Pyecharts树状图：地理图表

Pyecharts树状图：地理图表

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前言

本文主要是展示了Pyecharts地理图的地理图表的简单应用和基本案例。

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一、地理图表

1. 基本的国家地图(瑞士)

from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.datasets import register_url

try:
    register_url("https://echarts-maps.github.io/echarts-countries-js/")
except Exception:
    import ssl

    ssl._create_default_https_context = ssl._create_unverified_context
    register_url("https://echarts-maps.github.io/echarts-countries-js/")

geo = (
    Geo()
    .add_schema(maptype="瑞士")
    .set_global_opts(title_opts=opts.TitleOpts(title="瑞士"))
    .render("geo_chart_countries_js.html")
)

值得注意的是：这个图只是代码跑出来的图形的截图，其实代码跑出来的原图，如果你的鼠标放在上面，可以出现瑞士国家的各个州名，还蛮有用的。

2. 在地图上展示动态运动效果

from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType, SymbolType

c = (
    Geo()
    .add_schema(
        maptype="china",
        itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"),
    )
    .add(
        "",
        [("广州", 55), ("北京", 66), ("杭州", 77), ("重庆", 88)],
        type_=ChartType.EFFECT_SCATTER,
        color="white",
    )
    .add(
        "geo",
        [("广州", "上海"), ("广州", "北京"), ("广州", "杭州"), ("广州", "重庆")],
        type_=ChartType.LINES,
        effect_opts=opts.EffectOpts(
            symbol=SymbolType.ARROW, symbol_size=6, color="blue"
        ),
        linestyle_opts=opts.LineStyleOpts(curve=0.2),
    )
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(title_opts=opts.TitleOpts(title="Geo-Lines-background"))
    .render("geo_lines_background.html")
)

注意：上面的图只是截图，其实是在动态展示的，很有意思，如果坐标点多一些，可以展示你去过那里，那种行程轨迹图，很实用。

3. 分段展示图

from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.faker import Faker

c = (
    Geo()
    .add_schema(maptype="china")
    .add("geo", [list(z) for z in zip(Faker.provinces, Faker.values())])
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(is_piecewise=True),
        title_opts=opts.TitleOpts(title="Geo-VisualMap（分段型）"),
    )
    .render("geo_visualmap_piecewise.html")
)

注意： 这种图形可以展示比如电商的产品售卖情况，那个省市卖的多，很直观。

4. 省市图

from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.faker import Faker
from pyecharts.globals import ChartType

c = (
    Geo()
    .add_schema(maptype="广东")
    .add(
        "geo",
        [list(z) for z in zip(Faker.guangdong_city, Faker.values())],
        type_=ChartType.HEATMAP,
    )
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(), title_opts=opts.TitleOpts(title="Geo-广东地图")
    )
    .render("geo_guangdong.html")
)

注意： 和上面那个图类似，但是更加详细，因为可以详细到每个市。

5. 涟漪散点图

from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.faker import Faker
from pyecharts.globals import ChartType

c = (
    Geo()
    .add_schema(maptype="china")
    .add(
        "geo",
        [list(z) for z in zip(Faker.provinces, Faker.values())],
        type_=ChartType.EFFECT_SCATTER,
    )
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(title_opts=opts.TitleOpts(title="Geo-EffectScatter"))
    .render("geo_effectscatter.html")
)

注意：这种图有涟漪的动态效果，就很有意思，可以表示某个事件发起的中心。

二、地图升级版

1. 基本地图

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"))
    .render("map_base.html")
)

2. 广东省

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("map_guangdong.html")
)

3. 分段型

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")
)

4. 世界地图

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.country, Faker.values())], "world")
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="Map-世界地图"),
        visualmap_opts=opts.VisualMapOpts(max_=200),
    )
    .render("map_world.html")
)

5. 人口密度图

import ssl

import pyecharts.options as opts
from pyecharts.charts import Map
from pyecharts.datasets import register_url

"""
Gallery 使用 pyecharts 1.1.0 和 echarts-china-cities-js
参考地址: https://echarts.apache.org/examples/editor.html?c=map-HK
"""
ssl._create_default_https_context = ssl._create_unverified_context
# 与 pyecharts 注册，当画香港地图的时候，用 echarts-china-cities-js
register_url("https://echarts-maps.github.io/echarts-china-cities-js")

WIKI_LINK = (
    "http://zh.wikipedia.org/wiki/"
    "%E9%A6%99%E6%B8%AF%E8%A1%8C%E6%94%BF%E5%8D%80%E5%8A%83#cite_note-12"
)
MAP_DATA = [
    ["中西区", 20057.34],
    ["湾仔", 15477.48],
    ["东区", 31686.1],
    ["南区", 6992.6],
    ["油尖旺", 44045.49],
    ["深水埗", 40689.64],
    ["九龙城", 37659.78],
    ["黄大仙", 45180.97],
    ["观塘", 55204.26],
    ["葵青", 21900.9],
    ["荃湾", 4918.26],
    ["屯门", 5881.84],
    ["元朗", 4178.01],
    ["北区", 2227.92],
    ["大埔", 2180.98],
    ["沙田", 9172.94],
    ["西贡", 3368],
    ["离岛", 806.98],
]


NAME_MAP_DATA = {
    # "key": "value"
    # "name on the hong kong map": "name in the MAP DATA",
    "中西区": "中西区",
    "东区": "东区",
    "离岛区": "离岛",
    "九龙城区": "九龙城",
    "葵青区": "葵青",
    "观塘区": "观塘",
    "北区": "北区",
    "西贡区": "西贡",
    "沙田区": "沙田",
    "深水埗区": "深水埗",
    "南区": "南区",
    "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}
{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")
)

注：这里可以通过添加数据展示你想表达的密度图。

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总结

本文主要是介绍了Pyecharts各种地理图，主要用途已经在注意中提示。