pyecharts 地图绘制

地图绘制

1. 热力图

   from pyecharts import options as opts
   from pyecharts.charts import Geo
   from pyecharts.globals import GeoType, ThemeType
   from pyecharts.faker import Faker
   import random
   
   map = (
       Geo(init_opts=opts.InitOpts(width="1000px", height="800px", renderer="canvas",
                                   theme=ThemeType.LIGHT, animation_opts=opts.AnimationOpts(animation=True)))
           .add_schema(maptype="china")
           .add(series_name="中国热力图",
                data_pair=[list(z) for z in
                           zip(Faker.provinces,
                               [random.randint(0, (i + 1) * 200 + 100) for i in range(len(Faker.provinces))])],
                type_=GeoType.EFFECT_SCATTER, symbol_size=20,
                color=Faker.visual_color[random.randint(0, len(Faker.visual_color)-1)])
           .add(series_name="广东热力图",
                data_pair=[list(z) for z in
                           zip(Faker.guangdong_city,
                               [random.randint(0, (i + 1) * 100) for i in range(len(Faker.provinces))])],
                type_=GeoType.EFFECT_SCATTER, symbol_size=10,
                color=Faker.visual_color[1])
           .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
           .set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=400, pos_top=20),
                            title_opts=opts.TitleOpts(title=""),
                            )
   )
   map.render(path="./render/geo_heatmap.html")
   
   

   

2. 行程图

   import random
   
   from pyecharts import options as opts
   from pyecharts.charts import Geo
   from pyecharts.globals import GeoType, SymbolType
   from pyecharts.faker import Faker
   
   Faker.provinces = ["北京", "上海", "江苏", "浙江", "江西", "广东", "湖南", "和布克赛尔蒙古自治县"]
   map = (
       Geo()
           .add_schema(maptype="china",
                       itemstyle_opts=opts.ItemStyleOpts(color="#9cf", border_color="#111"))
           .add(series_name="顺序",
                data_pair=[list(z) for z in
                           zip(Faker.provinces,
                               [i + 1 for i in range(len(Faker.provinces))])],
                type_=GeoType.EFFECT_SCATTER,
                color=Faker.visual_color[0],
                )
           .add(series_name="行程",
                data_pair=[(Faker.provinces[i], Faker.provinces[(i + 1) % len(Faker.provinces)]) for i in
                           range(len(Faker.provinces))],
                type_=GeoType.LINES,
                effect_opts=opts.EffectOpts(
                    symbol=SymbolType.ARROW, symbol_size=5, color=Faker.visual_color[2],
                ),
                # curve>0,曲线凸；curve<0，曲线凹
                linestyle_opts=opts.LineStyleOpts(curve=0.1),
                # color=Faker.visual_color[2],
                )
           .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
           .set_global_opts(title_opts=opts.TitleOpts(title="行程图"))
   )
   map.render(path="render/geo_lines.html")
   
   

   

3. 分布图

   from pyecharts import options as opts
   from pyecharts.charts import Geo
   from pyecharts.globals import ThemeType
   from pyecharts.faker import Faker
   import numpy as np
   
   length = 100
   lons = np.random.ranf(length) * (117.30043 - 115.71633) + 115.71633
   lats = np.random.ranf(length) * (40.65389 - 39.47002) + 39.47002
   
   map = Geo(init_opts=opts.InitOpts(width="1000px", height="800px", renderer="svg",
                                     theme=ThemeType.LIGHT, animation_opts=opts.AnimationOpts(animation=True))) 
       .add_schema(maptype="北京", layout_size=100)
   
   # 添加自定义点和属性
   [map.add_coordinate("点%d" % i, lons[i], lats[i])
        .add(series_name=(lambda x: "类别-0" if x % 2 == 0 else "类别-1")(i),
             data_pair=[("点%d" % i, i * 100)],
             color=(lambda x: Faker.visual_color[0] if x % 2 == 0 else Faker.visual_color[2])(i),
             ) for i in range(length)]
   
   map.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) 
       .set_global_opts(title_opts=opts.TitleOpts(title="自定义点"))
   # 在 html 渲染图表
   map.render('./render/geo_points.html')
   # 在 Jupyter Notebook 中渲染图表
   # map.render_notebook()