from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
c = (
Map()
.add("商家A", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
.set_global_opts(
title_opts=opts.TitleOpts(title="Map-VisualMap（分段型）"),
visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True),
)
.render("Map_visualmap_piecewise.html")
)
visualmap_opts=opts.VisualMapOpts(max_=shop_top10.values.max()))
bar1.render("粽子店铺销量Top10.html")
#%% md
### 全国省份销量地区分布-地图
#%%
from pyecharts.charts import Map
# 计算销量
province_num = df.groupby('省份')['销量'].sum().sort_values(ascending=False)
# 绘制地图
map1 = Map(init_opts=opts.InitOpts(width='1350px', height='750px'))
map1.add("", [
list(z)
for z in zip(province_num.index.tolist(), province_num.values.tolist())
],
maptype='china')
map1.set_global_opts(title_opts=opts.TitleOpts(title='各省份粽子销量分布'),
visualmap_opts=opts.VisualMapOpts(max_=300000),
toolbox_opts=opts.ToolboxOpts())
map1.render("各省份粽子销量分布.html")
#%% md
### 不同价格区间的销量占比
#%%
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
c = (Map().add("商家A", [list(z) for z in zip(Faker.provinces, Faker.values())],
"china").set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(title_opts=opts.TitleOpts(
title="Map-不显示Label")).render("Map_without_label.html"))
legend_pos="80%",
legend_orient="vertical")
grid = Grid()
grid.add(line, grid_right="55%")
grid.add(pie, grid_left="60%")
grid.render(r".\my_first_Zuhetu.html")
"""水球图"""
liquid = Liquid("水球图示例")
liquid.add("Liquid", [0.8])
liquid.show_config()
liquid.render(r".\my_first_Shuiqiu1.html")
liquid = Liquid("水球图示例")
liquid.add("Liquid", [0.6, 0.5, 0.4, 0.3],
is_liquid_animation=False,
shape='diamond')
liquid.show_config()
liquid.render(r".\my_first_Shuiqiu2.html")
"""地图"""
value = [155, 10, 66, 78, 33, 80, 190, 53, 49.6]
attr = ["福建", "山东", "北京", "上海", "甘肃", "新疆", "河南", "广西", "西藏"]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add("",
attr,
value,
maptype='china',
is_visualmap=True,
visual_text_color='#000')
map.show_config()
map.render(r".\my_first_Ditu.html")
page.add(bar)
# 产品数量前15店铺柱状图
bar1 = (Bar().add_xaxis(
shop_name_group['shop_name'].values.tolist()[:15]).add_yaxis(
'产品数量前15店铺',
shop_name_group['title'].values.tolist()[:15]).set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
rotate=30))))
page.add(bar1)
# 销量前15店铺饼状图
pie = (Pie().add('',
sell_data.values.tolist()[:15]).set_global_opts(
title_opts=opts.TitleOpts(title='销量前15店铺',
pos_left='center'),
legend_opts=opts.LegendOpts(is_show=False)))
page.add(pie)
# 店铺位置分布图
shop_map = (Map().add(
'', location_data.values.tolist(), 'china').set_global_opts(
title_opts=opts.TitleOpts(title='店铺位置分布图'),
visualmap_opts=opts.VisualMapOpts(
max_=max(location_data['shop_name'].values.tolist()),
min_=min(location_data['shop_name'].values.tolist()),
is_show=True)))
page.add(shop_map)
page.render()
def to_map_city(self, city, variate, province, update_time):
#pices定义数据分段
pieces = [
{
"max": 59999,
"min": 10000,
"label": ">10000",
"color": "#990033"
},
{
"max": 9999,
"min": 5000,
"label": "5000-9999",
"color": "#CC0033"
},
{
"max": 4999,
"min": 1000,
"label": "1000-4999",
"color": "#FF0033"
},
{
"max": 999,
"min": 100,
"label": "100-999",
"color": "#FF6633"
},
{
"max": 99,
"min": 50,
"label": "50-99",
"color": "#FF9900"
},
{
"max": 49,
"min": 10,
"label": "10-49",
"color": "#FFCC66"
},
{
"max": 9,
"min": 1,
"label": "1-9",
"color": "#FFFFCC"
},
{
"max": 0,
"min": 0,
"label": "0",
"color": "#FFFFFF"
},
]
c = (
# 设置地图大小
Map(init_opts=opts.InitOpts(width='1000px', height='880px')
).add("累计确诊人数", [list(z) for z in zip(city, variate)],
province,
is_map_symbol_show=False)
#设置不显示市级名称
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# 设置全局变量
.set_global_opts(
title_opts=opts.TitleOpts(title="%s地区疫情地图分布" % (province),
subtitle='截止%s %s省疫情分布情况' %
(update_time, province),
pos_left="center",
pos_top="10px"),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
max_=200,
is_piecewise=True,
pieces=pieces,
),
).render("./map/china/{}疫情地图.html".format(province)))
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='湖北省实时确诊数据.html')
def map(self, area=None):
"""
话题地理分布
:param area: 限制区域，默认为中国
:return:
"""
if self.maparea:
if not area or self.maparea == area:
self.load(QUrl('file:///' + dirname + '/qt/buffer/map.html'))
self.flag = 4
return
if not area:
area = 'china'
self.maparea = area
topicset = self.topic.find()
temp = {}
for topic in topicset:
for item in topic['entity']['Ns']:
if self.maparea == 'china':
city = self.get_province(''.join(item[0]))
else:
city = self.get_city(''.join(item[0]), self.maparea)
if city:
if city not in temp:
temp[city] = 0
temp[city] += item[1]
data = []
for key, value in temp.items():
data.append([key, value])
if self.maparea == 'china':
temparea = '中国'
else:
temparea = self.maparea
max_value = 100
if temp.values():
max_value = max(temp.values())
map = (
Map()
.add("话题分布", data, self.maparea)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="话题分布-" + temparea),
visualmap_opts=opts.VisualMapOpts(max_=max_value),
)
)
html = map.render_embed()
html_id = re.search('div id="(.*?)"', html).group(1)
html = re.sub(r'width:\d+px', 'width:100%', html)
# JavaScript代码注入，实现点击地图获取点击区域
js = '''<script>
new QWebChannel(qt.webChannelTransport, function (channel) {
window.map = channel.objects.map;
});
chart_html_id.on('click', function(params){
area = params.name;
map.map_click(area);
});
document.onclick = function(e){
ctx = e.target.getContext('2d')
data = ctx.getImageData(e.pageX,e.pageY,1,1).data
if (data[3] == 0) {
map.map_click('china')
}
}
</script>'''
qwebchanneljs = '<script type="text/javascript" src="qwebchannel.js"></script>'
html = re.sub('</head>', qwebchanneljs + '</head>', html)
html = re.sub('</body>', js + '</body>', html)
html = re.sub('html_id', html_id, html)
with open(dirname + '/qt/buffer/map.html', 'w') as f:
f.write(html)
self.load(QUrl('file:///' + dirname + '/qt/buffer/map.html'))
self.flag = 4
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
c = (Map().add(
"商家A",
[list(z) for z in zip(Faker.guangdong_city, Faker.values())],
"china-cities",
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
title_opts=opts.TitleOpts(title="Map-中国地图（带城市）"),
visualmap_opts=opts.VisualMapOpts(),
))
c.width = "100%"
put_html(c.render_notebook())
"黄大仙区": "黄大仙",
"油尖旺区": "油尖旺",
"元朗区": "元朗",
}
c = (Map().add(
series_name="香港18区人口密度",
maptype="香港",
data_pair=MAP_DATA,
name_map=NAME_MAP_DATA,
is_map_symbol_show=False,
).set_global_opts(
title_opts=opts.TitleOpts(
title="香港18区人口密度 （2011）",
subtitle="人口密度数据来自Wikipedia",
subtitle_link=WIKI_LINK,
),
tooltip_opts=opts.TooltipOpts(trigger="item",
formatter="{b}<br/>{c} (p / km2)"),
visualmap_opts=opts.VisualMapOpts(
min_=800,
max_=50000,
range_text=["High", "Low"],
is_calculable=True,
range_color=["lightskyblue", "yellow", "orangered"],
),
))
c.width = "100%"
put_html(c.render_notebook())
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.globals import ThemeType
shanghai_list = [
'黄浦区', '徐汇区', '长宁区', '静安区', '普陀区', '虹口区', '杨浦区', '闵行区', '宝山区', '嘉定区',
'金山区', '松江区', '青浦区', '奉贤区', '崇明区', '浦东新区'
]
shanghai_people = [
65.38, 108.44, 69.4, 106.28, 128.19, 79.7, 131.27, 254.35, 204.23, 158.89,
80.5, 176.22, 121.9, 115.2, 68.81, 555.02
]
BAIDU_LINK = 'https://baike.baidu.com/item/%E4%B8%8A%E6%B5%B7%E8%A1%8C%E6%94%BF%E5%8C%BA%E5%88%92/7426389?fr=aladdin'
c = (Map(init_opts=opts.InitOpts(
theme=ThemeType.DARK,
bg_color='#404a59', width='1600px', height='900px')).add(
"上海市-常住人口", [list(z) for z in zip(shanghai_list, shanghai_people)],
"上海").set_global_opts(
title_opts=opts.TitleOpts(
title="上海地图-常住人口（单位：万人）",
subtitle="常住人口数据来自百度百科",
subtitle_link=BAIDU_LINK,
),
visualmap_opts=opts.VisualMapOpts()).render("map_shanghai.html"))
from pyecharts.charts import Map
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'
]
popu = [
10, 8, 18, 8, 5, 29, 8, 17, 27, 24, 12, 11, 6, 7, 22, 16, 11, 14, 18, 5, 1,
7, 14, 4, 6, 8, 6, 15, 13, 39, 25, 21
]
list1 = [[locate[i], popu[i]] for i in range(len(locate))]
map_1 = Map()
map_1.set_global_opts(
title_opts=opts.TitleOpts(title="全国疫情确诊人数分布图"),
visualmap_opts=opts.VisualMapOpts(max_=50) #最大数据范围
)
map_1.add("确诊人数", list1, maptype="china")
map_1.render('map1.html')
fig.add_trace(go.Scatter(x=date_list, y=dead_list, name='Deaths'))
fig.add_trace(go.Scatter(x=date_list, y=heal_list, name='Recovered'))
fig.update_layout(title='2020-nCoV daily plot')
fig.show()
from pyecharts.charts import Map
from pyecharts import options as opts
# 省和直辖市
province_distribution = catch_distribution()
# maptype='china' 只显示全国直辖市和省级
map = Map()
map.set_global_opts(
title_opts=opts.TitleOpts(title="20200129 casses distribution"),
visualmap_opts=opts.VisualMapOpts(
max_=3600,
is_piecewise=True,
pieces=[
{
"max": 50000,
"min": 1001,
"label": ">1000",
"color": "#8A0808"
},
{
"max": 1000,
"min": 500,
def get_year_chart(year: str):
map_data = [[[x["name"], x["value"]] for x in d["data"]] for d in MapData
if d["time"] == year][0]
min_data, max_data = (minNum, maxNum)
data_mark: List = []
i = 0
for x in time_list:
if x == year:
data_mark.append(total_num[i])
else:
data_mark.append("")
i = i + 1
map_chart = (Map().add(
series_name="",
data_pair=map_data,
zoom=1,
center=[119.5, 34.5],
is_map_symbol_show=False,
itemstyle_opts={
"normal": {
"areaColor": "#323c48",
"borderColor": "#404a59"
},
"emphasis": {
"label": {
"show": Timeline
},
"areaColor": "rgba(255,255,255, 0.5)",
},
},
).set_global_opts(
title_opts=opts.TitleOpts(
title="" + str(year) +
"全国各省份NCP实时动态(数据来源:丁香园; 数据仓库:BlankerL/DXY-2019-nCoV-Data)",
subtitle="",
pos_left="center",
pos_top="top",
title_textstyle_opts=opts.TextStyleOpts(
font_size=25, color="rgba(255,255,255, 0.9)"),
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
formatter=JsCode("""function(params) {
if ('value' in params.data) {
return params.data.value[2] + ': ' + params.data.value[0];
}
}"""),
),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="30",
pos_top="center",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
))
line_chart = (Line().add_xaxis(time_list).add_yaxis(
"", total_num).add_yaxis(
"",
data_mark,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
).set_series_opts(label_opts=opts.LabelOpts(
is_show=False)).set_global_opts(title_opts=opts.TitleOpts(
title="全国各省份NCP实时动态(单位: 百人)", pos_left="72%", pos_top="5%")))
bar_x_data = [x[0] for x in map_data]
bar_y_data = [{"name": x[0], "value": x[1][0]} for x in map_data]
bar = (Bar().add_xaxis(xaxis_data=bar_x_data).add_yaxis(
series_name="",
yaxis_data=bar_y_data,
label_opts=opts.LabelOpts(is_show=True,
position="right",
formatter="{b} : {c}"),
).reversal_axis().set_global_opts(
xaxis_opts=opts.AxisOpts(max_=maxCount,
axislabel_opts=opts.LabelOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(is_show=False)),
tooltip_opts=opts.TooltipOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
is_calculable=True,
dimension=0,
pos_left="10",
pos_top="top",
range_text=["High", "Low"],
range_color=["lightskyblue", "yellow", "orangered"],
textstyle_opts=opts.TextStyleOpts(color="#ddd"),
min_=min_data,
max_=max_data,
),
))
pie_data = [[x[0], x[1][0]] for x in map_data]
pie = (Pie().add(
series_name="",
data_pair=pie_data,
radius=["15%", "35%"],
center=["80%", "82%"],
itemstyle_opts=opts.ItemStyleOpts(border_width=1,
border_color="rgba(0,0,0,0.5)"),
).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True, formatter="{b} {d}%"),
legend_opts=opts.LegendOpts(is_show=False),
))
grid_chart = (Grid().add(
bar,
grid_opts=opts.GridOpts(pos_left="10",
pos_right="45%",
pos_top="50%",
pos_bottom="5"),
).add(
line_chart,
grid_opts=opts.GridOpts(pos_left="65%",
pos_right="80",
pos_top="10%",
pos_bottom="50%"),
).add(pie, grid_opts=opts.GridOpts(pos_left="45%", pos_top="60%")).add(
map_chart, grid_opts=opts.GridOpts()))
return grid_chart
visualmap_opts=opts.VisualMapOpts(max_=110))
bar1.set_series_opts(label_opts=opts.LabelOpts(position='right')) # 标签
bar1.reversal_axis()
bar1.render_notebook()
# In[48]:
from pyecharts.charts import Map
# In[49]:
c = Map(init_opts=opts.InitOpts(width='800px', height='750px'))
c.add('',[list(z) for z in zip(province_num.index.tolist(), province_num.values.tolist())], 'china')
c.set_global_opts(title_opts=opts.TitleOpts('调剂信息省份分布地图'),
toolbox_opts=opts.ToolboxOpts(is_show=True),
visualmap_opts=opts.VisualMapOpts(max_=110))
c.render_notebook()
# In[51]:
import jieba.analyse #cmd pip install jieba
# In[52]:
def map_base() -> Map:
c = (Map().add(
"", [list(z) for z in zip(provinces, value)],
"china").set_global_opts(title_opts=opts.TitleOpts(title="map-基本图形")))
return c
import datetime
from pyecharts.charts import Map
from pyecharts import options as opts
from pyecharts.globals import ThemeType
from crawler import get_data, get_article
today_url = get_article()
if today_url is not None:
data = get_data(today_url)
summary = "更新日期:" + str(datetime.date.today()) + ", 数据来源:十堰市政府官网\n累计确诊:" + data[0][1] + "例, 累计出院:" + data[1][1] + "例, 累计死亡:" + data[2][1] + "例"
map = (
Map(init_opts=opts.InitOpts(bg_color="#FFFAFA", theme=ThemeType.ROMANTIC, width=1000))
.add("确诊人数", data, "十堰", is_map_symbol_show=False, )
.set_global_opts(
title_opts=opts.TitleOpts(title="十堰疫情确诊人数分布图 (By: Microyu)", subtitle=summary, pos_left="left"),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
pieces=[
{"min": 201, "label": '>200人', "color": "#4F060d"},
{"min": 101, "max": 200, "label": '101-200人', "color": "#CB2A2F"},
{"min": 51, "max": 100, "label": '51-100人', "color": "#E45A4F"},
{"min": 10, "max": 50, "label": '10-50人', "color": "#F79D83"},
{"min": 1, "max": 9, "label": '1-9人', "color": "#FCEBCF"},
],
range_text=['高', '低'],
),
)
)
map.render(path="./index.html")
"Turks and Caicos Is.": "特克斯和凯科斯群岛",
"St. Vin. and Gren.": "圣文森特和格林纳丁斯",
"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_=40000000,is_piecewise=True))
map.render('世界疫情分布.html')
def map_visualmap(date='2020-03-16'):
pieces = [{
"min": 0,
"max": 0,
"color": '#FFFFFF',
"label": "0"
}, {
"min": 1,
"max": 499,
"color": '#D3545F',
"label": "1-499"
}, {
"min": 500,
"max": 4999,
"color": '#A1232B',
"label": "500-4999"
}, {
"min": 5000,
"max": 9999,
"color": '#8D1D2C',
"label": "5000-9999"
}, {
"min": 10000,
"max": 99999,
"color": '#701F29',
"label": "1万-10万"
}, {
"min": 100000,
"max": 499999,
"color": '#5E2028',
"label": "10万-50万"
}, {
"min": 500000,
"max": 9999999,
"color": '#402225',
"label": "50万以上"
}]
df = pd.read_json('namemap.json')
with open(date + '.json', 'rb') as f:
line = f.readline()
js = json.loads(line)
provinceName = []
confirmedCount = []
for index, row in enumerate(js['newslist']):
temp = df[df['中文'] == row['provinceName']]
if len(temp) != 0:
temp = str(temp.iloc[0]['英文'])
provinceName.append(temp)
confirmedCount.append(row['confirmedCount'])
map_chart = Map(init_opts=opts.InitOpts(page_title="新冠肺炎情况",
width=str(x / 1.1) + 'px',
height=str(y / 1.1) + 'px',
theme=ThemeType.ROMANTIC), )
map_chart.set_global_opts(title_opts=opts.TitleOpts(
pos_left='20px',
title="全球新冠肺炎疫情情况（截止{0}）".format(str(datetime.date.today()))),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
pieces=pieces,
pos_left='left',
pos_bottom="50px",
),
legend_opts=opts.LegendOpts(
selected_mode='single',
orient='right',
))
map_chart.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
map_chart.add(
series_name="累计确诊数",
data_pair=[list(i) for i in zip(
provinceName,
confirmedCount,
)],
maptype="world",
is_map_symbol_show=False,
itemstyle_opts={
"normal": {
"areaColor": "#323c48",
"borderColor": "#404a59"
},
"emphasis": {
"label": {
"show": Timeline
},
"areaColor": "rgba(255,255,255, 0.5)",
},
},
)
return map_chart
#!/usr/bin/env python
# -*-coding:utf-8 -*-
import os
import re
import linecache
import math
import time
import shutil
# import numpy as np
from pyecharts import options as opts
from pyecharts.charts import Map
os.chdir(os.path.split(os.path.realpath(__file__))[0])
a1 = ['北京', '上海', '广东省']
a2 = [1, 2, 3]
output = (Map().add("污染指数", zip(
a1, a2), "china", is_map_symbol_show=False).set_global_opts(
title_opts=opts.TitleOpts(title="2020年4月生态环境部通报"),
visualmap_opts=opts.VisualMapOpts(min_=1,
max_=3)).render("重点城市环境空气排名2.html"))
"大埔区": "大埔",
"荃湾区": "荃湾",
"屯门区": "屯门",
"湾仔区": "湾仔",
"黄大仙区": "黄大仙",
"油尖旺区": "油尖旺",
"元朗区": "元朗",
}
(Map(init_opts=opts.InitOpts(width="1400px", height="800px")).add(
series_name="香港18区人口密度",
maptype="香港",
data_pair=MAP_DATA,
name_map=NAME_MAP_DATA,
is_map_symbol_show=False,
).set_global_opts(
title_opts=opts.TitleOpts(
title="香港18区人口密度 （2011）",
subtitle="人口密度数据来自Wikipedia",
subtitle_link=WIKI_LINK,
),
tooltip_opts=opts.TooltipOpts(trigger="item",
formatter="{b}<br/>{c} (p / km2)"),
visualmap_opts=opts.VisualMapOpts(
min_=800,
max_=50000,
range_text=["High", "Low"],
is_calculable=True,
range_color=["lightskyblue", "yellow", "orangered"],
),
).render("Population_density_of_HongKong_v2.html"))
def to_map_world(self, country, variate, confirmed_glo, update_time):
pieces = [
{
"max": 9999999,
"min": 1000000,
"label": ">1000000",
"color": "#990033"
},
{
"max": 999999,
"min": 100000,
"label": "100000-999999",
"color": "#CC0033"
},
{
"max": 99999,
"min": 10000,
"label": "10000-99999",
"color": "#FF0033"
},
{
"max": 9999,
"min": 1000,
"label": "1000-9999",
"color": "#FF6633"
},
{
"max": 999,
"min": 100,
"label": "100-999",
"color": "#FF9900"
},
{
"max": 99,
"min": 10,
"label": "10-99",
"color": "#FFCC66"
},
{
"max": 9,
"min": 1,
"label": "1-9",
"color": "#FFFFCC"
},
{
"max": 0,
"min": 0,
"label": "0",
"color": "#FFFFFF"
},
]
c = (
# 设置地图大小
Map(init_opts=opts.InitOpts(width='1000px', height='880px')
).add("累计确诊人数", [list(z) for z in zip(country, variate)],
"world",
is_map_symbol_show=False)
#设置不显示国家名称
.set_series_opts(label_opts=opts.LabelOpts(is_show=False)
).set_global_opts(
title_opts=opts.TitleOpts(
title="世界疫情地图分布",
subtitle='截止{0}世界已确诊人数{1}'.format(
update_time, confirmed_glo),
pos_left="center",
pos_top="10px"),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(
max_=200,
is_piecewise=True,
pieces=pieces,
),
).render("./map/世界疫情地图.html"))
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
c = (
Map()
.add("商家A", [list(z) for z in zip(Faker.provinces, Faker.values())], "china")
.set_global_opts(title_opts=opts.TitleOpts(title="Map-基本示例"),visualmap_opts=opts.VisualMapOpts())
.render("./picture/map_base.html")
)
print([list(z) for z in zip(Faker.provinces, Faker.values())])
