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())])