一文带你掌握Pyecharts地理数据可视化的方法

pip install pyecharts -i http://pypi.douban.com/simple --trusted-host pypi.douban.com

import pyecharts
print(pyecharts.__version__)     # 查看当前pyecharts版本

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple echarts-countries-pypkg  		# 全球国家地图
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple echarts-china-provinces-pypkg  # 中国省级地图
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple echarts-china-cities-pypkg   # 中国市级地图
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple echarts-china-counties-pypkg  # 中国县区级地图

# -*- coding: UTF-8 -*-
"""
@File  ：demo1.py
@Author ：叶庭云
@CSDN  ：https://yetingyun.blog.csdn.net/
"""
import pandas as pd
from pyecharts.charts import Map
from pyecharts import options as opts
from pyecharts.globals import ThemeType, CurrentConfig

CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'

# pandas读取csv文件里的数据
df = pd.read_csv("Starbucks.csv")['Country']
# 统计各个地区星巴克门店数量
data = df.value_counts()
datas = [(i, int(j)) for i, j in zip(data.index, data.values)]


# 实例化一个Map对象
map_ = Map(init_opts=opts.InitOpts(theme=ThemeType.PURPLE_PASSION))
# 世界地图
map_.add("门店数量", data_pair=datas, maptype="world")
map_.set_series_opts(label_opts=opts.LabelOpts(is_show=False))  # 不显示label
map_.set_global_opts(
   title_opts=opts.TitleOpts(title="星巴克门店数量在全球分布", pos_left='40%', pos_top='10'),  # 调整title位置
   legend_opts=opts.LegendOpts(is_show=False),
   visualmap_opts=opts.VisualMapOpts(max_=13608, min_=1, is_piecewise=True,
   pieces=[{"max": 9, "min": 1, "label": "1-9", "color": "#00FFFF"},    # 分段 添加图例注释和颜色
     {"max": 99, "min": 10, "label": "10-99", "color": "#A52A2A"},
     {"max": 499, "min": 100, "label": "100-499", "color": "#0000FF	"},
     {"max": 999, "min": 500, "label": "500-999", "color": "#FF00FF"},
     {"max": 2000, "min": 1000, "label": "1000-2000", "color": "#228B22"},
     {"max": 3000, "min": 2000, "label": "2000-3000", "color": "#FF0000"},
     {"max": 20000, "min": 10000, "label": ">=10000", "color": "#FFD700"}
       ])
   )

# 渲染在网页上
map_.render('星巴克门店在全球的分布.html')

import pandas as pd
from pyecharts.globals import ThemeType, CurrentConfig, GeoType
from pyecharts import options as opts
from pyecharts.charts import Geo

CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
# pandas读取csv文件数据
df = pd.read_csv("china.csv")['City']
data = df.value_counts()

datas = [(i, int(j)) for i, j in zip(data.index, data.values)]
print(datas)

geo = Geo(init_opts=opts.InitOpts(width='1000px', height='600px', theme=ThemeType.DARK))
geo.add_schema(maptype='china', label_opts=opts.LabelOpts(is_show=True))  # 显示label 省名
geo.add('门店数量', data_pair=datas, type_=GeoType.EFFECT_SCATTER, symbol_size=8)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(title_opts=opts.TitleOpts(title='星巴克门店在中国的分布'),
          visualmap_opts=opts.VisualMapOpts(max_=550, is_piecewise=True,
          pieces=[{"max": 50, "min": 0, "label": "0-50", "color": "#708090"},    # 分段 添加图例注释 和颜色
               {"max": 100, "min": 51, "label": "51-100", "color": "#00FFFF"},
               {"max": 200, "min": 101, "label": "101-200", "color": "#00008B"},
               {"max": 300, "min": 201, "label": "201-300", "color": "#8B008B"},
               {"max": 600, "min": 500, "label": "500-600", "color": "#FF0000"},
                 ])
          )

geo.render("星巴克门店在中国的分布.html")

# -*- coding: UTF-8 -*-
"""
@File  ：demo3.py
@Author ：叶庭云
@CSDN  ：https://yetingyun.blog.csdn.net/
"""
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType, SymbolType, CurrentConfig, ThemeType

CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
# 链式调用
c = (
  Geo()
  .add_schema(
    maptype="china",
    itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"),
    label_opts=opts.LabelOpts(is_show=True)
  )
  .add(
    "",
    [("广州", 55), ("北京", 66), ("杭州", 77), ("重庆", 88), ('成都', 100), ('海口', 80)],
    type_=ChartType.EFFECT_SCATTER,
    color="white",
  )
  .add(
    "",
    [("广州", "上海"), ("广州", "北京"), ("广州", "杭州"), ("广州", "重庆"),
     ('成都', '海口'), ('海口', '北京'), ('海口', '重庆'), ('重庆', '上海')
     ],
    type_=ChartType.LINES,
    effect_opts=opts.EffectOpts(
      symbol=SymbolType.ARROW, symbol_size=6, color="blue" # 轨迹线蓝色
    ),
    linestyle_opts=opts.LineStyleOpts(curve=0.2), # 轨迹线弯曲度
  )
  .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
  .set_global_opts(title_opts=opts.TitleOpts(title="动态轨迹图"))
  .render("geo_lines_background.html")
)

# -*- coding: UTF-8 -*-
"""
@File  ：demo4.py
@Author ：叶庭云
@CSDN  ：https://yetingyun.blog.csdn.net/
"""
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.faker import Faker
from pyecharts.globals import GeoType, CurrentConfig

CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'

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

# -*- coding: UTF-8 -*-
"""
@File  ：demo5.py
@Author ：叶庭云
@CSDN  ：https://yetingyun.blog.csdn.net/
"""
import pandas as pd   
from pyecharts.charts import Geo  
from pyecharts import options as opts  
from pyecharts.globals import GeoType, CurrentConfig, ThemeType

CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
# 读取Excel数据 数据来源美团网酒店信息
df = pd.read_excel("hotel.xlsx")

# 获取 地点 经纬度信息
geo_sight_coord = {df.iloc[i]['酒店地址']: [df.iloc[i]['经度'], df.iloc[i]['纬度']] for i in range(len(df))}
data = [(df['酒店地址'][j], f"{int(df['最低价'][j])}元(最低价)") for j in range(len(df))]
# print(data)
# print(geo_sight_coord)

# 实例化Geo对象 导入成都地图
g = Geo(init_opts=opts.InitOpts(theme=ThemeType.PURPLE_PASSION, width="1000px", height="600px"))
g.add_schema(maptype="成都")

for k, v in list(geo_sight_coord.items()):
  # 添加地址、经纬度数据
  g.add_coordinate(k, v[0], v[1])

# 生成涟漪散点图
g.add("", data_pair=data, type_=GeoType.EFFECT_SCATTER, symbol_size=6)
g.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
g.set_global_opts(title_opts=opts.TitleOpts(title="成都-酒店地址分布"))
g.render("酒店地址分布.html")