pyecharts包绘制Map地图（笔记）
最近写文章，需要绘制地图，开始想用Tableau做，但是Tableau绘制某些地图不太方便，需要边界经纬度，于是找到了pyecharts
由于pyecharts版本更新，以前的代码不兼容新版本
pyecharts的中文官方文档(http://pyecharts.org/#/zh-cn/geography_charts)
通过作者给出的例子就可以快速上手，感谢作者！
pyecharts包安装
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pyecharts
下载地图包：
pip3 install echarts-countries-pypkg
pip3 install echarts-china-provinces-pypkg
pip3 install echarts-china-cities-pypkg
Map地图主要用于地理区域数据的可视化
geo地图主要用于热力分布图
#Map地图
#主要用于地理区域数据的可视化
#pyecharts包作者给出的func pyecharts.charts.Map.add
map.add(name, attr, value, maptype='china', is_roam=True, is_map_symbol_show=True, **kwargs)
def add(
# 系列名称，用于 tooltip 的显示，legend 的图例筛选。
series_name: str,
# 数据项 (坐标点名称，坐标点值)
data_pair: Sequence,
# 地图类型，具体参考 pyecharts.datasets.map_filenames.json 文件
maptype: str = "china",
# 是否选中图例
is_selected: bool = True,
# 是否开启鼠标缩放和平移漫游。
is_roam: bool = True,
# 当前视角的中心点，用经纬度表示
center: Optional[Sequence] = None,
# 当前视角的缩放比例。
zoom: Optional[Numeric] = 1,
# 自定义地区的名称映射
name_map: Optional[dict] = None,
# 标记图形形状
symbol: Optional[str] = None,
# 是否显示标记图形
is_map_symbol_show: bool = True,
# 标签配置项，参考 `series_options.LabelOpts`
label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),
# 提示框组件配置项，参考 `series_options.TooltipOpts`
tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,
# 图元样式配置项，参考 `series_options.ItemStyleOpts`
itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,
# 高亮标签配置项，参考 `series_options.LabelOpts`
emphasis_label_opts: Union[opts.LabelOpts, dict, None] = None,
# 高亮图元样式配置项，参考 `series_options.ItemStyleOpts`
emphasis_itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,
)
#新版本的导包方式发生了变化
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
#Map：地图
#准备数据
data = [('浙江', 10),('广东', 20),('河南', 30),('湖南', 40)]
def map_china():
c = (
Map()
.add(series_name="", data_pair=data, maptype="china")
# 使用 options 配置项，在 pyecharts 中，一切皆 Options
.set_global_opts(
title_opts=opts.TitleOpts(title="中国地图"),
visualmap_opts=opts.VisualMapOpts(max_=50,is_piecewise=True,#最大数据范围以及是否分段
)
))
return c
if __name__ == '__main__':
d_map = map_china()
d_map.render(path="C:/test_map_1.html")
作者给出的其他例子
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())], "广东")
.set_global_opts(
title_opts=opts.TitleOpts(title="Map-广东地图"), visualmap_opts=opts.VisualMapOpts()
)
.render("C:/map_guangdong.html")
)
不显示标签和标记
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())], "广东",is_map_symbol_show=False,) #不显示标记图形
.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #不显示标签（城市名）
.set_global_opts(
title_opts=opts.TitleOpts(title="Map-广东地图"), visualmap_opts=opts.VisualMapOpts()
)
.render("C:/map_guangdong.html")
)
自定义分段
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
cq_city = ["北碚区", "巴南区", "渝北区", "九龙坡区","渝中区","江北区","南岸区","沙坪坝区","大渡口区"]
GDP_value = [552, 781, 1543, 1211,1204,1028,725,936,228]
def map_cq():
c = (
Map()
.add("", [list(z) for z in zip(cq_city, GDP_value)], "重庆",is_map_symbol_show=False,)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="2018年重庆主城九区"),
visualmap_opts=opts.VisualMapOpts(max_=99999,is_piecewise=True,
pieces=[{"max": 499, "min": 0, "label": "0-499","color":"#FFE4E1"},
{"max": 899, "min": 500, "label": "500-899","color":"#FF7F50"},
{"max": 1299, "min": 900, "label": "900-1299","color":"#F08080"},
{"max": 1599, "min": 1300, "label": ">=1300","color":"#CD5C5C"}])
)
)
return c
if __name__ == '__main__':
cq = map_cq()
cq.render(path="C:/test_map_1.html")
自动分段
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
cq_city = ["北碚区", "巴南区", "渝北区", "九龙坡区","渝中区","江北区","南岸区","沙坪坝区","大渡口区"]
GDP_value = [552, 781, 1543, 1211,1204,1028,725,936,228]
def map_cq():
c = (
Map()
.add("", [list(z) for z in zip(cq_city, GDP_value)], "重庆",is_map_symbol_show=False,)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="2018年重庆主城九区GDP（亿元）"),
visualmap_opts=opts.VisualMapOpts(max_=1550,is_piecewise=True,)
)
)
return c
if __name__ == '__main__':
cq = map_cq()
cq.render(path="C:/test_map_1.html")
设置分段数
split_number 表示图例所分的段数
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
cq_city = ["北碚区", "巴南区", "渝北区", "九龙坡区","渝中区","江北区","南岸区","沙坪坝区","大渡口区"]
GDP_value = [552, 781, 1543, 1211,1204,1028,725,936,228]
def map_cq():
c = (
Map()
.add("", [list(z) for z in zip(cq_city, GDP_value)], "重庆",is_map_symbol_show=False,)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="2018年重庆主城九区GDP（亿元）"),
visualmap_opts=opts.VisualMapOpts(max_=1600,split_number=8, is_piecewise=True,) #split_number 表示图例所分的段数
)
)
return c
if __name__ == '__main__':
cq = map_cq()
cq.render(path="C:/test_map_1.html")
世界地图
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
country = ["China", "Canada", "Brazil", "Russia", "United States", "Africa", "Germany"]
value = [300, 100, 2000, 800, 10000, 400, 5000]
def map_world():
c = (
Map()
.add("", [list(z) for z in zip(country, value)], "world",is_map_symbol_show=False,)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="世界地图"),
visualmap_opts=opts.VisualMapOpts(max_=2000,)
)
)
return c
if __name__ == '__main__':
result = map_world()
result.render(path="C:/test_map_1.html")
