python pyecharts地理数据可视化绘制地理图表开发者社区

python pyecharts地理数据可视化绘制地理图表

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

import pyecharts
print(pyecharts.__version__)         # 查看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

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/'
# 用pandas读取csv文件里的数据
df = pd.read_csv("Starbucks.csv")['Country']
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   有交互性
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")

from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType, SymbolType, CurrentConfig
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"
        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="动态轨迹图"))
    .render("geo_lines_background.html")
)

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")
)

import pandas as pd     # 导入数据分析模块
from pyecharts.charts import Geo    # 导入地理信息处理模块
from pyecharts import options as opts   # 配置
from pyecharts.globals import GeoType, CurrentConfig, ThemeType
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
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)
g = Geo(init_opts=opts.InitOpts(theme=ThemeType.PURPLE_PASSION, width="1000px", height="600px"))
g.add_schema(maptype="北京")
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])
# 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))

python pyecharts地理数据可视化 绘制地理图表

python pyecharts地理数据可视化 绘制地理图表

文章目录

一、Pyecharts简介和安装

1. 简介

2. 安装

二、绘制地理图表

1. 世界地图—数据可视化

2. 国家地图—数据可视化

涟漪散点图

动态轨迹图

3. 省市地图—数据可视化

热力图

批量添加地址、经纬度数据，地理数据可视化在地图上

python pyecharts地理数据可视化绘制地理图表