Map()
world.add('{}疫情地图'.format(country), [list(z) for z in zip(countries, case_num)], 'world')
world.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
world.set_global_opts(title_opts=opts.TitleOpts(title='截至5月26日各国的累计治愈人数'),
visualmap_opts=opts.VisualMapOpts(max_=cal_upper_bound(max(case_num)),
is_piecewise=True,
range_color=('#ffffff', '#ff3838')
)
)
world.render(path='2.html')
elif province == '全国':
provinces = ['新疆', '西藏', '青海', '甘肃', '四川', '云南', '宁夏', '陕西', '重庆', '贵州', '广西', '内蒙古',
'山西', '河南', '湖北', '湖南', '广东', '澳门', '香港', '海南', '江西', '安徽', '河北', '北京',
'天津', '安徽', '江苏', '浙江', '上海', '福建', '山东', '南海诸岛', '台湾', '辽宁', '吉林',
'黑龙江']
case_num = [fetch_province_data(date, date, province) for province in provinces]
world = Map()
world.add('{}疫情地图'.format(country), [list(z) for z in zip(provinces, case_num)], country)
world.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
world.set_global_opts(title_opts=opts.TitleOpts(title='截至5月26日各国的累计治愈人数'),
visualmap_opts=opts.VisualMapOpts(max_=cal_upper_bound(max(case_num)),
is_piecewise=True,
range_color=('#ffffff', '#ff3838')
)
)
world.render(path='2.html')
else:
cities = ['北京']
case_num = [fetch_city_data(data, data, city) for city in cities]
world = Map()
world.add('{}疫情地图'.format(province), [list(z) for z in zip(cities, case_num)], 'province')
world.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
world.set_global_opts(title_opts=opts.TitleOpts(title='截至5月26日各国的累计治愈人数'),
visualmap_opts=opts.VisualMapOpts(max_=cal_upper_bound(max(case_num)),
is_piecewise=True,
range_color=('#ffffff', '#ff3838')
)
)
world.render(path='2.html')
